38 citations · 140 across the 9 of their papers we have counts for
9 papers
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…
Constraining Cosmology with Simulation-based inference and Optical Galaxy Cluster Abundance
Moonzarin Reza, Yuanyuan Zhang, Camille Avestruz +3
We test the robustness of simulation-based inference (SBI) in the context of cosmological parameter estimation from galaxy cluster counts and masses in simulated optical datasets.…
Machine Learning and Cosmology
Cora Dvorkin, Siddharth Mishra-Sharma, Brian Nord +8
Methods based on machine learning have recently made substantial inroads in many corners of cosmology. Through this process, new computational tools, new perspectives on data colle…
Validating Synthetic Galaxy Catalogs for Dark Energy Science in the LSST Era
Eve Kovacs, Yao-Yuan Mao, Michel Aguena +36
Large simulation efforts are required to provide synthetic galaxy catalogs for ongoing and upcoming cosmology surveys. These extragalactic catalogs are being used for many diverse…
DeepSZ: Identification of Sunyaev-Zel'dovich Galaxy Clusters using Deep Learning
Zhen Lin, Nicholas Huang, Camille Avestruz +4
Galaxy clusters identified from the Sunyaev Zel'dovich (SZ) effect are a key ingredient in multi-wavelength cluster-based cosmology. We present a comparison between two methods of…
Algorithms and Statistical Models for Scientific Discovery in the Petabyte Era
Brian Nord, Andrew J. Connolly, Jamie Kinney +35
The field of astronomy has arrived at a turning point in terms of size and complexity of both datasets and scientific collaboration. Commensurately, algorithms and statistical mode…
The Role of Machine Learning in the Next Decade of Cosmology
Michelle Ntampaka, Camille Avestruz, Steven Boada +27
In recent years, machine learning (ML) methods have remarkably improved how cosmologists can interpret data. The next decade will bring new opportunities for data-driven cosmologic…
Star-galaxy classification in the Dark Energy Survey Y1 dataset
I. Sevilla-Noarbe, B. Hoyle, M. J. Marchã +75
We perform a comparison of different approaches to star-galaxy classification using the broad-band photometric data from Year 1 of the Dark Energy Survey. This is done by performin…
Large Synoptic Survey Telescope Galaxies Science Roadmap
Brant E. Robertson, Manda Banerji, Michael C. Cooper +47
The Large Synoptic Survey Telescope (LSST) will enable revolutionary studies of galaxies, dark matter, and black holes over cosmic time. The LSST Galaxies Science Collaboration has…