BESTTIME · Big Extragalactic Surveys Treated Through Innovative MEthods

Big Extragalactic Surveys Treated Through Innovative MEthods

Astrophysics is at a tipping point where fundamentally new science can be empowered by applying machine learning techniques to large surveys with billions of galaxies and petabytes of data. This combination forms the cornerstone of the MSC project BESTTIME (Big Extragalactic Surveys Treated Through Innovative MEthods) A clear example of such a “data mining” approach is the Sloan Digital Sky Survey (SDSS), which provided photometric and spectroscopic information for local galaxies over one third of the sky. Over the last two decades, precious evidence to understand galaxy evolution came from SDSS thanks to such a wide area, resulting in a robust analysis that leverages large-number statistics. However, SDSS is limited to observe bright, nearby galaxies no further than a couple of billion light years from us. The exploration of the deeper cosmos (also meaning looking further back in time) has been conducted mainly by the Hubble Space Telscope (HST) with “pencil beam” surveys that pierced an extremely small area of the sky (<1 square degree in total). The overall objective of BESTTIME is to fill this gap by studying hundreds of thousands of galaxies (like SDSS) very far away (at distances reached by the HST surveys). More in detail, we focus on the most massive galaxies (5 to 100 times the Milky Way) which are very rare compared to other types of galaxies and therefore scarcely present in previous studies. Understanding the evolution of these galaxies is one of the most compelling issues in Astrophysics: how did they build up their mass so quickly? What prevented them to form new stars after only a few Gyr? By collecting unprecedented statistics for this kind of objects, the project aims at shedding light on both internal and “environmental” processes driving their characteristic growth, i.e., the formation of such unusually large number of stars in a relatively short period of cosmic time. With respect to the environmental processes, the pivotal goal is connecting these galaxies to the population of dark matter haloes hosting them, and on larger scales to the “cosmic web” of filaments that makes the fabric of the universe. The importance of this project for society goes beyond the scientific progress in Astrophysics, as the machine learning methods developed within BESTTIME can be transferred to other domains such as Biomedical Sciences. For example, the software used here to analyse telescope images and broaden our knowledge about the universe’s life may be also applied to X-ray or MRI images. The statistical tools applied to our census of galaxies may turn out to be useful for the big databases in Public Health Sciences.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз