Malgorzata Siudek - Uncovering galaxy evolutionary pathways using machine learning techniques
Seminar
IFAE
Our understanding of galaxy formation and evolution is making rapid advances, however, the outstanding, and basic, difficulty is to understand how the baryonic component collapses down to form galaxies. The actual mechanism that drives galaxy evolution and formation is poorly understood, and even more poorly constrained by observations. To solve this problem, we need both large datasets reaching past history of the Universe and, even more essential taking into account future surveys as JWST and Euclid, a novel methodologies suitable to deal with a mammoth dataset and an exceptional detailed information gathered by them.
I will demonstrate the potential of an unsupervised approach to galaxy classification based on ~50,000 VIPERS galaxies observed at redshift 0.4 < z < 1.2. Using machine-learning techniques working in a multidimensional space we revealed the true complexity of the galaxy population at z~0.7, a task that usual, simpler, colour-based approaches cannot fulfill. I will also show that large photometric samples can be used to distinguish different galaxy classes with an accuracy provided so far only by spectroscopic data except for particular galaxy classes.
Abelardo Moralejo
Anil Sonay
Bruno Bourguille
Camilo Rojas
Danaisis Vargas
Enrique Fernandez
Eric Peregrina
Javier Sabariego
Jorge Carretero
Machiel Kolstein
Manel Martinez
Marcel Algueró
Martine Bosman
Matteo Cavalli-Sforza
Matthias Jamin
michael leyton
Oscar Blanch Bigas
Oscar Martinez
Rafel Escribano
Raimon Casanova Mohr
Ramon Miquel
Sara Strauch
Sebastian Pina-Otey
Sergio González
Sidika Merve Colak
Tianya Wu