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DeepZipper: A Novel Deep Learning Architecture for Lensed Supernovae Identification
Robert Morgan, B. Nord, K. Bechtol +48
Large-scale astronomical surveys have the potential to capture data on large numbers of strongly gravitationally lensed supernovae (LSNe). To facilitate timely analysis and spectro…
The Dark Energy Survey Supernova Program: Cosmological biases from supernova photometric classification
M. Vincenzi, M. Sullivan, A. Möller +81
Cosmological analyses of samples of photometrically-identified Type Ia supernovae (SNe Ia) depend on understanding the effects of 'contamination' from core-collapse and peculiar SN…
Fink: early supernovae Ia classification using active learning
Marco Leoni, Emille E. O. Ishida, Julien Peloton +1
We describe how the Fink broker early supernova Ia classifier optimizes its ML classifications by employing an active learning (AL) strategy. We demonstrate the feasibility of impl…
SCONE: Supernova Classification with a Convolutional Neural Network
Helen Qu, Masao Sako, Anais Möller +1
We present a novel method of classifying Type Ia supernovae using convolutional neural networks, a neural network framework typically used for image recognition. Our model is train…
Rates and delay times of type Ia supernovae in the Dark Energy Survey
P. Wiseman, M. Sullivan, M. Smith +87
We use a sample of 809 photometrically classified type Ia supernovae (SNe Ia) discovered by the Dark Energy Survey (DES) along with 40415 field galaxies to calculate the rate of SN…
SkyMapper Optical Follow-up of Gravitational Wave Triggers: Alert Science Data Pipeline and LIGO/Virgo O3 Run
Seo-Won Chang, Christopher A. Onken, Christian Wolf +7
We present an overview of the SkyMapper optical follow-up program for gravitational-wave event triggers from the LIGO/Virgo observatories, which aims at identifying early GW170817-…