A different kind of experiment at CERN

This article, as written by me, appeared in The Hindu on January 24, 2012.

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At the Large Hadron Collider (LHC) at CERN, near Geneva, Switzerland, experiments are conducted by many scientists who don’t quite know what they will see, but know how to conduct the experiments that will yield answers to their questions. They accelerate beams of particles called protons to smash into each other, and study the fallout.

There are some other scientists at CERN who know approximately what they will see in experiments, but don’t know how to do the experiment itself. These scientists work with beams of antiparticles. According to the Standard Model, the dominant theoretical framework in particle physics, every particle has a corresponding particle with the same mass and opposite charge, called an anti-particle.

In fact, at the little-known AEgIS experiment, physicists will attempt to produce an entire beam composed of not just anti-particles but anti-atoms by mid-2014.

AEgIS is one of six antimatter experiments at CERN that create antiparticles and anti-atoms in the lab and then study their properties using special techniques. The hope, as Dr. Jeffrey Hangst, the spokesperson for the ALPHA experiment, stated in an email, is “to find out the truth: Do matter and antimatter obey the same laws of physics?”

Spectroscopic and gravitational techniques will be used to make these measurements. They will improve upon, “precision measurements of antiprotons and anti-electrons” that “have been carried out in the past without seeing any difference between the particles and their antiparticles at very high sensitivity,” as Dr. Michael Doser, AEgIS spokesperson, told this Correspondent via email.

The ALPHA and ATRAP experiments will achieve this by trapping anti-atoms and studying them, while the ASACUSA and AEgIS will form an atomic beam of anti-atoms. All of them, anyway, will continue testing and upgrading through 2013.

Working principle

Precisely, AEgIS will attempt to measure the interaction between gravity and antimatter by shooting an anti-hydrogen beam horizontally through a vacuum tube and then measuring how it much sags due to the gravitational pull of the Earth to a precision of 1 per cent.

The experiment is not so simple because preparing anti-hydrogen atoms is difficult. As Dr. Doser explained, “The experiments concentrate on anti-hydrogen because that should be the most sensitive system, as it is not much affected by magnetic or electric fields, contrary to charged anti-particles.”

First, antiprotons are derived from the Antiproton Decelerator (AD), a particle storage ring which “manufactures” the antiparticles at a low energy. At another location, a nanoporous plate is bombarded with anti-electrons, resulting in a highly unstable mixture of both electrons and anti-electrons called positronium (Ps).

The Ps is then excited to a specific energy state by exposure to a 205-nanometre laser and then an even higher energy state called a Rydberg level using a 1,670-nanometre laser. Last, the excited Ps traverses a special chamber called a recombination trap, when it mixes with antiprotons that are controlled by precisely tuned magnetic fields. With some probability, an antiproton will “trap” an anti-electron to form an anti-hydrogen atom.

Applications

Before a beam of such anti-hydrogen atoms is generated, however, there are problems to be solved. They involve large electric and magnetic fields to control the speed of and collimate the beams, respectively, and powerful cryogenic systems and ultra-cold vacuums. Thus, Dr. Doser and his colleagues will spend many months making careful changes to the apparatus to ensure these requirements work in tandem by 2014.

While antiparticles were first discovered in 1959, “until recently, it was impossible to measure anything about anti-hydrogen,” Dr. Hangst wrote. Thus, the ALPHA and AEgIS experiments at CERN provide a seminal setting for exploring the world of antimatter.

Anti-particles have been used effectively in many diagnostic devices such as PET scanners. Consequently, improvements in our understanding of them feed immediately into medicine. To name an application: Antiprotons hold out the potential of treating tumors more effectively.

In fact, the feasibility of this application is being investigated by the ACE experiment at CERN.

In the words of Dr. Doser: “Without the motivation of attempting this experiment, the experts in the corresponding fields would most likely never have collaborated and might well never have been pushed to solve the related interdisciplinary problems.”

LHC to re-awaken in 2015 with doubled energy, luminosity

This article, as written by me, appeared in The Hindu on January 10, 2012.

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After a successful three-year run that saw the discovery of a Higgs-boson-like particle in early 2012, the Large Hadron Collider (LHC) at CERN, near Geneva, Switzerland, will shut down for 18 months for maintenance and upgrades.

This is the first of three long shutdowns, scheduled for 2013, 2017, and 2022. Physicists and engineers will use these breaks to ramp up one of the most sophisticated experiments in history even further.

According to Mirko Pojer, Engineer In-charge, LHC-operations, most of these changes were planned in 2011. They will largely concern fixing known glitches on the ATLAS and CMS particle-detectors. The collider will receive upgrades to increase its collision energy and frequency.

Presently, the LHC smashes two beams, each composed of precisely spaced bunches of protons, at 3.5-4 tera-electron-volts (TeV) per beam.

By 2015, the beam energy will be pushed up to 6.5-7 TeV per beam. Moreover, the bunches which were smashed at intervals of 50 nanoseconds will do so at 25 nanoseconds.

After upgrades, “in terms of performance, the LHC will deliver twice the luminosity,” Dr. Pojer noted in an email to this Correspondent, with reference to the integrated luminosity. Precisely, it is the number of collisions that the LHC can deliver per unit area which the detectors can track.

The instantaneous luminosity, which is the luminosity per second, will be increased to 1x10³⁴ per centimetre-squared per second, ten-times greater than before, and well on its way to peaking at 7.73x10³⁴ per centimetre-squared per second by 2022.

As Steve Myers, CERN’s Director for Accelerators and Technology, announced in December 2012, “More intense beams mean more collisions and a better chance of observing rare phenomena.” One such phenomenon is the appearance of a Higgs-boson-like particle.

The CMS experiment, one of the detectors on the LHC-ring, will receive some new pixel sensors, a technology responsible for tracking the paths of colliding particles. To make use of the impending new luminosity-regime, an extra layer of these advanced sensors will be inserted around a smaller beam pipe.

If results from it are successful, CMS will receive the full unit in late-2016.

In the ATLAS experiment, unlike with CMS which was built with greater luminosities in mind, pixel sensors are foreseen to wear out within one year after upgrades. As an intermediate solution, a new layer of sensors called the B-layer will be inserted within the detector for until 2018.

Because of the risk of radiation damage due to more numerous collisions, specific neutron shields will be fit, according to Phil Allport, ATLAS Upgrade Coordinator.

Both ATLAS and CMS will also receive evaporative cooling systems and new superconducting cables to accommodate the higher performance that will be expected of them in 2015. The other experiments, LHCb and ALICE, will also undergo inspections and upgrades to cope with higher luminosity.

An improved failsafe system will be installed and the existing one upgraded to prevent accidents such as the one in 2008.

Then, an electrical failure damaged 29 magnets and leaked six tonnes of liquid helium into the tunnel, precipitating an eight-month shutdown.

Generally, as Martin Gastal, CMS Experimental Area Manager, explained via email, “All sub-systems will take the opportunity of this shutdown to replace failing parts and increase performance when possible.”

All these changes have been optimised to fulfil the LHC’s future agenda. This includes studying the properties of the newly discovered particle, and looking for signs of new theories of physics like supersymmetry and higher dimensions.

(Special thanks to Achintya Rao, CMS Experiment.)

The case of the red-haired kids

This blog post first appeared, as written by me, on The Copernican science blog on December 30, 2012.

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Seriously, shame on me for not noticing the release of a product named Correlate until December 2012. Correlate by Google was released in May last year and is a tool to see how two different search trends have panned out over a period of time. But instead of letting you pick out searches and compare them, Correlate saves a bit of time by letting you choose one trend and then automatically picks out trends similar to the one you’ve your eye on.

For instance, I used the “Draw” option and drew a straight, gently climbing line from September 19, 2004, to July 24, 2011 (both randomly selected). Next, I chose “India” as the source of search queries for this line to be compared with, and hit “Correlate”. Voila! Google threw up 10 search trends that varied over time just as my line had.



Since I’ve picked only India, the space from which the queries originate remains fixed, making this a temporal trend - a time-based one. If I’d fixed the time - like a particular day, something short enough to not produce strong variations - then it’d have been a spatial trend, something plottable on a map.

Now, there were a lot of numbers on the results page. The 10 trends displayed in fact were ranked according to a particular number “r” displayed against them. The highest ranked result, “free english songs”, had r = 0.7962. The lowest ranked result, “to 3gp converter”, had r = 0.7653.



And as I moused over the chart itself, I saw two numbers, one each against the two trends being tracked. For example, on March 1, 2009, the “Drawn Series” line had a number +0.701, and the “free english songs” line had a number -0.008, against it.



What do these numbers mean?

