activity
19982026
most citedLSST: from Science Drivers to Reference Design and Anticipated Data Products

3.5k citations · 8k across the 58 of their papers we have counts for

collaborators

61 papers

astro-ph.CO2026

An improved standardization framework for Type II-P supernovae using a hierarchical Bayesian approach

A. Holas, S. Taubenberger, C. Vogl +41

We use Type II-P supernovae (SNe II-P) observed as part of the Nearby Supernova Factory (SNfactory) experiment as standardizable candles to develop an improved standardization fram…

astro-ph.HE2025

An Agnostic Approach to Building Empirical Type Ia Supernova Light Curves: Evidence for Intrinsic Chromatic Flux Variation Using Nearby Supernova Factory Data

Jared Hand, A. G. Kim, G. Aldering +35

We present a new empirical Type Ia supernova (SN Ia) model with three chromatic flux variation templates: one phase dependent and two phase independent. No underlying dust extincti…

astro-ph.IM2023

Rainbow: a colorful approach on multi-passband light curve estimation

E. Russeil, K. L. Malanchev, P. D. Aleo +10

We present Rainbow, a physically motivated framework which enables simultaneous multi-band light curve fitting. It allows the user to construct a 2-dimensional continuous surface a…

astro-ph.SR2022★ 7 cited

Bump Morphology of the CMAGIC Diagram

L. Aldoroty, L. Wang, P. Hoeflich +38

We apply the color-magnitude intercept calibration method (CMAGIC) to the Nearby Supernova Factory SNe Ia spectrophotometric dataset. The currently existing CMAGIC parameters are t…

cs.LG2022★ 1 cited

Explainable classification of astronomical uncertain time series

Michael Franklin Mbouopda, Emille E. O. Ishida, Engelbert Mephu Nguifo +1

Exploring the expansion history of the universe, understanding its evolutionary stages, and predicting its future evolution are important goals in astrophysics. Today, machine lear…

astro-ph.HE2022★ 12 cited

Supernova search with active learning in ZTF DR3

Maria V. Pruzhinskaya, Emille E. O. Ishida, Alexandra K. Novinskaya +9

We provide the first results from the complete SNAD adaptive learning pipeline in the context of a broad scope of data from large-scale astronomical surveys. The main goal of this…