Type Ia supernovae (SNe Ia) are thermonuclear exploding stars that can be used to put constraints on the nature of our universe. One challenge with population analyses of SNe Ia is Malmquist bias, where we preferentially observe the brighter SNe due to limitations of our telescopes. If untreated, this bias can propagate through to our posteriors on cosmological parameters. In this work, we...
The Vera C. Rubin Observatory’s LSST will detect millions of transient candidates through difference image analysis (DIA), issuing real-time alerts to the community. While DIA is highly sensitive, it is prone to spurious detections caused by noise, artifacts, or imperfect subtractions. Filtering these out—known as the "Real/Bogus" problem—typically relies on supervised machine learning trained...
