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title is misleading, the gold nucleii collide to create the a quark-gluon-plasma (probably) which eventually cools and condenses to create these new particles which are made from regular old quarks and gluons like every other baryonic particle.

Rather than somehow magically creating a new form of matter from gold.

I guess the exciting part is that they contain a hyperon which is a baryon with one strange quark in it, in this case it looks like a lambda-bar which is gonna be made from anti up, down and strange quarks. Well that and you've managed to bind three of these anti-particles into an anti-Helium or anti-Hydrogen isotope


Right on! Here's a bit more explanation from Ars Technica: http://arstechnica.com/science/news/2010/03/strange-antipart...


I think the imaginary time should be taken as an indication that it doesn't make sense to talk about travelling at v > c, in terms of a Special Relativistic framework at least :)


The usual energy loss mechanism for cosmic-rays (fast protons) is reverse-compton scattering off the cosmic-microwave background photons, which is quite amazing really. Some of the most energetic particles scattering off the ubiquitous but very low energy background.

Anyway, if you propose that this is the mechanism for energy loss and you know how dense the CMB photons are (which we do very well) then you can predict interesting things like the maximum distance a cosmic ray can travel before it runs out of steam entirely, see

http://en.wikipedia.org/wiki/Greisen–Zatsepin–Kuzmin_limit

Although i'm not sure how the OMG particle fits in with this scheme yet


I was looking at that too... the trick that I see is that that's a statistical limit, not a "travel X miles, lose X% energy, no matter what" limit. It's still entirely possible for a particle to be over the limit, it's just highly unlikely.

We detect super-energetic particles frequently, but only a couple "over the limit". Seems to fit the statistical model to me.


Sirens of titan! :)


inspiration and life path seeking? a little too rich a dish for my liking


Too much for a blog's goal?

It's honestly the major reason I continue to write. No man is an island. My views suck in isolation, they get pretty awesome after sharing. That process of shaking down vulnerable thoughts is priceless.


One of the authors, Baez, wrote "Gauge Fields, Knots, and Gravity" a lovely, cheap, and fairly accessible intro to applications of topology in mathematical physics. Worth a glance if you find yourself wondering about all this


The Brothers Karamazov

Gravity's Rainbow


check out "the structure and interpretation of classical mechanics" by sussmann et al for a whole raft of things like this done in scheme.

I don't see why would you want to stay away from floats, using rationals isn't going to magically fix the numerical errors from approximating derivatives and such.

I believe they develop an automatic differentiation routine in SICM that will actually allow you to make a pretty good planetary motion simulator, i.e one that won't just go horribly wrong if you run it for a few thousand years.

Of course if you're really interested/mathematically inclined one of the best ways to simulate Hamiltonian systems is via iterated maps, not unlike what was in the article. A good (but quite hard) ref to this is "simulating hamiltonian dynamics" by Leimkuhler and Reich.


Seems pretty clear to me, it doesn't claim to be an article for the non mathematically inclined. Not that "pop" articles on this subject wouldn't be pretty cool too.


Well, I'm pretty mathematically inclined, but ignorant of Bayesian statistics, and I still didn't really get it. Frinstance the first full-length paragraph:

The full Bayesian probability model includes the unobserved parameters. The marginal distribution over parameters is known as the “prior” parameter distribution, as it may be computed without reference to observable data. The conditional distribution over parameters given observed data is known as the “posterior” parameter distribution.

uses too much jargon; I'm sure I'd understand it if he'd defined "marginal distribution" and "conditional distribution" and clarified exactly what the difference between observable and unobservable data and/or parameters is. The hypothetical audience for this seems to be people who are intimately familiar with statistical terminology but know absolutely nothing about Bayesian statistics.


I think those concepts are best understood by example.


a one page comparison of NYC vs SF, blurgh...


Based on a four day visit rolleyes

Almost every time I visit a new place I want to move there, then I hang out for a few days and miss either New York or Sydney and realize that I'm perfectly content with my home(s).


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