STAMPS@CMU and the NSF AI Planning Institute Physics for the Future jointly present:

Three-dimensional cosmography of the high redshift Universe using intergalactic absorption

by Collin Politsch (Machine Learning Department, Carnegie Mellon University)

Online webinar October 23, 2020 at 1:30-2:30 PM EDT.
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Abstract
The Lyman-α forest – a dense series of hydrogen absorptions seen in the spectra of distant quasars – provides a unique observational probe of the redshift z>2 Universe. The density of spectroscopically measured quasars across the sky has recently risen to a level that has enabled secure measurements of large-scale structure in the three-dimensional distribution of intergalactic gas using the inhomogeneous hydrogen absorption patterns imprinted in the densely sampled quasar sightlines. In principle, these modern Lyman-α forest observations can be used to statistically reconstruct three-dimensional density maps of the intergalactic medium over the massive cosmological volumes illuminated by current spectroscopic quasar surveys. However, until now, such maps have been impossible to produce without the development of scalable and statistically rigorous spatial modeling techniques. Using a sample of approximately 160,000 quasar sightlines measured across 25 percent of the sky by the SDSS-III Baryon Oscillation Spectroscopic Survey, here we present a 154 Gpc3 large-scale structure map of the redshift 1.98≤z≤3.15 intergalactic medium — the largest volume large-scale structure map of the Universe to date — accompanied by rigorous quantification of the statistical uncertainty in the reconstruction.

Bio

Collin is a Postdoctoral Fellow in the Machine Learning Department at Carnegie Mellon University. He received his joint Ph.D. in Statistics and Machine Learning from CMU in the summer of 2020 with his thesis titled "Statistical Astrophysics: From Extrasolar Planets to the Large-scale Structure of the Universe." Prior to that, he received a M.Sc. in Machine Learning from CMU in 2017 and a B.Sc. in Mathematics from the University of Kansas in 2014. His research interests include applications of statistical machine learning methods to problems in astrophysics, spatio-temporal data analysis, uncertainty quantification, and forecasting COVID-19.