Seminário da Astronomia do ON aborda a conexão halo-galáxia através de aprendizado de máquina

Na quinta-feira, dia 17 de agosto, a Coordenação de Astronomia e Astrofísica (COAST) do Observatório Nacional (ON/MCTI) realizará uma nova edição de seu seminário. 

O evento virtual acontecerá às 14h (horário de Brasília) e terá transmissão ao vivo por meio do Canal do ON no YouTube. O tema será “CAMELS-SAM: untangling the galaxy-halo connection with machine learning and galaxy clustering”.

Para falar sobre o assunto, foi convidada a pesquisadora Lucia Perez, do Center for Computational Astrophysics, Flatiron Institute, EUA.

Título: CAMELS-SAM: untangling the galaxy-halo connection with machine learning and galaxy clustering

Resumo: As the next generation of large galaxy surveys comes online, it is becoming increasingly important to develop and understand the machine learning tools that analyze big astronomical data. Neural networks are powerful and capable of probing deep patterns in data but must be trained carefully on large and representative data sets. We developed and generated a new `hump' of the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project: CAMELS-SAM, encompassing one thousand dark-matter only simulations of (100 h^-1 cMpc)^3 with different cosmological parameters (Omega_M and sigma_8) and run through the Santa Cruz semi-analytic model for galaxy formation over a broad range of astrophysical parameters. In this talk, I will introduce and discuss the CAMELS and CAMELS-SAM ecosystems, and the exciting possibilities they enable. As a proof-of-concept for the power of the vast CAMELS-SAM suite of simulated galaxies in a large volume and broad parameter space, we probed the power of simple clustering summary statistics to marginalize over astrophysics and constrain cosmology using neural networks. We use the two-point correlation function, count-in-cells, and the Void Probability Function, and probe non-linear and linear scales across 0.68< R <27 h^-1 cMpc. We find our neural networks can both marginalize over the uncertainties in astrophysics to constrain cosmology to 3-8% error across various types of galaxy selections, while simultaneously learning about the SC-SAM astrophysical parameters. This work encompasses vital first steps toward creating algorithms able to marginalize over the uncertainties in our galaxy formation models and measure the underlying cosmology of our universe. CAMELS-SAM has been publicly released alongside the rest of CAMELS, and offers great potential to many applications of machine learning in astrophysics: https://camels-sam.readthedocs.io.

Link para live no YouTube: https://www.youtube.com/watch?v=lzlNen5a_RI

Durante este ano, os seminários da COAST estão acontecendo de forma presencial, híbrida ou remota – pelo canal do ON no YouTube. 

Para mais informações: seminarioscoast@on.br 

As edições anteriores estão disponíveis na playlist.