This is what I want to really discuss because they have strong implications on how lay people interpret data that appears in the context of some scientific text, like a published paper. Each of these numbers is associated with a particular behaviour of some trend at a specific point. So, instead of looking at it as numbers and shapes on a piece of paper, look at it for what it represents and you’ll see so many possibilities coming to life.

The numbers against the trends, +0.701 for “Drawn Series” (my line) and -0.008 for “free english songs” in March ‘09, are the deviations. The deviation is a lovely metric because it sort of presents the local picture in comparison to the global picture, and this perspective is made possible by the simple technique used to evaluate it.

Consider my line. Each of the points on the line has a certain value. Use this information to find their average value. Now, the deviation is how much a point’s value is away from the average value.

It’s like if 11 red-haired kids were made to stand in a line ordered according to the redness of their hair. If the “average” colour around was a perfect orange, then the kid with the “reddest” hair and the kid with the palest-red hair will be the most deviating. Kids with some semblance of orange in their hair-colour will be progressively less deviating until they’re past the perfect “orangeness”, and the kid with perfectly-orange hair will completely non-deviating.

So, on August 23, 2009, “Drawn Series” was higher than its average value by 0.701 and “free english songs” was lower than its average value by 0.008. Now, if you’re wondering what the units are to measure these numbers: Deviations are dimensionless fractions - which means they’re just numbers whose highness or lowness are indications of intensity.

And what’re they fractions of? The value being measured along the trend being tracked.

Now, enter standard deviation. Remember how you found the average value of a point on my line? Well, the standard deviation is the average value among all deviations. It’s like saying the children fitting a particular demographic are, for instance, 25 per cent smarter on average than other normal kids: the standard deviation is 25 per cent and the individual deviations are similar percentages of the “smartness” being measured.

So, right now, if you took the bigger picture, you’d see the chart, the standard deviation (the individual deviations if you chose to mouse-over), the average, and that number “r”. The average will indicate the characteristic behaviour of the trend - let’s call it “orange” - the standard deviation will indicate how far off on average a point’s behaviour will be deviating in comparison to “orange” - say, “barely orange”, “bloody”, etc. - and the individual deviations will show how “orange” each point really is.

At this point I must mention that I conveniently oversimplified the example of the red-haired kids to avoid a specific problem. This problem has been quite single-handedly responsible for the news-media wrongly interpreting results from the LHC/CERN on the Higgs search.

In the case of the kids, we assumed that, going down the line, each kid’s hair would get progressively darker. What I left out was how much darker the hair would get with each step.

Let’s look at two different scenarios.

Scenario 1: The hair gets darker by a fixed amount each step.

Let’s say the first kid’s got hair that’s 1 units of orange, the fifth kid’s got 5 units, and the 11th kid’s got 11 units. This way, the average “amount of orange” in the lineup is going to be 6 units. The deviation on either side of kid #6 is going to increase/decrease in steps of 1. In fact, from the first to the last, it’s going to be 5, 4, 3, 2, 1, 0, 1, 2, 3, 4, and 5. Straight down and then straight up.



Scenario 2: The hair gets darker slowly and then rapidly, also from 1 to 11 units.

In this case, the average is not going to be 6 units. Let’s say the “orangeness” this time is 1, 1.5, 2, 2.5, 3, 3.5, 4, 5.5, 7.5, 9.75, and 11 per kid, which brings the average to ~4.6591 units. In turn, the deviations are 3.6591, 3.1591, 2.6591, 2, 1591, 1.6591, 1.1591, 0.6591, 0.8409, 2.8409, 5.0909, and 6.3409. In other words, slowly down and then quickly more up.



In the second scenario, we saw how the average got shifted to the left. This is because there were more less-orange kids than more-orange ones. What’s more important is that it didn’t matter if the kids on the right had more more-orange hair than before. That they were fewer in number shifted the weight of the argument away from them!

In much the same way, looking for the Higgs boson from a chart that shows different peaks (number of signature decay events) at different points (energy levels), with taller but fewer peaks to one side and shorter but many more peaks to the other, can be confusing. While more decays could’ve occurred at discrete energy levels, the Higgs boson is more likely (note: not definitely) to be found within the energy-level where decays occur more frequently (in the chart below, decays are seen to occur more frequently at 118-126 GeV/c2 than at 128-138 GeV/c2 or 110-117 GeV/c2).

[caption id="attachment_24766" align="aligncenter" width="750"] Idea from Prof. Matt Strassler's blog[/caption]

If there’s a tall peak where a Higgs isn’t likely to occur, then that’s an outlier, a weirdo who doesn’t fit into the data. It’s probably called an outlier because its deviation from the average could be well outside the permissible deviation from the average.

This also means it’s necessary to pick the average from the right area to identify the right outliers. In the case of the Higgs, if its associated energy-level (mass) is calculated as being an average of all the energy levels at which a decay occurs, then freak occurrences and statistical noise are going to interfere with the calculation. But knowing that some masses of the particle have been eliminated, we can constrain the data to between two energy levels, and then go after the average.

So, when an uninformed journalist looks at the data, the taller peaks can catch the eye, even run away with the ball. But look out for the more closely occurring bunches - that’s where all the action is!

If you notice, you’ll also see that there are no events at some energy levels. This is where you should remember that uncertainty cuts both ways. When you’re looking at a peak and thinking “This can’t be it; there’s some frequency of decays to the bottom, too”, you’re acknowledging some uncertainty in your perspective. Why not acknowledge some uncertainty when you’re noticing absent data, too?

While there’s a peak at 126 GeV/c2, the Higgs weighs between 124-125 GeV/c2. We know this now, so when we look at the chart, we know we were right in having been uncertain about the mass of the Higgs being 126 GeV/c2. Similarly, why not say “There’s no decays at 113 GeV/c2, but let me be uncertain and say there could’ve been a decay there that’s escaped this measurement”?

Maybe this idea’s better illustrated with this chart.



There’s a noticeable gap between 123 and 125 GeV/c2. Just looking at this chart and you’re going to think that with peaks on either side of this valley, the Higgs isn’t going to be here… but that’s just where it is! So, make sure you address uncertainty when you’re determining presences as well as absences.

So, now, we’re finally ready to address “r”, the Pearson covariance coefficient. It’s got a formula, and I think you should see it. It’s pretty neat.



(TeX: r\quad =\quad \frac { { \Sigma }_{ i=1 }^{ n }({ X }_{ i }\quad -\quad \overset { \_ }{ X } )({ Y }_{ i }\quad -\quad \overset { \_ }{ Y } ) }{ \sqrt { { \Sigma }_{ i=1 }^{ n }{ ({ X }_{ i }\quad -\quad \overset { \_ }{ X } ) }^{ 2 } } \sqrt { { \Sigma }_{ i=1 }^{ n }{ (Y_{ i }\quad -\quad \overset { \_ }{ Y } ) }^{ 2 } } })

The equation says "Let's see what your Pearson covariance, "r", is by seeing how much all of your variations are deviant keeping in mind both your standard deviations."

The numerator is what’s called the covariance, and the denominator is basically the product of the standard deviations. X-bar, which is X with a bar atop, is the average value of X - my line - and the same goes for Y-bar, corresponding to Y - “mobile games”. Individual points on the lines are denoted with the subscript “i”, so the points would be X1, X2, X3, ..., and Y1, Y2, Y3, …”n” in the formula is the size of the sample - the number of days over which we’re comparing the two trends.

The Pearson covariance coefficient is not called the Pearson deviation coefficient, etc., because it normalises the graph’s covariance. Simply put, covariance is a measure of how much the two trends vary together. It can have a minimum value of 0, which would mean one trend’s variation has nothing to do with the other’s, and a maximum value of 1, which would mean one trend’s variation is inescapably tied with the variation of the other’s. Similarly, if the covariance is positive, it means that if one trend climbs, the other would climb, too. If the covariance is negative, then one trend’s climbing would mean the other’s descending (In the chart below, between Oct ’09 and Jan ’10, there’s a dip: even during the dive-down, the blue line is on an increasing note – here, the local covariance will be negative).



Apart from being a conveniently defined number, covariance also records a trend’s linearity. In statistics, linearity is a notion that stands by its name: like a straight line, the rise or fall of a trend is uniform. If you divided up the line into thousands of tiny bits and called each one on the right the “cause” and the one on the left the “effect”, then you’d see that linearity means each effect for each cause is either an increase or a decrease by the same amount.

Just like that, if the covariance is a lower positive number, it means one trend’s growth is also the other trend’s growth, and in equal measure. If the covariance is a larger positive number, you’d have something like the butterfly effect: one trend moves up by an inch, the other shoots up by a mile. This you’ll notice is a break from linearity. So if you plotted the covariance at each point in a chart as a chart by itself, one look will tell you how the relationship between the two trends varies over time (or space).

How big is your language?

