lunes, 17 de septiembre de 2012

You Are Here: How Astronomical Surveys Are Pinpointing Our Place in the Cosmos

Upcoming telescope projects on Earth and in space will map out billions of stars and galaxies all around us

By John Matson

ZOOMING IN: A still from a simulated fly-through of galaxies mapped by the Sloan Digital Sky Survey. Image: Miguel A. Aragón (Johns Hopkins University), Mark SubbaRao (Adler Planetarium), Alex Szalay (Johns Hopkins University), Yushu Yao (Lawrence Berkeley National Laboratory, NERSC), and the SDSS-III Collaboration

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Like surveyors charting out a parcel of land by measuring angles, distances and elevations, astronomers have long mapped the positions of celestial objects in the sky.
Those celestial maps are about to see some major revisions. New and upcoming campaigns using ground-based telescopes or spacecraft promise to fill in many new details in astronomers’ maps of the sky. Together these projects will catalogue detailed positional information on several billion stars and galaxies near and far.
One of the most dramatic upgrades to celestial cartography should come from the European Space Agency’s Gaia spacecraft, which is scheduled to launch next year. After taking up a position in deep space, well beyond the orbit of the moon, Gaia will map the positions and distances of roughly one billion stars. The mission is the successor to the Hipparcos satellite, which launched in 1989 and whose catalogue still finds wide use. But that satellite charted just 120,000 stars or so, and only a slight minority were pinpointed with top-level precision.
Hipparcos measured precise stellar distances, to within 1 percent, for fewer than 1,000 stars. Most of the satellite’s distance measurements have much greater uncertainties of 20 percent or more. Gaia should measure the distances to about 10 million stars with a precision of 1 percent or better. “That’s about the quantum leap that we will make,” says Timo Prusti, project scientist for Gaia at the European Space Agency (ESA).
By mapping out so many stars, astronomers hope to improve their understanding of our home galaxy’s layout. “The main science goal is to address the issues of our Milky Way—the structure and the dynamics,” Prusti says. Buried as we are within the Milky Way, humankind has never had a glimpse of the galaxy in its entirety. The astronomer’s predicament is a bit like that of an artist who must sketch the Manhattan skyline from midtown, instead of from a clear vantage point across the Hudson River. Just as the artist can inspect Manhattan’s skyscrapers one by one to reconstruct the skyline in her sketch, the astronomer can fill in a map of the galaxy one star at a time.
A next-generation space telescope called Euclid ought to extend that map from the local to the global, by mapping up to two billion galaxies in three dimensions. The mission, which ESA approved in June for a 2020 launch, will scan roughly one third of the sky to measure the positions and distances of galaxies across the universe. The hope is that the distribution of cosmic structure will reveal some hidden clue to the nature of dark energy, the unknown entity driving the accelerating expansion of the universe.
“We have no idea what dark energy is, but it’s a very subtle effect,” says Richard Griffiths, the Euclid program scientist at NASA, which is a participant in the European-led mission. “The only way we can get at it is to study the whole universe, basically.”
Euclid’s design specs should allow astronomers to see galaxies so distant that their light has taken more than half the age of the universe to reach Earth. “We will literally obtain a three-dimensional image of our universe, with us in the very center, and we will be able to detect the accelerated expansion in it,” says ESA’s project scientist René Laureijs. “It will give us the opportunity to watch the universe evolving over the last 10 billion years.”
Euclid is not the only project charting galaxies to try to unravel the mystery of dark energy. A campaign called the Dark Energy Survey will soon take advantage of a new 570-megapixel camera on a four-meter telescope at the Cerro Tololo Inter-American Observatory in Chile. The survey will measure the shapes and positions of some 200 million galaxies across a quarter of the southern sky. Whereas the scope of the ground-based project pales in comparison to the billions of galaxies targeted by Euclid, the Dark Energy Survey should have a significant head start on its space-based counterpart. The project’s camera has just been installed on the telescope and could see first light as soon as this month, according to the project’s Facebook page.

Fuente

Scientific American Physics news@email.scientificamerican.com

Common Interpretation of Heisenberg's Uncertainty Principle Is Proved False

A new experiment shows that measuring a quantum system does not necessarily introduce uncertainty

By Geoff Brumfiel

The uncertainty principle limits what we can know about a quantum system, and that fuzziness is not entirely caused by the act of measurement. Image: flickr/@Doug88888

