443 citations · 653 across the 8 of their papers we have counts for
9 papers
The Impact of Host Galaxy Properties on Supernova Classification with Hierarchical Labels
V. Ashley Villar, Sebastian Gomez, Edo Berger +1
With the advent of the Vera C. Rubin Observatory, the discovery rate of supernovae (SNe) will surpass the rate of SNe with real time spectroscopic followup by three orders of magni…
A Detection of Helium in the Bright Superluminous Supernova SN 2024rmj
Harsh Kumar, Edo Berger, Peter K. Blanchard +8
We present extensive ultraviolet (UV), optical, and near-infrared (NIR) photometric and spectroscopic observations of the nearby hydrogen-poor superluminous supernova (SLSN-I) SN20…
A Near-IR Search for Helium in the Superluminous Supernova SN 2024ahr
Harsh Kumar, Edo Berger, Peter K. Blanchard +13
We present a detailed study of SN 2024ahr, a hydrogen-poor superluminous supernova (SLSN-I), for which we determine a redshift of . SN 2024ahr has a peak absolute magnitu…
Superphot+: Realtime Fitting and Classification of Supernova Light Curves
Kaylee M. de Soto, Ashley Villar, Edo Berger +9
Photometric classifications of supernova (SN) light curves have become necessary to utilize the full potential of large samples of observations obtained from wide-field photometric…
The First Two Years of FLEET: an Active Search for Superluminous Supernovae
Sebastian Gomez, Edo Berger, Peter K. Blanchard +5
In November 2019 we began operating FLEET (Finding Luminous and Exotic Extragalactic Transients), a machine learning algorithm designed to photometrically identify Type I superlumi…
Identifying Tidal Disruption Events with an Expansion of the FLEET Machine Learning Algorithm
Sebastian Gomez, V. Ashley Villar, Edo Berger +5
We present an expansion of FLEET, a machine learning algorithm optimized to select transients that are most likely to be tidal disruption events (TDEs). FLEET is based on a random…