Data Science is nowadays the "bread and butter" of the daily astronomer activity, as testified by a number of initiatives such as the International Astrostatistics Association, the Astroinformatics and Astrostatistics Commission of the IAU, or the IAU-IAA Astrostatistics and Astroinformatics seminar series.
We use advanced data science methods and techniques, and we combine them in cutting-edge distributed/parallel architectures in order to efficiently analyze very large astrophysical datasets. Our group includes experts in machine learning, computer science, and Bayesian inference.
The group is formeb by:
Fabio Castagna - INAF-Osservatorio Astronomico di Brera (Computer Science post-doc)
Marco Landoni - INAF-Osservatorio Astronomico di Brera
Paolo Benzoni, Giovanni Tessarolo, Nicolo' Molteni - Univ. Insubria (Three computer Science MS graduates)
Francesca Senarica - Univ. Milano (Physics MS)
Fabio made available to the astronomical community a Joint X-ray and SZ fitting code, JOXSZ, and, a pressure profile fitter for galaxy clusters, PreProfit. He is now leading a research to deploy MCMC analyses of large datasets on serverless computing. Giovanni and Nicolo' compared the performances of various (MCMC) samplers. Paolo ported in pymc the Bayesian codes of Stefano' book on Bayesian Methods. In preparation of the incoming eRosita data release, Francesca wrote a pipeline to automatically remote accessing and analyzing X-ray data of samples.
We have a number of MS and PhD subjects, such as: Galaxy cluster science in the Euclid era with Big Data Methods, Bringing efficient and scalable Bayesian computations for Big Data in the astronomer toolbox, Delivering the SZ potential of the James Clerk Maxwell Telescope, Quando l’eleganza vince sulla forza bruta.
[figure credits: Ohio University]