6 citations · 11 across the 7 of their papers we have counts for
8 papers
DeepDISC on LSST Data Preview 1: Scene-Level Detection, Deblending, and Star/Galaxy Classification in the ECDFS and EDFS Fields
Prince Yadav, Yaswant Sai Ejjagiri, Xin Liu +2
We present the first application of DeepDISC, a scene-level deep-learning framework, to real LSST data, using the Data Preview 1 (DP1) coadded images of the Extended Chandra Deep F…
DB-Bench: Benchmarking Deblenders for LSST DESC Using the Blending ToolKit
Aidan Berres, Grant Merz, Xin Liu +3
Blending will be a major source of systematic uncertainty in downstream science analyses of LSST data. We benchmark the performance of several deblenders, leveraging the Blending T…
DeepDISC-Euclid: Source Classification and Photometric Redshifts in Euclid Deep Field North With a Pixel-Level Deep Learning Approach
Yuanzhe Jiang, Yue Shen, Grant Merz +8
The first Euclid Quick Data Release (Q1) provides extensive imaging and spectroscopic data for hundreds of millions of photometric objects across several deep fields. Accurate clas…
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…
Photometric Redshifts in JWST Deep Fields: A Pixel-Based Alternative with DeepDISC
Grant Merz, Ming-Yang Zhuang, Junyao Li +4
Photo-z algorithms that utilize SED template fitting have matured, and are widely adopted for use on high-redshift near-infrared data that provides a unique window into the early u…
Redshift Assessment Infrastructure Layers (RAIL): Rubin-era photometric redshift stress-testing and at-scale production
The RAIL Team, Jan Luca van den Busch, Eric Charles +31
Virtually all extragalactic use cases of the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) require the use of galaxy redshift information, yet the vast majorit…
DeepDISC-photoz: Deep Learning-Based Photometric Redshift Estimation for Rubin LSST
Grant Merz, Xin Liu, Samuel Schmidt +11
Photometric redshifts will be a key data product for the Rubin Observatory Legacy Survey of Space and Time (LSST) as well as for future ground and space-based surveys. The need for…
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…