By Geoff Brumfiel of Nature magazine
Contrary to what many students are taught, quantum uncertainty may not always be in the eye of the beholder. A new experiment shows that measuring a quantum system does not necessarily introduce uncertainty. The study overthrows a common classroom explanation of why the quantum world appears so fuzzy, but the fundamental limit to what is knowable at the smallest scales remains unchanged.
At the foundation of quantum mechanics is the Heisenberg uncertainty principle. Simply put, the principle states that there is a fundamental limit to what one can know about a quantum system. For example, the more precisely one knows a particle's position, the less one can know about its momentum, and vice versa. The limit is expressed as a simple equation that is straightforward to prove mathematically.
Heisenberg sometimes explained the uncertainty principle as a problem of making measurements. His most well-known thought experiment involved photographing an electron. To take the picture, a scientist might bounce a light particle off the electron's surface. That would reveal its position, but it would also impart energy to the electron, causing it to move. Learning about the electron's position would create uncertainty in its velocity; and the act of measurement would produce the uncertainty needed to satisfy the principle.
Physics students are still taught this measurement-disturbance version of the uncertainty principle in introductory classes, but it turns out that it's not always true. Aephraim Steinberg of the University of Toronto in Canada and his team have performed measurements on photons (particles of light) and showed that the act of measuring can introduce less uncertainty than is required by Heisenberg’s principle. The total uncertainty of what can be known about the photon's properties, however, remains above Heisenberg's limit.
Delicate measurement
Steinberg's group does not measure position and momentum, but rather two different inter-related properties of a photon: its polarization states. In this case, the polarization along one plane is intrinsically tied to the polarization along the other, and by Heisenberg’s principle, there is a limit to the certainty with which both states can be known.
The researchers made a ‘weak’ measurement of the photon’s polarization in one plane — not enough to disturb it, but enough to produce a rough sense of its orientation. Next, they measured the polarization in the second plane. Then they made an exact, or 'strong', measurement of the first polarization to see whether it had been disturbed by the second measurement.
When the researchers did the experiment multiple times, they found that measurement of one polarization did not always disturb the other state as much as the uncertainty principle predicted. In the strongest case, the induced fuzziness was as little as half of what would be predicted by the uncertainty principle.
Don't get too excited: the uncertainty principle still stands, says Steinberg: “In the end, there's no way you can know [both quantum states] accurately at the same time.” But the experiment shows that the act of measurement isn't always what causes the uncertainty. “If there's already a lot of uncertainty in the system, then there doesn't need to be any noise from the measurement at all,” he says.
The latest experiment is the second to make a measurement below the uncertainty noise limit. Earlier this year, Yuji Hasegawa, a physicist at the Vienna University of Technology in Austria, measured groups of neutron spins and derived results well below what would be predicted if measurements were inserting all the uncertainty into the system.

Fuente Scientific American

Scientific American Physics news@email.scientificamerican.com

domingo, 16 de septiembre de 2012

The Curious Wavefunction

Musings on chemistry and the history and philosophy of science

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Theories, models and the future of science

By Ashutosh Jogalekar | September 5, 2012 |

Dark matter and dark energy: Models for accounting for the distribution of matter and the acceleration of the universe (Image: Edelweiss)

Last year’s Nobel Prize for physics was awarded to Saul Perlmutter, Brian Schmidt and Adam Riess for their discovery of an accelerating universe, a finding leading to the startling postulate that 75% of our universe contains a hitherto unknown entity called dark energy. This is an important discovery which is predated by brilliant minds and an exciting history. It continues a grand narrative that starts from Henrietta Swan Leavitt (who established a standard reference for calculating astronomical distances) through Albert Einstein (whose despised cosmological constant was resurrected by these findings) and Edwin Hubble, continuing through George Lemaitre and George Gamow (with their ideas about the Big Bang) and finally culminating in our current sophisticated understanding of the expanding universe.
But what is equally interesting is the ignorance that the prizewinning discovery reveals. The prize was awarded for the observation of an accelerating universe, not the explanation. Nobody really knows why the universe is accelerating. The current explanation for the acceleration consists of a set of different models incorporating entities like dark energy, none of which has been definitively proven to explain the facts well enough. And this makes me wonder if such a proliferation of models without accompanying concrete theories is going to embody science in the future.
The twentieth century saw theoretical advances in physics that agreed with experiment to an astonishing degree of accuracy. This progress culminated in the development of quantum electrodynamics, whose accuracy in Richard Feynman’s words is equivalent to calculating the distance between New York and Los Angeles within a hairsbreadth. Since then we have had some successes in quantitatively correlating theory to experiment, most notably in the work on validating the Big Bang and the development of the standard model of particle physics. But dark energy- there’s no theory for it as of now that remotely approaches the rigor of QED when it comes to comparison with experiment.
Of course it’s unfair to criticize dark energy since we are just getting started on tackling its mysteries. Maybe someday a comprehensive theory will be found, but given the complexity of what we are trying to achieve (essentially explain the nature of all the matter and energy in the universe) it seems likely that we may always be stuck with models, not actual theories. And this may be the case not just with cosmology but with other sciences. The fact is that the kinds of phenomena that science has been dealing with recently have been multifactorial, complex and emergent. The kind of mechanical, reductionist approaches that worked so well for atomic physics and molecular biology may turn out to be too impoverished for taking these phenomena apart. Take biology for instance. Do you think we could have a complete “theory” for the human brain that can quantitatively calculate all brain states leading to consciousness and our reaction to the external world? How about trying to build a “theory” for signal transduction that would allow us to not just predict but truly understand (in a holistic way) all the interactions with drugs and biomolecules that living organisms undergo? And then there’s other complex phenomena like the economy, the weather and social networks. It seems wise to say that we don’t anticipate real overarching theories for these phenomena anytime soon.

