Using artificial intelligence to detect and characterise large samples of dwarf galaxies from the Euclid and LSST missions

Supervision

Pierre-Alain Duc (ObAS, Strasbourg)

Laboratory and team

ObAS, Strasbourg - Team GALHECOS

Subject description

Dwarf galaxies are by far the most numerous galaxies in the Universe, but probably also the most difficult to find and characterise. Indeed, their small size and low surface brightness make them particularly difficult to detect outside our Local Group. However, two instruments are about to overcome these difficulties. The Euclid space mission is already a game changer. Thanks to its large field of view and excellent image quality, it can clearly identify large populations of dwarf galaxies, as well as their nucleii and globular cluster populations. At the same time, the ground-based LSST experiment with the Rubin telescope will collect complementary data, in particular detailed colour information. The combination of LSST and Euclid photometry will, for example, help to rule out background galaxies as dwarf candidates and make it easier to identify the population of globular clusters (GCs). 

The PhD student will have privileged access to both sets of data at a critical time, thanks to the PhD supervisor's tickets. The long term aim of the thesis, which will be carried out in close collaboration with the international Euclid and LSST consortia, will be to:

- Identify the population of dwarf galaxies in a specific region of the sky - that around the Fornax supercluster. This region has been chosen because its density minimises the risk of contamination by background objects and maximises the chances of finding globular-cluster-rich objects, for which numerous follow-up studies may be done. 

- study the spatial distribution of dwarfs relative to the most massive galaxies, and the implications for nucleus and globular cluster content. Due to a lack of statistics, the causes of the large variations in the GC content of the dwarf population are still largely unknown. 

- Determine the most relevant parameters to fully characterise the dwarf populations (e.g. effective radius, surface brightness, colour, dark matter content), providing insightful criteria for their identification in the remaining Euclid and LSST surveys. 

One of the big challenges of this work is the large set of data currently available. The analysis of this data set require the development of ad hoc artificial intelligence tools. They are needed to : 

- detect and segment the LSB candidates in the images 

- validate their status as dwarf dwarf galaxies, excluding background objects with the caveat that their distances are not known 

- Determine automatically their properties 

Two ways have so far been explored : 

- Using pre-trained foundation models (I.e. galaxy Zoo) on image cutouts (when catalogs of sources are available), and further eye validation (with costumed visualisation tools) 

- Using dedicated neuronal networks and ad hoc filters (i.e. Gabor filters) to directly detect/segment the candidates, taking into account foreground or background contaminants (e.g. Galaxy cirrus) without the need of pre-defined cutouts 

Both methods will be compared, and their effectiveness depending on image depth, and availability of multi-sectral information will be analysed.

Related mathematical skills

Basic knowledge of AI / deep learning methods is needed. Having some interest in the analysis of astronomical data and more in general in astrophysics would be an asset.

Autre financement public

Candidates recruited as PhDs will benefit from IRMIA++ funding and will have to follow the Graduate Program "Mathematics and Applications: Research and Interactions" (https://irmiapp.unistra.fr/training/presentation).

IRMIA++ is one of the 15 Interdisciplinary Thematic Institute (ITI) of the University of Strasbourg. It brings together a research cluster and a master-doctorate training program, relying on 12 research teams and 9 master tracks.

It encompasses all mathematicians at Université de Strasbourg, with partners in computer science and physics. ITI IRMIA++ builds on the internationally renowned research in mathematics in Strasbourg, and its well-established links with the socio-economic environment. It promotes interdisciplinary academic collaborations and industrial partnerships.

A core part of the IRMIA++ mission is to realize high-level training through integrated master-PhD tracks over 5 years, with common actions fostering an interdisciplinary culture, such as joint projects, new courses and workshops around mathematics and its interactions.

Selection will rely on the professional project of the candidate, his/her interest for interdisciplinarity and academic results.