This blog post first appeared, as written by me, on The Copernican science blog on December 20, 2012.

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It all starts with Zipf’s law. Ever heard of it? It’s a devious little thing, especially when you apply it to languages.

Zipf’s law states that the chances of finding a word of a language in all the texts written in that language are inversely proportional to the word’s rank in the frequency table. In other words, this means that the chances of finding the most frequent word is twice as much as are chances of finding the second most frequent word, thrice as much as are chances of finding the third most frequent word, and so on.

Unfortunately (only because I like how “Zipf” sounds), the law holds only until about the 1,000th most common word; after this point, a logarithmic plot drawn between frequency and chance stops being linear and starts to curve.

The importance of this break is that if Zipf’s law fails to hold for a large corpus of words, then the language, at some point, must be making some sort of distinction between common and exotic words, and its need for new words must either be increasing or decreasing. This is because, if the need remained constant, then the distinction would be impossible to define except empirically and never conclusively - going against the behaviour of Zipf's law.

Consequently, the chances of finding the 10,000th word won’t be 10,000 times less than the chances of finding the most frequently used word but a value much lesser or much greater.

A language's diktat

Analysing each possibility, i.e., if the chances of finding the 10,000th-most-used word are NOT 10,000 times less than the chances of finding the most-used word but…

• Greater (i.e., The Asymptote): The language must have a long tail, also called an asymptote. Think about it. If the rarer words are all used almost as frequently as each other, then they can all be bunched up into one set, and when plotted, they’d form a straight line almost parallel to the x-axis (chance), a sort of tail attached to the rest of the plot.


• Lesser (i.e., The Cliff): After expanding to include a sufficiently large vocabulary, the language could be thought to “drop off” the edge of a statistical cliff. That is, at some point, there will be words that exist and mean something, but will almost never be used because syntactically simpler synonyms exist. In other words, in comparison to the usage of the first 1,000 words of the language, the (hypothetical) 10,000th word would be used negligibly.

The former possibility is more likely – that the chances of finding the 10,000th-most-used word would not be as low as 10,000-times less than the chances of encountering the most-used word.

As a language expands to include more words, it is likely that it issues a diktat to those words: “either be meaningful or go away”. And as the length of the language’s tail grows, as more exotic and infrequently used words accumulate, the need for those words drops off faster over time that are farther from Zipf’s domain.

Another way to quantify this phenomenon is through semantics (and this is a far shorter route of argument): As the underlying correlations between different words become more networked – for instance, attain greater betweenness – the need for new words is reduced.

Of course, the counterargument here is that there is no evidence to establish if people are likelier to use existing syntax to encapsulate new meaning than they are to use new syntax. This apparent barrier can be resolved by what is called the principle of least effort.

Proof and consequence

While all of this has been theoretically laid out, there had to have been many proofs over the years because the object under observation is a language – a veritable projection of the right to expression as well as a living, changing entity. And in the pursuit of some proof, on December 12, I spotted a paper on arXiv that claims to have used an “unprecedented” corpus (Nature scientific report here).

Titled “Languages cool as they expand: Allometric scaling and the decreasing need for new words”, it was hard to miss in the midst of papers, for example, being called “Trivial symmetries in a 3D topological torsion model of gravity”.

The abstract of the paper, by Alexander Petersen from the IMT Lucca Institute for Advanced Studies, et al, has this line: “Using corpora of unprecedented size, we test the allometric scaling of growing languages to demonstrate a decreasing marginal need for new words…” This is what caught my eye.

While it’s clear that Petersen’s results have been established only empirically, that their corpus includes all the words in books written with the English language between 1800 and 2008 indicates that the set of observables is almost as large as it can get.

Second: When speaking of corpuses, or corpora, the study has also factored in Heaps’ law (apart from Zipf’s law), and found that there are some words that obey neither Zipf nor Heaps but are distinct enough to constitute a class of their own. This is also why I underlined the word common earlier in this post. (How Petersen, et al, came to identify this is interesting: They observed deviations in the lexicon of individuals diagnosed with schizophrenia!)

The Heaps’ law, also called the Heaps-Herdan law, states that the chances of discovering a new word in one large instance-text, like one article or one book, become lesser as the size of the instance-text grows. It’s like a combination of the sunk-cost fallacy and Zipf’s law.

It’s a really simple law, too, and makes a lot of sense even intuitively, but the ease with which it's been captured statistically is what makes the Heaps-Herdan law so wondrous.

[caption id="attachment_24742" align="aligncenter" width="472"] The sub-linear Heaps' law plot: Instance-text size on x-axis; Number of individual words on y-axis.[/caption]

Falling empires

And Petersen and his team establish in the paper that, extending the consequences of Zipf’s and Heaps’ laws to massive corpora, the larger a language is in terms of the number of individual words it contains, the slower it will grow, the lesser cultural evolution it will engender. In the words of the authors: “… We find a scaling relation that indicates a decreasing ‘marginal need’ for new words which are the manifestations of cultural evolution and the seeds for language growth.”

However, for the class of “distinguished” words, there seems to exist a power law – one that results in a non-linear graph unlike Zipf’s and Heaps’ laws. This means that as new exotic words are added to a language, the need for them, as such, is unpredictable and changes over time for as long as they are away from the Zipf’s law’s domain.

All in all, languages eventually seem an uncanny mirror of empires: The larger they get, the slower they grow, the more intricate the exchanges become within it, the fewer reasons there are to change, until some fluctuations are injected from the world outside (in the form of new words).

In fact, the mirroring is not so uncanny considering both empires and languages are strongly associated with cultural evolution. Ironically enough, it is the possibility of cultural evolution that very meaningfully justifies the creation and employment of languages, which means that at some point, languages only become bloated in some way to stop germination of new ideas and instead start to suffocate such initiatives.

Does this mean the extent to which a culture centered on a language has developed and will develop depends on how much the language itself has developed and will develop? Not conclusively – as there are a host of other factors left to be integrated – but it seems a strong correlation exists between the two.

So… how big is your language?

One of the hottest planets cold enough for ice

This article, as written by me, appeared in The Hindu on December 6, 2012.

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Mercury, the innermost planet in the Solar System, is like a small rock orbiting the Sun, continuously assaulted by the star's heat and radiation. It would have to be the last place to look for water at.

However, observations of NASA's MESSENGER spacecraft indicate that Mercury seems to harbour enough water-ice to fill 20 billion Olympic skating rinks.

On November 29, during a televised press conference, NASA announced that data recorded since March 2011 by MESSENGER's on-board instruments hinted that large quantities of water ice were stowed in the shadows of craters around the planet's North Pole.

Unlike Earth, Mercury’s rotation is not tilted about an axis. This means areas around the planet's poles that are not sufficiently tilted toward the sun will remain cold for long periods of time.

This characteristic allows the insides of polar craters to maintain low temperatures for millions of years, and capable of storing water-ice. But then, where is the water coming from?

Bright spots were identified by MESSENGER's infrared laser fired from orbit into nine craters around the North Pole. The spots lined up perfectly with a thermal model of ultra-cold spots on the planet that would never be warmer than -170 degrees centigrade.

These icy spots are surrounded by darker terrain that receives a bit more sunlight and heat. Measurements by the neutron spectrometer aboard MESSENGER suggest that this darker area is a layer of material about 10 cm thick that lies on top of more ice, insulating it.

Dr. David Paige, a planetary scientist at the University of California, Los Angeles, and lead author of one of three papers that indicate the craters might contain ice, said, "The darker material around the bright spots may be made up of complex hydrocarbons expelled from comet or asteroid impacts." Such compounds must not be mistaken as signs of life since they can be produced by simple chemical reactions as well.

The water-ice could also have been derived from crashing comets, the study by Paige and his team concludes.

Finding water on the system’s hottest planet changes the way scientists perceive the Solar System’s formation.

Indeed, in the mid-1990s, strong radar signals were fired from the US Arecibo radar dish in Puerto Rico, aimed at Mercury’s poles. Bright radar reflections were seen from crater-like regions, which was indicative of water-ice.

“However, other substances might also reflect radar in a similar manner, like sulfur or cold silicate materials,” says David J. Lawrence, a physicist from the Johns Hopkins University Applied Physics Laboratory and lead author of the neutron spectrometer study.

Lawrence and his team observed particles called neutrons bouncing and ricocheting off the planet via a spectrometer aboard MESSENGER. As high-energy cosmic rays from outer space bombarded into atoms on the planet, debris of particles, including neutrons, was the result.

However, hydrogen atoms in the path of neutrons can halt the speeding particles almost completely as both weigh about the same. Since water molecules contain two hydrogen atoms each, areas that could contain water-ice will show a suppressed count of neutrons in the space above them.

