Novel Bayesian Model to Fit Spectrophotometric Data of Hubble & Spitzer Space Telescopes (Mo Ashkik) скачать в хорошем качестве

Novel Bayesian Model to Fit Spectrophotometric Data of Hubble & Spitzer Space Telescopes (Mo Ashkik)

Speaker: Mo Ashkik Title: A Novel Bayesian Model to Fit Spectrophotometric Data of Hubble and Spitzer Space Telescopes Video:    • Novel Bayesian Model to Fit Spectrophotome...   Event description: Understanding how the most massive galaxies rapidly formed and quenched when Universe was only ~3 billion years old is one of the major challenges of extragalactic astronomy. In this talk, I will discuss how to improve our understanding of massive galaxy formation by combining the spectro-photometric observations of the Hubble and Spitzer Space Telescopes for strong gravitationally lensed galaxies. In particular, a multi-level regression model is built that can fit all multi-wavelength data for a range of instruments within a hierarchical Bayesian framework to constrain the properties of the stellar populations. The details of how this model is implemented using PyMC, as well as the estimates of the posteriors of all parameters of interest and nuisance parameters will be highlighted. Discourse Discussion https://discourse.pymc.io/t/a-novel-b... Timestamps 00:00 Start of event 02:03 Observable Astronomy: Grism Spectroscopy 03:42 Understanding Quiescent galaxies: How do they form so quickly? 05:35 Physical Model 08:16 Build the model - spectroscopy 11:27 Build the model - HST photometry 12:14 Build the model - Spitzer photometry 12:52 Build the model - Nuisance Parameters 20:50 Test prior using mock data 30:00 Summary Note: help us add timestamps here https://github.com/pymc-devs/video-ti... Speaker bio: Mo is a grad student of (astro)physics by day, a matheux and a Bayesian enthusiast all along. Broadly interested in cosmology and probability too. Speaker info: -Twitter:   / moakhshik   -GitHub: https://github.com/makhshik Part of PyMCon2020. More details at http://www.pymcon.com #bayesian #PyMC statistics