58 citations · 58 across the 2 of their papers we have counts for
6 papers
The Twins Embedding of Type Ia Supernovae I: The Diversity of Spectra at Maximum Light
K. Boone, G. Aldering, P. Antilogus +34
We study the spectral diversity of Type Ia supernovae (SNe Ia) at maximum light using high signal-to-noise spectrophotometry of 173 SNe Ia from the Nearby Supernova Factory. We dec…
The Twins Embedding of Type Ia Supernovae II: Improving Cosmological Distance Estimates
K. Boone, G. Aldering, P. Antilogus +34
We show how spectra of Type Ia supernovae (SNe Ia) at maximum light can be used to improve cosmological distance estimates. In a companion article, we used manifold learning to bui…
Anomaly detection in the Zwicky Transient Facility DR3
K. L. Malanchev, M. V. Pruzhinskaya, V. S. Korolev +11
We present results from applying the SNAD anomaly detection pipeline to the third public data release of the Zwicky Transient Facility (ZTF DR3). The pipeline is composed of 3 stag…
The SNEMO and SUGAR Companion Datasets
G. Aldering, P. Antilogus, C. Aragon +41
The Nearby Supernova Factory has made spectrophotometric observations of Type Ia supernovae since . This work presents an interim version of the data produced, including $210…
SUGAR: An improved empirical model of Type Ia Supernovae based on spectral features
P. -F. Léget, E. Gangler, F. Mondon +40
Type Ia Supernovae (SNe Ia) are widely used to measure the expansion of the Universe. Improving distance measurements of SNe Ia is one technique to better constrain the acceleratio…
Anomaly Detection in the Open Supernova Catalog
Maria V. Pruzhinskaya, Konstantin L. Malanchev, Matwey V. Kornilov +4
In the upcoming decade large astronomical surveys will discover millions of transients raising unprecedented data challenges in the process. Only the use of the machine learning al…