Because scientists have been living with the idea of Mercury containing water for the last couple decades, the find by MESSENGER is not likely to be revolutionary. However, it bolsters an exciting idea.

As Lawrence says, “I think this discovery reinforces the reality that water is able to find its way to many places in the Solar System, and this fact should be kept in mind when studying the system and its history.”

This is poetry. This is dance.

Drop everything, cut off all sound/noise, and watch this.

[vimeo http://www.vimeo.com/53914149 w=398&h=224]

If you've gotten as far as this line, here's some extra info: this video was shot with these cameras for the sake of this conversation.

To understand the biology behind such almost-improbable fluidity, this is a good place to start.

Window for an advanced theory of particles closes further

A version of this article, as written by me, appeared in The Hindu on November 22, 2012.

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On November 12, at the first day of the Hadron Collider Physics Symposium at Kyoto, Japan, researchers presented a handful of results that constrained the number of hiding places for a new theory of physics long believed to be promising.

Members of the team from the LHCb detector on the Large Hadron Collider (LHC) experiment located on the border of France and Switzerland provided evidence of a very rare particle-decay. The rate of the decay process was in fair agreement with an older theory of particles' properties, called the Standard Model (SM), and deviated from the new theory, called Supersymmetry.

"Theorists have calculated that, in the Standard Model, this decay should occur about 3 times in every billion total decays of the particle," announced Pierluigi Campana, LHCb spokesperson. "This first measurement gives a value of around 3.2 per billion, which is in very good agreement with the prediction."

The result was presented at the 3.5-sigma confidence level, which corresponds to an error rate of 1-in-2,000. While not strong enough to claim discovery, it is valid as evidence.



The particle, called a B_s⁰ meson, decayed from a bottom antiquark and strange quark pair into two muons. According to the SM, this is a complex and indirect decay process: the quarks exchange a W boson particle, turn into a top-antitop quark pair, which then decays into a Z boson or a Higgs boson. The boson then decays to two muons.

This indirect decay is called a quantum loop, and advanced theories like Supersymmetry predict new, short-lived particles to appear in such loops. The LHCb, which detected the decays, reported no such new particles.

[caption id="attachment_24512" align="aligncenter" width="584"] The solid blue line shows post-decay muons from all events, and the red dotted line shows the muon-decay event from the B(s)0 meson. Because of a strong agreement with the SM, SUSY may as well abandon this bastion.[/caption]

At the same time, in June 2011, the LHCb had announced that it had spotted hints of supersymmetric particles at 3.9-sigma. Thus, scientists will continue to conduct tests until they can stack 3.5 million-to-1 odds for or against Supersymmetry to close the case.

As Prof. Chris Parkes, spokesperson for the UK participation in the LHCb experiment, told BBC News: "Supersymmetry may not be dead but these latest results have certainly put it into hospital."

The symposium, which concluded on November 16, also saw the release of the first batch of data generated in search of the Higgs boson since the initial announcement on July 4 this year.

The LHC can't observe the Higgs boson directly because it quickly decays into lighter particles. So, physicists count up the lighter particles and try to see if some of those could have come from a momentarily existent Higgs.

These are still early days, but the data seems consistent with the predicted properties of the elusive particle, giving further strength to the validity of the SM.

Dr. Rahul Sinha, a physicist at the Institute of Mathematical Sciences, Chennai, said, “So far there is nothing in the Higgs data that indicates that it is not the Higgs of Standard Model, but a conclusive statement cannot be made as yet.”

The scientific community, however, is disappointed as there are fewer channels for new physics to occur. While the SM is fairly consistent with experimental findings, it is still unable to explain some fundamental problems.

One, called the hierarchy problem, asks why some particles are much heavier than others. Supersymmetry is theoretically equipped to provide the answer, but experimental findings are only thinning down its chances.

Commenting on the results, Dr. G. Rajasekaran, scientific adviser to the India-based Neutrino Observatory being built at Theni, asked for patience. “Supersymmetry implies the existence of a whole new world of particles equaling our known world. Remember, we took a hundred years to discover the known particles starting with the electron.”

With each such tightening of the leash, physicists return to the drawing board and consider new possibilities from scratch. At the same time, they also hope that the initial results are wrong. "We now plan to continue analysing data to improve the accuracy of this measurement and others which could show effects of new physics," said Campana.

So, while the area where a chink might be found in the SM armour is getting smaller, there is hope that there is a chink somewhere nonetheless.

The travails of science communication

There's an interesting phenomenon in the world of science communication, at least so far as I've noticed. Every once in a while, there comes along a concept that is gaining in research traction worldwide but is quite tricky to explain in simple terms to the layman.

Earlier this year, one such concept was the Higgs mechanism. Between December 13, 2011, when the first spotting of the Higgs boson was announced, and July 4, 2012, when the spotting was confirmed as being the piquingly-named "God particle", the use of the phrase "cosmic molasses" was prevalent enough to prompt an annoyed (and struggling-to-make-sense) Daniel Sarewitz to hit back on Nature. While the article had a lot to say, and a lot more waiting there to just to be rebutted, it did include this remark:

If you find the idea of a cosmic molasses that imparts mass to invisible elementary particles more convincing than a sea of milk that imparts immortality to the Hindu gods, then surely it’s not because one image is inherently more credible and more ‘scientific’ than the other. Both images sound a bit ridiculous. But people raised to believe that physicists are more reliable than Hindu priests will prefer molasses to milk. For those who cannot follow the mathematics, belief in the Higgs is an act of faith, not of rationality.

Sarewitz is not wrong in remarking of the problem as such, but in attempting to use it to define the case of religion's existence. Anyway: In bridging the gap between advanced physics, which is well-poised to "unlock the future", and public understanding, which is well-poised to fund the future, there is good journalism. But does it have to come with the twisting and turning of complex theory, maintaining only a tenuous relationship between what the metaphor implies and what reality is?

The notion of a "cosmic molasses" isn't that bad; it does get close to the original idea of a pervading field of energy whose forces are encapsulated under certain circumstances to impart mass to trespassing particles in the form of the Higgs boson. Even this is a "corruption", I'm sure. But what I choose to include or leave out makes all the difference.

The significance of experimental physicists having probably found the Higgs boson is best conveyed in terms of what it means to the layman in terms of his daily life and such activities more so than trying continuously to get him interested in the Large Hadron Collider. Common, underlying curiosities will suffice to to get one thinking about the nature of God, or the origins of the universe, and where the mass came from that bounced off Sir Isaac's head. Shrouding it in a cloud of unrelated concepts is only bound to make the physicists themselves sound defensive, as if they're struggling to explain something that only they will ever understand.

In the process, if the communicator has left out things such as electroweak symmetry-breaking and Nambu-Goldstone bosons, it's OK. They're not part of what makes the find significant for the layman. If, however, you feel that you need to explain everything, then change the question that your post is answering, or merge it with your original idea, etc. Do not indulge in the subject, and make sure to explain your concepts as a proper fiction-story: Your knowledge of the plot shouldn't interfere with the reader's process of discovery.

Another complex theory that's doing the rounds these days is that of quantum entanglement. Those publications that cover news in the field regularly, such as R&D mag, don't even do as much justice as did SciAm to the Higgs mechanism (through the "cosmic molasses" metaphor). Consider, for instance, this explanation from a story that appeared on November 16.

Electrons have a property called "spin": Just as a bar magnet can point up or down, so too can the spin of an electron. When electrons become entangled, their spins mirror each other.

The causal link has been omitted! If the story has set out to explain an application of quantum entanglement, which I think it has, then it has done a fairly good job. But what about entanglement-the-concept itself? Yes, it does stand to lose a lot because many communicators seem to be divesting of its intricacies and spending more time explaining why it's increasing in relevance in modern electronics and computation. If relevance is to mean anything, then debate has to exist - even if it seems antithetical to the deployment of the technology as in the case of nuclear power.

Without understanding what entanglement means, there can be no informed recognition of its wonderful capabilities, there can be no public dialog as to its optimum use to further public interests. When when scientific research stops contributing to the latter, it will definitely face collapse, and that's the function, rather the purpose, that sensible science communication serves.

After less than 100 days, Curiosity renews interest in Martian methane

A version of this story, as written by me, appeared in The Hindu on November 15, 2012.

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In the last week of October, the Mars rover Curiosity announced that there was no methane on Mars. The rover’s conclusion is only a preliminary verdict, although it is already controversial because of the implications of the gas’s discovery (or non-discovery).

The presence of methane is one of the most important prerequisites for life to have existed in the planet’s past. The interest in the notion was increased when Curiosity found signs that water may have flowed in the past through Gale Crater, the immediate neighbourhood of its landing spot, after finding sedimentary settlements.

