21 citations · 23 across the 5 of their papers we have counts for
5 papers
Analysing the prevalence of tidal features in HSC-SSP using self-supervised representation learning
A. Desmons, S. Brough, F. Lanusse +2
We use a combination of self-supervised machine learning and visual classification to identify tidal features in a sample of 34,331 galaxies with stellar masses $\log_{10}(M_{*}/\r…
Measuring the intracluster light fraction with machine learning
Louisa Canepa, Sarah Brough, Francois Lanusse +2
The intracluster light (ICL) is an important tracer of a galaxy cluster's history and past interactions. However, only small samples have been studied to date due to its very low s…
Detecting Galaxy Tidal Features Using Self-Supervised Representation Learning
Alice Desmons, Sarah Brough, Francois Lanusse
Low surface brightness substructures around galaxies, known as tidal features, are a valuable tool in the detection of past or ongoing galaxy mergers, and their properties can answ…
Detecting Tidal Features using Self-Supervised Representation Learning
Alice Desmons, Sarah Brough, Francois Lanusse
Low surface brightness substructures around galaxies, known as tidal features, are a valuable tool in the detection of past or ongoing galaxy mergers. Their properties can answer q…
The clustering of galaxies: Predictions from the BLUETIDES simulation
Aklant Kumar Bhowmick, Tiziana Di Matteo, Yu Feng +1
We study the clustering of the highest-z galaxies (from ~ to a few tens Mpc scales) using the BLUETIDES simulation and compare it to current observational constraints from Hu…