Author: Sean Carroll

For the last few months there’s been some excitement among particle-astrophysicists about intriguing results from the PAMELA satellite experiment and the ATIC balloon experiment. (We also blogged about it here and here.) PAMELA claimed to see an excess in the number of high-energy cosmic positrons (anti-electrons) over what you would expect from conventional astrophysical sources, while ATIC (which can’t distinguish between positrons and electrons) saw an overall rise in the number of positrons and electrons combined, more or less consistent with what PAMELA saw. One dramatic but plausible explanation for this result is that the positrons are produced when dark matter particles and antiparticles annihilated with each other, which would certainly be exciting. But it wasn’t quite a home run, because there was no evidence for the corresponding excess of anti-protons you would probably also expect. (Although that is not a deal-breaker; with a little ingenuity, particle physicists are able to come up with models that produce positrons but not anti-protons.) There was also some controversy when theorists wrote papers trying to fit the data before the data were even published, by snapping pictures of plots shown at conferences with their cell-phone cameras. More than enough drama for a TV movie, I would say.

A tinfoil-hat conspiracy theorist might imagine that all the excitement was intentionally manufactured, just so people would pay more attention to the first measurement from the new Fermi (formerly GLAST) gamma-ray telescope. And now those results are in! (Other Fermi results have already appeared, but not about this particular question.)

Sadly, the results are “in” in the sense of being published in Physical Review Letters, which helpfully charges $25 if you’re not a subscriber. (Presumably it will be on arxiv soon, probably tonight.) The best summary of the results, although somewhat technical, is by Bruce Winstein and Kathryn Zurek at Physics, the American Physical Society’s in-house journal that highlights interesting results.

And here are those results.

Fermi is more like ATIC than like PAMELA, in that it also cannot distinguish between electrons and positrons, so this graph shows both. The blue line is a simple model that you might expect in the absence of any dark-matter annihilations, and the red points are the Fermi results. If you look very closely, you can see the grey squares representing the ATIC data, which peak way up there between 100 GeV and 1000 GeV of energy.

So: hmm. Sadly it’s not a completely definitive result, either way. (This is reflected in the coverage in the popular press, where, unlike the physicists, they need to come to a conclusion: Ron Cowen at Science News says “Another Clue in the Case for Dark Matter,” while Adrian Cho at ScienceNow says “Lights Out for Dark Matter Claim?” Both do a good job in the body of their articles.) The Fermi data are clearly lower than the ATIC data were — but they’re not quite as low as the simple model would predict. The energy resolution of Fermi also isn’t quite as good — it’s harder for them to pinpoint the energy of each particle — so it’s conceivable that there is a sharp peak that simply gets smeared out by their instrument. But I completely agree with Winstein and Zurek’s take:

These results, as precise as they are, do not definitively confirm or rule out a DM source. Although the large ATIC excess, which had been consistent with PAMELA, is ruled out, because of uncertainties from charge-dependent modulation in the flux from the solar wind, the Fermi and PAMELA data do remain consistent as having the same source. Since several natural astrophysical explanations can generate the Fermi and PAMELA spectra, the likely course is that one will be found there. It may simply be, as the Fermi paper points out, that the primary electron spectrum in the cosmic-ray source, predicted to fall as ~E^-3.3 (where E is the particle energy), does not fall as steeply as thought in the energy range observed by Fermi.

In other words, it’s not too hard to imagine an astrophysical explanation that doesn’t require new physics beyond the Standard Model (which would still be interesting). But more data would be nice. We’ll keep looking.

A couple of years ago I got to hear Geoffrey West, one of Time magazine’s 100 Most Influential People, give a talk on his research at a meeting of the American Association for the Advancement of Science. It was a fantastic talk, and I immediately had the idea to ask him to come to Caltech at some point and give it as a colloquium. So tomorrow he’ll be here, and anyone in the neighborhood interested in a semi-technical account of complex systems from physics to biology is welcome to stop by. He might be angling for the record for the longest talk title ever:

The Complexity, Simplicity, and Unity of Living Systems from Cells to Cities: A Physicist’s Search for Quantitative, Unified Theories of Biological and Social Structure and Organization

Although Life is very likely the most complex phenomenon in the Universe, many of it’s most fundamental and complex phenomena scale with size in a surprisingly simple fashion. For example, metabolic rate scales approximately as the 3/4-power of mass over 27 orders of magnitude from complex molecules up to the largest multicellular organisms. Similarly, time-scales (such as lifespans and growth-rates) and sizes (such as genome lengths, RNA densities, and tree heights) scale as power laws with exponents which are typically simple multiples of 1/4. The universality and simplicity of these relationships, together with emergent “universal” invariants, suggest that fundamental constraints underly much of the coarse-grained generic structure and organisation of living systems. It will be shown how these 1/4 power scaling laws follow from underlying principles embedded in the dynamical and geometrical structure of space-filling, fractal-like, hierarchical branching networks, presumed optimised by natural selection. These ideas lead to a general quantitative, predictive theory that potentially captures the essential features of many diverse biological systems. Examples will include vascular systems, growth, cancer, aging and mortality, sleep, cell size, genome lengths, and DNA nucleotide substitution rates. These ideas will be extended to social organisations: to what extent are cities or corporations an extension of biology? Are they “just” very large organisms? Analogous scaling laws reflecting underlying social network structure point to general principles of organization common to all cities, but, counter to biological systems, the pace of social life systematically increases with size. This has dramatic implications for growth, development and particularly for sustainability: innovation and wealth creation that fuel social systems, if left unchecked, potentially sow the seeds for their inevitable collapse.

We’ve talked before about the difficulty in defining “life,” although one safe criterion is that a living organism is going to be pretty complex. What about the other way — when you have an undeniably complex system like a city or a university or a galaxy, at what point does it become useful to think of it as a “living organism”? Those are hard questions, but one angle is to investigate the similarities that complex systems demonstrate as they are manifested at different sizes. That’s the idea of “scaling laws” — measuring a feature common to a set of complicated systems (number of parts, speed of motion, etc.) and see how they change as a function of scale.

You might have imagined that complexity comes in a variety of completely different forms, and there would be no simple relationship that included viruses, house cats, and sprawling urban centers. But the data reveal a remarkable degree of regularity — many complex systems share certain basic features, just scaled up or down in ways appropriate to their size.

Here is one startling example: every living being on Earth gets about a billion heartbeats worth of lifespan. Larger organisms live longer, but their hearts (or other analogous rhythmic processes) beat more slowly. Use those heartbeats wisely!

The next challenge, of course, is to understand why. A few stabs have been taken in that direction using ideas about hierarchical networks of smaller systems — about which I shouldn’t say much, at least until I’ve heard the talk.

Those of you who can’t make it to LA on short notice can enjoy this video, or check out Blake Stacey’s live-blog of a previous talk.