In the late weekend (after taxes), I worked on fundamental photometric calibration, analyzing the contribution by Stubbs and Tonry. I don't think a great deal of what they say is truly new, but it is certainly about time someone wrote it down and got the conversation rolling. It is boring as heck, but it is required if precision cosmology is to move forward. My own contributions on this subject are here (my most-cited first-author paper) and here (my least).
This afternoon, Burles, Coil (Arizona), and I discussed the current and near-future steps with PRIMUS. Coil, Blanton, and Eisenstein are working on sample selection, mechanical collisions
, and masks. Cool (Arizona) is working on extractions. Burles is working on getting the wavelength solutions right (his arc-fitting software is incredible). Masjedi is working on redshifts. What's next? Science. Note how I am not working on anything. Very clever, no?
At group meeting, Burles told us about the gravitational lenses he and Bolton (Harvard) have been finding in copious numbers (about 40?) from looking for two-redshift objects in the SDSS spectroscopy. They have one of the largest statistical collections of gravitational lenses in existence, and they have many uses for lensing, cosmology, and galaxy astrophysics.
After Burles, Coil told us about the clustering of quasars (from SDSS) with galaxies (from DEEP2) at redshift of unity. She finds that quasars are clustered like the galaxies, and have similar bias. She finds this at great signal-to-noise by using a cross-correlation (rather than auto-correlation). Indeed, as Eisenstein, I, and others have been arguing for many years, the cross-correlation is much higher in signal-to-noise than the auto-correlation function for rare populations, and you should almost never use the latter when you can use the former. Coil's result is a great advertisement for this fact, because the cross-correlation of DEEP2 with 17 (yes, 17) SDSS quasars has a higher signal-to-noise measurement of clustering at Mpc scales than the entire 2dF QSO survey auto-correlation function!
After Coil, Marla Geha (OCIW) told us about the gas fractions of dwarf galaxies, which show an enormous range, but one that is a very strong function of environment. Dwarf galaxies with very low gas fractions are almost always close to (ie, within hundreds of kpc of) more luminous galaxies. This effect has never been seen before because prior to Blanton et al (2005), there has not been a dwarf sample selected without regard to environment! The very nice thing is that Geha's results (with Blanton and Masjedi) rule out many ideas about dwarf galaxy evolution and support others—a rare thing in the world of galaxy astrophysics, filled as it is with soft predictions.
After group meeting, Mukhanov (Munich) gave a wonderful informal talk about what inflation naively and straightforwardly predicts (and what it does not). Nice!
So... what does inflation naively and straightforwardly predict? (And by "naively and straightforwardly" do we mean "can be figured out without too much effort" or "will be true unless you delicately tune your inflationary model"?)
ReplyDeleteSean! I am honored. I mean both: As you can imagine, Mukhanov (and I) believe simple, order-of-magnitude arguments over anything complicated!
ReplyDeleteThe gist of the answer is: If you use inflation to solve for you the causality problem, then in general you have so much inflation that you get a Universe that is very close to spatially flat and that has a primordial power spectrum that is not exactly H-Z, but with a weak logarithmic dependence on scale (so WMAP, eg, will measure something like 0.92<n<0.97).
The only sense in which there was any fine tuning would be if Mukhanov had been required to show us a potential that produced enough inflation, but he was talking about inflation as a kinematic phase, not a dynamical one. I think this is fair, but not everyone would agree!
Great. I'm all in favor of extracting the simple and robust predictions. But it's still important to keep in mind that simple modifications (e.g. adding another light scalar) can change the predictions (e.g. by giving you a blue spectrum).
ReplyDelete