Molecular models - such as that of a ribosome depicted here - are already an integral part of chemistry and biology (Image: MRC)

On the other hand, I think it’s a sign of things to come that most of these fields are rife with explanatory models of varying accuracy and validity. Most importantly, modeling and simulation are starting to be considered as a respectable “third leg” of science, in addition to theory and experiment. One simple reason for this is the recognition that many of science’s greatest current challenges may not be amenable to rigorous theorizing, and we may have to treat models of phenomena as independent, authoritative explanatory entities in their own right. We are already seeing this happen in chemistry, biology, climate science and social science, and I have been told that even cosmologists are now extensively relying on computational models of the universe. My own field of drug discovery is a great example of the success and failure of models. Here models are used not just in computationally simulating the interactions of drugs with diseased proteins at a molecular level but in fitting pharmacological data and x-ray diffraction data, in constructing gene and protein networks and even in running and analyzing clinical trials. Models permeate drug discovery and development at every stage, and it’s hard to imagine a time when we will have an overarching “theory” encompassing the various stages of the process.
Admittedly these and other models are still far behind theory and experiment which have had head starts of about a thousand years. But there can be little doubt that such models can only become more accurate with increasing computational firepower and more comprehensive inclusion of data. How accurate remains to be seen, but it’s worth noting that there are already books that make a case for an independent, study-worthy philosophy of modeling and simulation; a recent book by the University of South Florida philosopher Eric Winsberg for instance extols philosophers of science to treat models not just as convenient applications and representations of theories (which are then the only fundamental things worth studying) but as ultimate independent explanatory devices in themselves that deserve separate philosophical consideration.
Could this then be at least part of the future of science? A future where robust experimental observations are encompassed not by beautifully rigorous and complete theories like general relativity or QED but only by different models which are patched together through a combination of rigor, empirical data, fudge factors and plain old intuition? This would be a new kind of science, as useful in its applications as its old counterpart but rooting itself only in models and not in complete theories. Given the history of theoretical science, such a future may seem dark and depressing. That is because as the statistician George Box famously quipped, although some models are useful, all models are in some sense wrong. What Box meant was that models often feature unrealistic assumptions about the details of a system, and yet allow us to reproduce the essential features of reality. They are subject to fudge factors and to the whims of their creators. Thus they can never provide the certain connection to “reality” that theories seem to. This is especially a problem when disparate models give the same answer to a question. In the absence of discriminating ideas, which model is then the “correct” one? The usual, convenient answer is “none of them”, since they all do an equally good job of explaining the facts. But this view of science, where models that can be judged only on the basis of their utility are the ultimate arbiters of reality and where there is thus no sense of a unified theoretical framework, feels deeply unsettling. In this universe the “real” theory will always remain hidden behind a facade of models, much as reality is always hidden behind the event horizon of a black hole. Such a universe can hardly warm the cockles of the heart of those who are used to crafting grand narratives for life and the cosmos. However it may be the price we pay for more comprehensive understanding. In the future, Nobel Prizes may be frequently awarded for important observations for which there are no real theories, only models. The discovery of dark matter and energy and our current attempts to understand the brain and signal transduction could well be the harbingers of this new kind of science.
Should we worry about such a world rife with models and devoid of theories? Not necessarily. If there’s one thing about science that we know, it’s that it evolves. Grand explanatory theories have traditionally been supposed to be a key part- probably the key part- of the scientific enterprise. But this is mostly because of historical precedent as well a psychological urge for seeking elegance and unification. And even historically sciences have progressed much without complete theories, as chemistry did for hundreds of years before the emergence of the atomic and structural theories. The belief that a grand theory is essential for the true development of a discipline has been resoundingly validated in the past but it’s utility may well have plateaued. I am not advocating some “end of science” scenario here – far from it – but as the recent history of string theory and theoretical physics in general demonstrates, even the most mathematically elegant and psychologically pleasing theories may have scant connection to reality. Because of the sheer scale and complexity of what we are trying to currently explain, we may have hit a roadblock in the application of the largely reductionist traditional scientific thinking which has served us so well for half a millennium
Ultimately what matters though is whether our constructs- theories, models, rules of thumb or heuristic pattern recognition- are up to the task of constructing consistent explanations of complex phenomena. The business of science is explanation, whether through unified narratives or piecemeal explanation is secondary. Although the former sounds more psychologically satisfying, science does not really care about stoking our egos. What is out there exists, and we do whatever’s necessary and sufficient to unravel it.
This is a revised version of a past post.

About the Author: Ashutosh (Ash) Jogalekar is a chemist interested in the nature of the "central science" and its intersection with philosophy, history and culture. He is also more generally interested in the history and philosophy of science and is particularly fascinated by how science tries to mirror reality by building models. Follow on Twitter @curiouswavefn.

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The views expressed are those of the author and are not necessarily those of Scientific American.