The rover’s Tunable Laser Spectrometer (TLS), which analysed a small sample of Martian air to come to the conclusion, had actually detected a few parts per billion of methane. However, recognising that the reading was too low to be significant, it sounded a “No”.

In an email to this Correspondent, Adam Stevens, a member of the science team of the NOMAD instrument on the ExoMars Trace Gas Orbiter due to be launched in January 2016, stressed: “No orbital or ground-based detections have ever suggested atmospheric levels anywhere above 10-30 parts per billion, so we are not expecting to see anything above this level.”

At the same time, he also noted that the 10-30 parts per billion (ppb) is not a global average. The previous detections of methane found the gas localised in the Tharsis volcanic plateau, the Syrtis Major volcano, and the polar caps, locations the rover is not going to visit. What continues to keep the scientists hopeful is that methane on Mars seems to get replenished by some geochemical or biological source.

The TLS will also have an important role to play in the future. At some point, the instrument will go into a higher sensitivity-operating mode and make measurements of higher significance by reducing errors.

It is pertinent to note that scientists still have an incomplete understanding of Mars’s natural history. As Mr. Stevens noted, “While not finding methane would not rule out extinct or extant life, finding it would not necessarily imply that life exists or existed.”

Apart from methane, there are very few “bulk” signatures of life that the Martian geography and atmosphere have to offer. Scientists are looking for small fossils, complex carbon compounds and other hydrocarbon gases, amino acids, and specific minerals that could be suggestive of biological processes.

While Curiosity has some fixed long-term objectives, they are constantly adapted according to what the rover finds. Commenting on its plans, Mr. Stevens said, “Curiosity will definitely move up Aeolis Mons, the mountain in the middle of Gale Crater, taking samples and analyses as it goes.”

Curiosity is not the last chance to look more closely for methane in the near future, however.

On the other side of the Atlantic, development of the ExoMars Trace Gas Orbiter (TGO), with which Mr. Stevens is working, is underway. A collaboration between the European Space Agency and the Russian Federal Space Agency, the TGO is planned to deploy a stationary Lander that will map the sources of methane and other gases on Mars.

Its observations will contribute to selecting a landing site for the ExoMars rover due to be launched in 2018.

Even as Curiosity completed 100 days on Mars on November 14, it still has 590 days to go. However, it has also already attracted attention from diverse fields of study. There is no doubt that from the short trip from the rim of Gale Crater, where it is now, to the peak of Aeolis Mons, Curiosity will definitely change our understanding of the enigmatic red planet.

A latent monadology: An extended revisitation of the mind-body problem

[caption id="attachment_24464" align="alignleft" width="266"] Image by Genis Carreras[/caption]

In an earlier post, I'd spoken about a certain class of mind-body interfacing problems (the way I'd identified it): evolution being a continuous process, can psychological changes effected in a certain class of people identified solely by cultural practices "spill over" as modifications of evolutionary goals? There were some interesting comments on the post, too. You may read them here.

However, the doubt was only the latest in a series of others like it. My interest in the subject was born with a paper I'd read quite a while ago that discussed two methods either of which humankind could possibly use to recreate the human brain as a machine. The first method, rather complexly laid down, was nothing but the ubiquitous recourse called reverse-engineering. Study the brain, understand what it's made of, reverse all known cause-effect relationships associated with the organ, then attempt to recreate the cause using the effect in a laboratory with suitable materials to replace the original constituents.

The second method was much more interesting (this bias could explain the choice of words in the previous paragraph). Essentially, it described the construction of a machine that could perform all the known functions of the brain. Then, this machine would have to be subjected to a learning process, through which it would acquire new skills while it retained and used the skills it's already been endowed with. After some time, if the learnt skills, so chosen to reflect real human skills, are deployed by the machine to recreate human endeavor, then the machine is the brain.

Why I like this method better than the reverse-engineered brain is because it takes into account the ability to learn as a function of the brain, resulting in a more dynamic product. The notion of the brain as a static body is definitively meaningless as, axiomatically, conceiving of it as a really powerful processor stops short of such Leibnizian monads as awareness and imagination. While these two "entities" evade comprehension, subtracting the ability to, yes, somehow recreate them doesn't yield a convincing brain as it is. And this is where I believe the mind-body problem finds solution. For the sake of argument, let's discuss the issue differentially.

[caption id="attachment_24463" align="aligncenter" width="320"] Spherical waves coming from a point source. The solution of the initial-value problem for the wave equation in three space dimensions can be obtained from the solution for a spherical wave through the use of partial differential equations. (Image by Oleg Alexandrov on Wikimedia, including MATLAB source code.)[/caption]

Hold as constant: Awareness
Hold as variable: Imagination

The brain is aware, has been aware, must be aware in the future. It is aware of the body, of the universe, of itself. In order to be able to imagine, therefore, it must concurrently trigger, receive, and manipulate different memorial stimuli to construct different situations, analyze them, and arrive at a conclusion about different operational possibilities in each situation. Note: this process is predicated on the inability of the brain to birth entirely original ideas, an extension of the fact that a sleeping person cannot be dreaming of something he has not interacted with in some way.

Hold as constant: Imagination
Hold as variable: Awareness

At this point, I need only prove that the brain can arrive at an awareness of itself, the body, and the universe, through a series of imaginative constructs, in order to hold my axiom as such. So, I'm going to assume that awareness came before imagination did. This leaves open the possibility that with some awareness, the human mind is able to come up with new ways to parse future stimuli, thereby facilitating understanding and increasing the sort of awareness of everything that better suits one's needs and environment.

Now, let's talk about the process of learning and how it sits with awareness, imagination, and consciousness, too. This is where I'd like to introduce the metaphor called Leibniz's gap. In 1714, Gottfried Leibniz's 'Principes de la Nature et de la Grace fondés en raison' was published in the Netherlands. In the work, which would form the basis of modern analytic philosophy, the philosopher-mathematician argues that there can be no physical processes that can be recorded or tracked in any way that would point to corresponding changes in psychological processes.

... supposing that there were a mechanism so constructed as to think, feel and have perception, we might enter it as into a mill. And this granted, we should only find on visiting it, pieces which push one against another, but never anything by which to explain a perception. This must be sought, therefore, in the simple substance, and not in the composite or in the machine.

If any technique was found that could span the distance between these two concepts - the physical and the psychological - then Leibniz says the technique will effectively bridge Leibniz's gap: the symbolic distance between the mind and the body.

Now it must be remembered that the German was one of the three greatest, and most fundamentalist, rationalists of the 17th century: the other two were Rene Descartes and Baruch Spinoza (L-D-S). More specifically: All three believed that reality was composed fully of phenomena that could be explained by applying principles of logic to a priori, or fundamental, knowledge, subsequently discarding empirical evidence. If you think about it, this approach is flawless: if the basis of a hypothesis is logical, and if all the processes of development and experimentation on it are founded in logic, then the conclusion must also be logical.

[caption id="attachment_24472" align="aligncenter" width="529"] (L to R) Gottfried Leibniz, Baruch Spinoza, and Rene Descartes[/caption]

However, where this model does fall short is in describing an anomalous phenomenon that is demonstrably logical but otherwise inexplicable in terms of the dominant logical framework. This is akin to Thomas Kuhn's philosophy of science: a revolution is necessitated when enough anomalies accumulate that defy the reign of an existing paradigm, but until then, the paradigm will deny the inclusion of any new relationships between existing bits of data that don't conform to its principles.

When studying the brain (and when trying to recreate it in a lab), Leibniz's gap, as understood by L-D-S, cannot be applied for various reasons. First: the rationalist approach doesn't work because, while we're seeking logical conclusions that evolve from logical starts, we're in a good position to easily disregard the phenomenon called emergence that is prevalent in all simple systems that have high multiplicity. In fact, ironically, the L-D-S approach might be more suited for grounding empirical observations in logical formulae because it is only then that we run no risk of avoiding emergent paradigms.

[caption id="attachment_24473" align="aligncenter" width="529"] "Some dynamical systems are chaotic everywhere, but in many cases chaotic behavior is found only in a subset of phase space. The cases of most interest arise when the chaotic behavior takes place on an attractor, since then a large set of initial conditions will lead to orbits that converge to this chaotic region." - Wikipedia[/caption]

Second: It is important to not disregard that humans do not know much about the brain. As elucidated in the less favored of the two-methods I've described above, were we to reverse-engineer the brain, we can still only make the new-brain do what we already know that it already does. The L-D-S approach takes complete knowledge of the brain for granted, and works post hoc ergo propter hoc ("correlation equals causation") to explain it.

[youtube http://www.youtube.com/watch?v=MygelNl8fy4?rel=0]

Therefore, in order to understand the brain outside the ambit of rationalism (but still definitely within the ambit of empiricism), introspection need not be the only way. We don't always have to scrutinize our thoughts to understand how we assimilated them in the first place, and then move on from there, when we can think of the brain itself as the organ bridging Leibniz's gap. At this juncture, I'd like to reintroduce the importance of learning as a function of the brain.

