From the 1 of 6 linked papers with an AI index.
6 papers
DB-Bench: Benchmarking Deblenders for LSST DESC Using the Blending ToolKit
Aidan Berres, Grant Merz, Xin Liu +3
The paper evaluates several image deblending algorithms for LSST galaxy surveys using the Blending ToolKit, comparing their detection, segmentation, and reconstruction performance…
The Vera C. Rubin Observatory Data Preview 1
Vera C Rubin Observatory Team, Tatiana Acero Cuellar, Emily Acosta +325
We present Rubin Data Preview 1 DP1, the first data from the NSF DOE Vera C Rubin Observatory, comprising raw and calibrated single epoch images, coadds, difference images, detecti…
Opportunities in AI/ML for the Rubin LSST Dark Energy Science Collaboration
LSST Dark Energy Science Collaboration, Eric Aubourg, Camille Avestruz +63
The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will produce unprecedented volumes of heterogeneous astronomical data (images, catalogs, and alerts) that cha…
Enabling Early Transient Discovery in LSST via Difference Imaging with DECam
Yize Dong, Kaylee de Soto, V. Ashley Villar +58
We present SLIDE, a pipeline that enables transient discovery in data from the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST), using archival images from the Da…
MADNESS Deblender: Maximum A posteriori with Deep NEural networks for Source Separation
Biswajit Biswas, Eric Aubourg, Alexandre Boucaud +4
Due to the unprecedented depth of the upcoming ground-based Legacy Survey of Space and Time (LSST) at the Vera C. Rubin Observatory, approximately two-thirds of the galaxies are li…
The Blending ToolKit: A simulation framework for evaluation of galaxy detection and deblending
Ismael Mendoza, Andrii Torchylo, Thomas Sainrat +18
We present an open source Python library for simulating overlapping (i.e., blended) images of galaxies and performing self-consistent comparisons of detection and deblending algori…