Blanton-Hogg group meeting

Blanton–Hogg group meeting re-started today. We began the introduction process that will take at least two meetings! I talked about inferring stellar ages from spectra.

Boris Leistedt spoke about large-scale structure projects he did this summer, including one in which they are finding a good basis for doing power spectrum estimation with weak lensing. He also talked about large-scale structure work he is doing in DES.

Kopytova spoke about her project with me to infer cool-star parameters, marginalizing out issues with calibration or continuum normalization. Blanton asked her to interpret the calibration inferences; that's a good idea!

Chang Hoon Hahn spoke about comparisons of two-point and three-point functions measured in SDSS-IV data with different fiber-collision methods, and the same in simulations. This led to our monthly argument about fiber collisions, which is a problem that sounds so simple and is yet so hard! The problem is that when two galaxies are very nearby on the sky, there is a reduced probability for getting the redshift of one of them. If left uncorrected, these missing data screw up two-point statistics badly (and one-point statistics less badly). Dan Cervone suggested that we look at the missing data literature for guidance. The big issue is that we don't have a good model for the spatial distribution of galaxies, and the best corrections for missing redshifts depend in detail on that spatial distribution! I suggested some data-driven approaches, but there might not be enough data to test or implement them.

Vakili spoke about using likelihood-free inference to do cosmological large-scale structure projects, which currently use a wrong (and difficult to compute) likelihood function. He and Hahn are collaborating on a test-bed to get this kind of inference working; we discussed their first results.

Late in the day, I spoke with Matt Kleban (NYU) about a statistical theory he has for the distribution (number per class) of "classified objects". He seems to have a model with almost no parameters that does a good job of explaining a wide range of data. I discussed with him some methods for testing goodness of fit and also model comparisons with other kinds of models.

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