To think of the brain as residing at a nexus, the most helpful logical frameworks are the computational theory of the mind (CTM) and the Copenhagen interpretation of quantum mechanics (QM).

[caption id="attachment_24476" align="aligncenter" width="529"] xkcd #45 (depicting the Copenhagen interpretation)[/caption]

In the CTM-framework, the brain is a processor, and the mind is the program that it's running. Accordingly, the organ works on a set of logical inputs, each of which is necessarily deterministic and non-semantic; the output, by extension, is the consequence of an algorithm, and each step of the algorithm is a mental state. These mental states are thought to be more occurrent than dispositional, i.e., more tractable and measurable than the psychological emergence that they effect. This is the break from Leibniz's gap that I was looking for.

Because the inputs are non-semantic, i.e., interpreted with no regard for what they mean, it doesn't mean the brain is incapable of processing meaning or conceiving of it in any other way in the CTM-framework. The solution is a technical notion called formalization, which the Stanford Encyclopedia of Philosophy describes thus:

... formalization shows us how semantic properties of symbols can (sometimes) be encoded in syntactically-based derivation rules, allowing for the possibility of inferences that respect semantic value to be carried out in a fashion that is sensitive only to the syntax, and bypassing the need for the reasoner to have employ semantic intuitions. In short, formalization shows us how to tie semantics to syntax.

A corresponding theory of networks that goes with such a philosophy of the brain is connectionism. It was developed by Walter Pitts and Warren McCulloch in 1943, and subsequently popularized by Frank Rosenblatt (in his 1957 conceptualization of the Perceptron, a simplest feedforward neural network), and James McClelland and David Rumelhart ('Learning the past tenses of English verbs: Implicit rules or par­allel distributed processing', In B. MacWhinney (Ed.), Mechanisms of Language Acquisition (pp. 194-248). Mah­wah, NJ: Erlbaum) in 1987.

[caption id="attachment_24477" align="aligncenter" width="529"] (L to R) Walter Pitts (L-top), Warren McCulloch (L-bottom), David Rumelhart, and James McClelland[/caption]

As described, the L-D-S rationalist contention was that fundamental entities, or monads or entelechies, couldn't be looked for in terms of physiological changes in brain tissue but in terms of psychological manifestations. The CTM, while it didn't set out to contest this, does provide a tensor in which the inputs and outputs are correlated consistently through an algorithm with a neural network for an architecture and a Turing/Church machine for an algorithmic process. Moreover, this framework's insistence on occurrent processes is not the defier of Leibniz: the occurrence is presented as antithetical to the dispositional.

[caption id="attachment_24478" align="alignleft" width="220"] Jerry Fodor[/caption]

The defier of Leibniz is the CTM itself: if all of the brain's workings can be elucidated in terms of an algorithm, inputs, a formalization module, and outputs, then there is no necessity to suppress any thoughts to the purely-introspectionist level (The domain of CTM, interestingly, ranges all the way from the infraconscious to the set of all modular mental processes; global mental processes, as described by Jerry Fodor in 2000, are excluded, however).

Where does quantum mechanics (QM) come in, then? Good question. The brain is a processor. The mind is a program. The architecture is a neural network. The process is that of a Turing machine. But how is the information between received and transmitted? Since we were speaking of QM, more specifically the Copenhagen interpretation of it, I suppose it's obvious that I'm talking about electrons and electronic and electrochemical signals being transmitted through sensory, motor, and interneurons. While we're assuming that the brain is definable by a specific processual framework, we still don't know if the interaction between the algorithm and the information is classical or quantum.

While the classical outlook is more favorable because almost all other parts of the body are fully understand in terms of classical biology, there could be quantum mechanical forces at work in the brain because - as I've said before - we're in no way to confirm or deny if it's purely classical or purely non-classical operationally. However, assuming that QM is at work, then associated aspects of the mind, such as awareness, consciousness, and imagination, can be described by quantum mechanical notions such as the wavefunction-collapse and Heisenberg's uncertainty principle - more specifically, by strong and weak observations on quantum systems.

The wavefunction can be understood as an avatar of the state-function in the context of QM. However, while the state-function can be constantly observable in the classical sense, the wavefunction, when subjected to an observation, collapses. When this happens, what was earlier a superposition of multiple eigenstates, metaphorical to physical realities, becomes resolved, in a manner of speaking, into one. This counter-intuitive principle was best summarized by Erwin Schrodinger in 1935 as a thought experiment titled...

[youtube http://www.youtube.com/watch?v=IOYyCHGWJq4?rel=0]

This aspect of observation, as is succinctly explained in the video, is what forces nature's hand. Now, we pull in Werner Heisenberg and his notoriously annoying principle of uncertainty: if either of two conjugate parameters of a particle is measured, the value of the other parameter is altered. However, when Heisenberg formulated the principle heuristically in 1927, he also thankfully formulated a limit of uncertainty. If a measurement could be performed within the minuscule leeway offered by the constant limit, then the values of the conjugate parameters could be measured simultaneously without any instantaneous alterations. Such a measurement is called a "weak" measurement.

Now, in the brain, if our ability to imagine could be ascribed - figuratively, at least - to our ability to "weakly" measure the properties of a quantum system via its wavefunction, then our brain would be able to comprehend different information-states and eventually arrive at one to act upon. By extension, I may not be implying that our brain could be capable of force-collapsing a wavefunction into a particular state... but what if I am? After all, the CTM does require inputs to be deterministic.

[caption id="attachment_24482" align="aligncenter" width="500"] How hard is it to freely commit to a causal chain?[/caption]

By moving upward from the infraconscious domain of applicability of the CTM to the more complex cognitive functions, we are constantly teaching ourselves how to perform different kinds of tasks. By inculcating a vast and intricately interconnected network of simple memories and meanings, we are engendering the emergence of complexity and complex systems. In this teaching process, we also inculcate the notion of free-will, which is simply a heady combination of traditionalism and rationalism.

While we could be, with the utmost conviction, dreaming up nonsensical images in our heads, those images could just as easily be the result of parsing different memories and meanings (that we already know), simulating them, "weakly" observing them, forcing successive collapses into reality according to our traditional preferences and current environmental stimuli, and then storing them as more memories accompanied by more semantic connotations.

A muffling of the monsoons

New research conducted at the Potsdam Institute for Climate Impact research suggests that global warming could cause frequent and severe failures of the Indian summer monsoon in the next two centuries.

The study joins a growing body of work conducted by different research groups across the last five years that demonstrate a negative relationship between the two phenomena.

The researchers, Jacob Schewe and Anders Levermann, defined failure as a decrease in rainfall by 40 to 70 per cent below normal levels. Their findings, published on November 6 in the Environmental Research Letters, show that as we move into the 22^nd century, increasing temperatures contribute to a strengthening Pacific Walker circulation that brings higher pressures over eastern India, which weaken the monsoon.

The Walker circulation was first proposed by Sir Gilbert Walker over 70 years go. It dictates that over regions such as the Indian peninsula, changes in temperature and changes in pressure and rainfall feedback into each other to bring a cyclic variation in rainfall levels.  The result of this is a seasonal high pressure over the western Indian Ocean.

Now, almost once every five years, the eastern Pacific Ocean undergoes a warm phase that leads to a high air pressure over it. This is called the El Nino Southern Oscillation.

In years when El Nino occurs, the high pressure over the western Indian Ocean shifts eastward and brings high pressure over land, suppressing the monsoon.

The researchers’ simulation showed that as temperatures increased in the future, the Walker circulation brings more high pressure over India on average, even though the strength of El Nino isn’t shown to increase.

The researchers described the changes they observed as unprecedented in the Indian Meteorological Department’s data, which dates back to the early-1900s. As Schewe, lead author of the study, commented to Phys Org, “Our study points to the possibility of even more severe changes to monsoon rainfall caused by climatic shifts that may take place later this century and beyond.”

A study published in 2007 by researchers at the Lawrence Livermore National Laboratory, California, and the International Pacific Research Centre, Hawaii, showed an increase in rainfall levels throughout much of the 21^st century followed by a rapid decrease. This is consistent with the findings of Schewe and Levermann.

Similarly, a study published in April 2012 by the Chinese Academy of Sciences demonstrated the steadily weakening nature of the Indian summer monsoon since 1860 owing to rising global temperatures.

The Indian economy, being predominantly agrarian, depends greatly on the summer monsoon which lasts from June to September. The country last faced a widespread drought due to insufficient rainfall in the summer of 2009, when it had to import sugar and pushed world prices for the commodity to a 30-year high.

