Detecting Low Surface Brightness Galaxies with Mask R-CNN

Conference · Fri Jan 01 04:00:00 EST 2021

OSTI ID:1834181

Levy, Caleb ^[1]; Ciprijanovic, Aleksandra ^[2]; Drlica-Wagner, Alex ^[2]; Mutlu-Pakdil, Burçin ^[3]; Nord, Brian ^[4]; Tanoglidis, Dimitrios ^[3]

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1. Colgate U.
2. Fermilab
3. Chicago U.
4. Fermilab; Chicago U.

Low surface brightness galaxies (LSBGs), galaxies that are fainter than the dark night sky, are famously difficult to detect. However, studies of these galaxies are essential to improve our understanding of the formation and evolution of low-mass galaxies. In this work, we train a deep learning model using the Mask R-CNN framework on a set of simulated LSBGs inserted into images from the Dark Energy Survey (DES) Data Release 2 (DR2). This deep learning model is combined with several conventional image pre-processing steps to develop a pipeline for the detection of LSBGs. We apply this pipeline to the full DES DR2 coadd image dataset, and preliminary results show the detection of 22 large, high-quality LSBG candidates that went undetected by conventional algorithms. Furthermore, we find that Galactic cirrus represents the largest contaminant in our resulting candidate list.

• Research Organization: Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)
• Sponsoring Organization: USDOE Office of Science (SC), High Energy Physics (HEP) (SC-25)
• Contributing Organization: DES
• DOE Contract Number: AC02-07CH11359
• OSTI ID: 1834181
• Report Number(s): FERMILAB-CONF-21-461-V; oai:inspirehep.net:1983184
• Country of Publication: United States
• Language: English