Using machine-learning techniques to drag galaxies from the noise in deep imaging

Date
-
Call year
2017
Investigator
Johan Hendrik
Knapen Koelstra
Financial institution
Amount granted to the IAC Consortium
100.000,00 €
Description

We propose to extend our current lead in the area of deep astronomical imaging to decipher the secrets of galaxy formation and evolution. In particular, we will prepare for the imaging from the Large Synoptic Survey Telescope (LSST) to which we will soon have privileged access. The LSST project is truly a 'Big Data' project in astrophysics, which will produce 60 petabytes of imaging data at a rate of 20 TB per night, and which will initiate a profound chance in our profession, from gathering specific data to answer a question to data-driven exploration and discovery. We will develop algorithms to deal, in an automatic fashion, with two of the most important systematic problems affecting deep astronomical imaging: scattered light and foreground emission from Galactic cirrus. We know how to deal with this on scales of one or a few images, but will now include machine-learning techniques to automate the process, and make it work on huge datasets.

Related projects
Project Image
Spiral Galaxies: Evolution and Consequences

Our small group is well known and respected internationally for our innovative and important work on various aspects of the structure and evolution of nearby spiral galaxies. We primarily use observations at various wavelengths, exploiting synergies that allow us to answer the most pertinent questions relating to what the main properties of

Johan Hendrik
Knapen Koelstra
State of being in force
Level
Type of funding
Fundación BBVA