Backfiring biofuels in the EU

A version of this article as written by me appeared in The Hindu on November 8, 2012.

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The European Union (EU) announced on October 17 that the amount of biofuels that will be required to make up the transportation energy mix by 2020 has been halved from 10 per cent to 5 per cent. The rollback mostly affects first-generation biofuels, which are produced from food crops such as corn, sugarcane, and potato.

The new policy is in place to mitigate the backfiring of switching from less-clean fossil fuels to first-generation biofuels. An impact assessment study conducted in 2009-2012 by the EU found that greenhouse gas emissions were on the rise because of conversion of agricultural land to land for planting first-generation biofuel crops. In the process, authorities found that large quantities of carbon stock had been released into the atmosphere because of forest clearance and peatland-draining.

Moreover, because food-production has now been shifted to take place in another location, transportation and other logistic fuel costs will have been incurred, emissions due to which will also have to be factored in. These numbers fall under the causal ambit of indirect land-use change (ILUC), which also includes the conversion of previously untenable land to fertile land. On October 17, The Guardian published an article that called the EU's proposals "watered down" because it had chosen not to penalize fuel suppliers involved in the ILUC.

This writer believes that's only fair - that the EU not penalize the fuel suppliers - considering the "farming" of first-generation biofuels was enabled, rather incentivized by the EU, which would have well known that agricultural processes would be displaced and that agricultural output would drop in certain pockets. The backfiring happened only because the organization had underestimated the extent to which collateral emissions would outweigh the carbon-credits saved due to biofuel-use. (As for not enforcing legal consequences on those who manufacture only first-generation biofuels but go on to claim carbon credits arising from second-generation biofuel use as well: continue reading.)

Anyway, as a step toward achieving the new goals, the EU will impose an emissions threshold on the amount of carbon stock that can be released when some agricultural land is converted for the sake of biofuel crops. Essentially, this will exclude many biofuels from entering the market.

While this move reduces the acreage because “fuel-farming” eats it up, it is not without criticisms. As Tracy Carty, a spokeswoman for the poverty action group Oxfam, said over Twitter, “The cap is lower than the current levels of biofuels use and will do nothing to reduce high food prices.” Earlier, especially in the US, the recourse of first-generation biofuels such as biodiesel had been resorted to by farmers looking to cash in on their steady (and state-assured) demand as opposed to the volatility of food prices.

The October 17 announcement effectively revises the Renewable Energy Directive (RED), 2009, which first required that biofuels constitute 10 per cent of the alternate energy mix by 2020.

The EU is now incentivising second-generation biofuels, mostly in an implied manner, which are manufactured from crop residues such as organic waste, algae, and woody materials, and do not interfere with food-production. The RED also requires that biofuels that replace fossil fuels be at least 35 per cent more efficient. Now, the EU has revised that number to increase to 50 per cent in 2017, and to 60 per cent after 2020. This is a clear sign that first-generation biofuels, which enter the scene with a bagful of emissions, will be phased out while their second-generation counterparts will take their places - at least, this ought to happen considering the profitability of first-generation alternatives is set to go down.

However, the research concerning high-performance biofuels is still nascent. As of now, it has been aimed at extracting the maximum amount of fuel from available stock, not as much at improving their efficiency. This is especially observed with the extraction of ethanol from wood, high-efficiency microalgae for biodiesel production, production of butanol from biomass with help from acetobutylicum, etc. - where more is known about the extraction efficiency and process economics than the performance of the fuel itself. Perhaps the new proposals will siphon some research out of the biotech. community in the near future.



Like the EU, the USA also has a biofuel-consumption target set for 2022, by when it requires that 36 billion gallons of renewable fuel be mixed with transport fuel, up from the 9 billion gallons mandated by 2008. More specifically, under the Energy Independence and Security Act (EISA) of 2007,

• RFS program to include diesel, in addition to gasoline;


• Establish new categories of renewable fuel, and set separate volume requirements for each one.


• EPA to apply lifecycle greenhouse gas performance threshold standards to ensure that each category of renewable fuel emits fewer greenhouse gases than the petroleum fuel it replaces.

(The underlined bit's what the EU has now included in its policies.)

However, a US National Research Council report released on October 24 found that if algal biofuels, second-generation fluids whose energy capacity lies between petrol’s and diesel’s, have to constitute as much as 5 percent of the country’s alternate energy mix, “unsustainable demands” would be placed on “energy, water and nutrients”.

Anyway, two major energy blocs – the USA and the EU – are leading the way to phase out first-generation biofuels and replace them completely with their second-generation counterparts. In fact, two other large-scale producers of biofuels, Indonesia and Argentina, wherefrom the EU imports 22 per cent of its biofuels, could also be forced to ramp up investment and research inline with their buyer's interests. As Gunther Oettinger, the EU Energy Commissioner, remarked, “This new proposal will give new incentives for best-performing biofuels.” The announcement also affirms that till 2020, no major changes will be effected in the biofuels sector, and post-2020, only second-generation biofuels will be supported, paving the way for sustained and focused development of high-efficiency, low-emission alternatives to fossil fuels.

(Note: The next progress report of the European Commission on the environmental impact of the production and consumption of biofuels in the EU is due on December 31, 2014.)

A cultured evolution?

Can perceptions arising out of cultural needs override evolutionary goals in the long-run?

For example, in India, the average marriage-age is in the late 20s now. Here, the (popular) tradition is to frown down upon, and even ostracize, those who would engage in premarital sex.

So, after 10,000 years, say, are Indians more likely to have the development of their sexual desires postponed to occur in their late 20s (if they are not exposed to any avenues of sexual expression)?

This question arose as a consequence of a short discussion with some friends on an article that appeared in SciAm: about if (heterosexual) men and women could stay "just friends".

To paraphrase the principal question in the context of the SciAm-featured "study":

1. Would you agree that the statistical implications of gender-sensitive studies will vary from region to region simply because the reasons on the basis of which such relationships can be established vary from one socio-political context to another?


2. Assuming you have agreed to the first question: Would you contend that the underlying biological imperatives can, someday, be overridden altogether in favor of holding up cultural paradigms (or vice versa)?

Is such a thing even possible? (To be clear: I'm not looking for hypotheses and conjectures; if you can link me to papers that support your point of view, that'd be great.)

A case of Kuhn, quasicrystals & communication – Part IV

Dan Shechtman won the Nobel Prize for chemistry in 2011. This led to an explosion of interest on the subject of QCs and Shechtman’s travails in getting the theory validated.

Numerous publications, from Reuters to The Hindu, published articles and reports. In fact, The Guardian ran an online article giving a blow-by-blow account of how the author, Ian Sample, attempted to contact Shechtman while the events succeeding the announcement of the prize unfolded.

All this attention served as a consummation of the events that started to avalanche in 1982. Today, QCs are synonymous with the interesting possibilities of materials science as much as with perseverance, dedication, humility, and an open mind.

Since the acceptance of the fact of QCs, the Israeli chemist has gone on to win Physics Award of the Friedenberg Fund (1986), the Rothschild Prize in engineering (1990), the Weizmann Science Award (1993), the 1998 Israel Prize for Physics, the prestigious Wolf Prize in Physics (1998), and the EMET Prize in chemistry (2002).

As Pauling’s influence on the scientific community faded with Shechtman’s growing recognition, his death in 1994 did still mark the complete lack of opposition to an idea that had long since gained mainstream acceptance. The swing in Shechtman’s favour, unsurprisingly, began with the observation of QCs and the icosahedral phase in other laboratories around the world.

Interestingly, Indian scientists were among the forerunners in confirming the existence of QCs. As early as in 1985, when the paper published by Shechtman and others in the Physical Review Letters was just a year old, S Ranganathan and Kamanio Chattopadhyay (amongst others), two of India’s preeminent crystallographers, published a paper in Current Science announcing the discovery of materials that exhibited decagonal symmetry. Such materials are two-dimensional QCs with periodicity exhibited in one of those dimensions.

The story of QCs is most important as a post-Second-World-War incidence of a paradigm shift occurring in a field of science easily a few centuries old.

No other discovery has rattled scientists as much in these years, and since the Shechtman-Pauling episode, academic peers have been more receptive of dissonant findings. At the same time, credit must be given to the rapid advancements in technology and human knowledge of statistical techniques: without them, the startling quickness with which each hypothesis can be tested today wouldn’t have been possible.

The analysis of the media representation of the discovery of quasicrystals with respect to Thomas Kuhn’s epistemological contentions in his The Structure of Scientific Revolutions was an attempt to understand his standpoints by exploring more of what went on in the physical chemistry circles of the 1980s.

