LSST-DA Catalyst Fellow Aritra Ghosh led a team, including researchers from LSST-DA member institutions University of Washington (UW) and Yale, to discover that galaxies in dense environments are as much as 25% larger than otherwise similar galaxies in more isolated environments. UW and Yale issued press releases about this work.
To arrive at this finding, Dr. Ghosh and his team created a machine-learning code that can determine the structure of a galaxy in less than a millisecond and created a catalog of millions of galaxies, both of which they made openly available to other astronomers. Galaxy-evolution expert Charlotte Olson, who is also a Catalyst Fellow, shared that “finding trends in how galaxy properties relate to the environments in which they live can be incredibly revealing in terms of what physical processes dominate in galaxy evolution. This is why the correlation Aritra is finding between size and density of environment is so significant.” Olsen added, “Arita’s method of getting the most out of a large sample using machine learning is paving the way for other researchers like myself who will need to use these types of tools and methods to conduct our own science on LSST data.”
A burgeoning leader within the LSST science community, Dr. Ghosh recently proposed and chaired a session at the 2024 Rubin Community Workshop that brought together members of the US Data Facility at SLAC, pipeline scientists from Rubin Data Management, and future users of Rubin LSST to ensure access to the type of data products — Rubin image cut-outs — that will allow Dr. Ghosh’s work to be extended with Rubin data.
The LSST-DA Catalyst Fellowship funded by the John Templeton Foundation is an interdisciplinary prize postdoctoral fellowship that is part of the LSST Interdisciplinary Network for Collaboration and Computing (LINCC). LINCC also includes the LINCC Frameworks project to build open-source LSST analysis software, based at UW and Carnegie Mellon, and the Data Science Fellowship Program for graduate students run by Northwestern.