While there remains the unresolved discrepancy – whether knowledge is non-accumulative simply because the information founding it has not been available before – Kuhn’s propositions hold in terms of the identification of the anomaly, the mounting of the crisis period, the communication breakdown within scientific circles, the shift from normal science to cutting-edge science, and the eventual acceptance of a new paradigm and the discarding of the old one.

Consequently, it appears that science journalists have indeed taken note of these developments in terms of The Structure. Thus, the book’s influence on science journalism can be held to be persistent, and is definitely evident.

A case of Kuhn, quasicrystals & communication – Part III

The doctrine of incommensurability arises out of the conflict between two paradigms and the faltering of communications between the two adherent factions.

According to Kuhn, scientists are seldom inclined to abandon the paradigm at the first hint of crisis – as elucidated in the previous section – and instead denounce the necessity for a new paradigm. However, these considerations aside, the implications for a scientist who proposes the introduction of a new paradigm, as Shechtman did, are troublesome.

Such a scientist will find himself ostracized by the community of academicians he belongs to because of the anomalous nature of his discovery and, thus, his suddenly questionable credentials. At the same time, because of such ostracism, the large audience required to develop the discovery and attempt to inculcate its nature into the extant paradigm becomes inaccessible.

As a result, there is a communication breakdown between the old faction and the new faction, whereby the former rejects the finding and continues to further the extant paradigm while the latter rejects the paradigm and tries to bring in a new one.

Incommensurability exists only during the time of crisis, when a paradigm shift is foretold. A paradigm shift is called so because there is no continuous evolution from the old paradigm to the new one. As Kuhn puts it (p. 103),

… the reception of a new paradigm often necessitates a redefinition of the corresponding science.

For this reason, what is incommensurable is not only the views of warring scientists but also the new knowledge and the old one. In terms of a finding, the old knowledge could be said to be either incomplete or misguided, whereas the new one could be remedial or revolutionary.

In Shechtman’s case, because icosahedral symmetries were altogether forbidden by the old theory, the new finding was not remedial but revolutionary. Therefore, the new terms that the finding introduced were not translatable in terms of the old one, leading to a more technical form of communication breakdown and the loss of the ability of scientists to predict what could happen next.

A final corollary of the doctrine is that because of the irreconcilable nature of the new and old knowledge, its evolution cannot be held to be continuous, only contiguous. In this sense, knowledge becomes a non-cumulative entity, one that cannot have been accumulated continuously over the centuries, but one that underwent constant redefinition to become what it is today.

As for Dan Shechtman, the question is this: Does the media’s portrayal of the crisis period reflect any incommensurability (be it in terms of knowledge or communication)?

How strong was the paradigm shift?

In describing the difference between “seeing” and “seeing as”, Kuhn speaks about two kinds of incommensurability as far as scientific knowledge is concerned. Elegantly put as “A pendulum is not a falling stone, nor is oxygen dephlogisticated air,” the argument is that when a paradigm shift occurs, the empirical data will remain unchanged even as the relationship between the data changes. In Shechtman’s and Levine’s cases, the discovery of “forbidden” 3D icosahedral point symmetry does not mean that the previous structures are faulty but simply that the new structure is now one of the possibilities.

However, there is some discrepancy regarding how much the two paradigms are really incommensurable. For one, Kuhn’s argument that an old paradigm and a new paradigm will be strongly incommensurable can be disputed: he says that during a paradigm shift, there can be no reinterpretation of the old theory that can transform to being commensurable with the new one.

However, this doesn’t seem to be the case: five-fold axes of symmetry were forbidden by the old theory because they had been shown mathematically to lack translational symmetry, and because the thermodynamics of such a structure did not fall in line with the empirical data corresponding to crystals that were perfectly crystalline or perfectly amorphous.

Therefore, the discovery of QCs established a new set of relationships between the parameters that influenced the formation of one crystal structure over another. At the same time, they did permit a reinterpretation of the old theory because the finding did not refute the old laws – it just introduced an addition.

For Kuhn to be correct a paradigm shift should have occurred that introduced a new relationship between different bits of data; in Shechtman’s case, the data was not available in the first place!

Here, Shechtman can be attributed with making a fantastic discovery and no more. There is no documented evidence to establish that someone observed QC before Shechtman did but interpreted it according to the older paradigm.

In this regard, what is thought to be a paradigm shift can actually be argued to be an enhancement of the old paradigm: no shift need have occurred. However, this was entirely disregarded by science journalists and commentators such as Browne and Eugene Garfield, who regarded the discovery of QCs as simply being anomalous and therefore crisis-prompting, indicating a tendency to be historicist - in keeping with the antirealism argument against scientific realism as put forth by Richard Boyd.

Thus, the comparison to The Structure that held up all this time fails.

There are many reasons why this could have been so, not the least of which is the involvement of Pauling and his influence in postponing the announcement of the discovery (Pauling’s credentials were, at the time, far less questionable than Shechtman’s were).

[caption id="attachment_24299" align="aligncenter" width="229"] Linus Carl Pauling (1901-1994) (Image from Wikipedia)[/caption]

As likely as oobleck

Alan I. Goldman, a professor of physics at the Iowa State University, wrote in the 84^th volume of the American Scientist,

Quasicrystals … are rather like oobleck, a form of precipitation invented by Dr. Seuss. Both the quasicrystals and the oobleck are new and unexpected. Since the discovery of a new class of materials is about as likely as the occurrence of a new form of precipitation, quasicrystals, like oobleck, suffered at first from a credibility problem.

There were many accomplished chemists who thought that QCs were nothing more than as-yet not fully understood crystal structures, and some among them even believed that QCs were an anomalous form of glass.

The most celebrated among those accomplished was Linus Pauling, who died in 1994 after belatedly acknowledging the existence of QCs. It was his infamous remark in 1982 that bought a lot of trouble for Shechtman, who was subsequently asked to leave the research group because he was “bringing disgrace” on its members and the paper he sought to publish was declined by journals.

Perhaps this was because he took immense pride in his works and in his contributions to the field of physical chemistry; otherwise, his “abandonment” of the old paradigm would have come easier – and here, the paradigm that did include an observation of QCs is referred to as old.

In fact, Pauling was so adamant that he proposed a slew of alternate crystal structures that would explain the structure of QCs as well as remain conformant with the old paradigm, with a paper appearing in 1988, long after QCs had become staple knowledge.

Order and periodicity

Insofar as the breakdown in communication is concerned, it seems to have stemmed from the tying-in of order and periodicity: crystallography’s handing of crystalline and amorphous substances had ingrained into the chemist’s psyche the coexistence of structures and repeatability.

Because the crystal structures of QCs were ordered but not periodical, even those who could acknowledge their existence had difficulty believing that QCs “were just as ordered as” crystals were, in the process isolating Shechtman further.

John Cahn, a senior crystallographer at NBS at the time of the fortuitous discovery, was one such person. Like Pauling, Cahn also considered possible alternate explanations before he could agree with Shechtman and ultimately co-author the seminal PRL paper with him.

His contention was that forbidden diffraction patterns – like the one Shechtman had observed – could be recreated by the superposition of multiple allowed but rotated patterns (because of the presence of five-fold symmetry, the angle of rotation could have been 72°).

[caption id="attachment_24300" align="aligncenter" width="500"] A crystal-twinning pattern in a leucite crystal[/caption]

This was explained through a process called twinning, whereby the growth vector of a crystal, during its growth phase, could suddenly change direction without any explanation or prior indication. In fact, Cahn’s exact response was,

Go away, Danny. These are twins and that’s not terribly interesting.

This explanation involving twinning was soon adopted by many of Shechtman’s peers, and he was repeatedly forced to return with results from the diffraction experiment to attempt to convince those who disagreed with the finding. His attempts were all in vain, and he was eventually dismissed from the project group at NBS.

Conclusion

All these events are a reflection of the communication breakdown within the academic community and, for a time, the two sides were essentially Shechtman and all the others.

The media portrayal of this time, however, seems to be completely factual and devoid of deduction or opining because of the involvement of the likes of Pauling and Cahn, who, in a manner of speaking, popularized the incident among media circles: that there was a communication breakdown became ubiquitous fact.

Shechtman himself, after winning the Nobel Prize for chemistry in 2011 for the discovery of QCs, admitted that he was isolated for a time before acceptance came his way – after the development of a crisis became known.

At the same time, there is the persisting issue of knowledge as being non-accumulative: as stated earlier, journalists have disregarded the possibility, not unlike many scientists, unfortunately, that the old paradigm did not make way for a new one as much as it became the new one.

That this was not the focus of their interest is not surprising because it is a pedantic viewpoint, one that serves to draw attention to the “paradigm shift” not being "Kuhnian" in nature, after all. Just because journalists and other writers constantly referred to the discovery of QCs as being paradigm-shifting need not mean that a paradigm-shift did occur there.