119 citations · 440 across the 33 of their papers we have counts for
33 papers
The Roman eXtreme Deep Field (RXDF)
Haojing Yan, Anton M. Koekemoer, Yue Shen +127
The Roman eXtreme Deep Field (RXDF) program is one of the five General Astrophysics Survey (GAS) programs approved for observing time with the Nancy Grace Roman Space Telescope in…
Insights on Galaxy Evolution from Interpretable Sparse Feature Networks
John F. Wu
Galaxy appearances reveal the physics of how they formed and evolved. Machine learning models can now exploit galaxies' information-rich morphologies to predict physical properties…
Disentangling Dense Embeddings with Sparse Autoencoders
Charles O'Neill, Christine Ye, Kartheik Iyer +1
Sparse autoencoders (SAEs) have shown promise in extracting interpretable features from complex neural networks. We present one of the first applications of SAEs to dense text embe…
pathfinder: A Semantic Framework for Literature Review and Knowledge Discovery in Astronomy
Kartheik G. Iyer, Mikaeel Yunus, Charles O'Neill +27
The exponential growth of astronomical literature poses significant challenges for researchers navigating and synthesizing general insights or even domain-specific knowledge. We pr…
Predicting dark matter halo masses from simulated galaxy images and environments
Austin J. Larson, John F. Wu, Craig Jones
Galaxies are theorized to form and co-evolve with their dark matter halos, such that their stellar masses and halo masses should be well-correlated. However, it is not known whethe…
Designing an Evaluation Framework for Large Language Models in Astronomy Research
John F. Wu, Alina Hyk, Kiera McCormick +15
Large Language Models (LLMs) are shifting how scientific research is done. It is imperative to understand how researchers interact with these models and how scientific sub-communit…
Katachi: Decoding the Imprints of Past Star Formation on Present Day Morphology in Galaxies with Interpretable CNNs
Juan Pablo Alfonzo, Kartheik G. Iyer, Masayuki Akiyama +8
The physical processes responsible for shaping how galaxies form and quench over time leave imprints on both the spatial (galaxy morphology) and temporal (star formation history; S…
PHANGS-ML: dissecting multiphase gas and dust in nearby galaxies using machine learning
Dalya Baron, Karin M. Sandstrom, Erik Rosolowsky +25
The PHANGS survey uses ALMA, HST, VLT, and JWST to obtain an unprecedented high-resolution view of nearby galaxies, covering millions of spatially independent regions. The high dim…
Deep Learning Cosmic Ray Transport from Density Maps of Simulated, Turbulent Gas
Chad Bustard, John Wu
The coarse-grained propagation of Galactic cosmic rays (CRs) is traditionally constrained by phenomenological models of Milky Way CR propagation fit to a variety of direct and indi…
How the Galaxy-Halo Connection Depends on Large-Scale Environment
John F. Wu, Christian Kragh Jespersen, Risa H. Wechsler
We investigate the connection between galaxies, dark matter halos, and their large-scale environments at with Illustris TNG300 hydrodynamic simulation data. We predict stella…
Constructing Impactful Machine Learning Research for Astronomy: Best Practices for Researchers and Reviewers
D. Huppenkothen, M. Ntampaka, M. Ho +19
Machine learning has rapidly become a tool of choice for the astronomical community. It is being applied across a wide range of wavelengths and problems, from the classification of…
Quantifying Roman WFI Dark Images with the Wavelet Scattering Transform
Phani Datta Velicheti, John F. Wu, Andreea Petric
The Nancy Grace Roman Space Telescope will survey a large area of the sky at near-infrared wavelengths with its Wide Field Instrument (WFI). The performance of the 18 WFI H4RG-10 d…
Learning the galaxy-environment connection with graph neural networks
John F. Wu, Christian Kragh Jespersen
Galaxies co-evolve with their host dark matter halos. Models of the galaxy-halo connection, calibrated using cosmological hydrodynamic simulations, can be used to populate dark mat…
NANCY: Next-generation All-sky Near-infrared Community surveY
Jiwon Jesse Han, Arjun Dey, Adrian M. Price-Whelan +206
The Nancy Grace Roman Space Telescope is capable of delivering an unprecedented all-sky, high-spatial resolution, multi-epoch infrared map to the astronomical community. This oppor…
JWST constraints on the UV luminosity density at cosmic dawn: implications for 21-cm cosmology
Sultan Hassan, Christopher C. Lovell, Piero Madau +10
An unprecedented array of new observational capabilities are starting to yield key constraints on models of the epoch of first light in the Universe. In this Letter we discuss the…
A variable active galactic nucleus at triply-imaged by the galaxy cluster MACS J0035.4-2015
Lukas J. Furtak, Ramesh Mainali, Adi Zitrin +32
We report the discovery of a triply imaged active galactic nucleus (AGN), lensed by the galaxy cluster MACS J0035.4-2015 (). The object is detected in Hubble…
Identification of galaxy shreds in large photometric catalogs using Convolutional Neural Networks
Enrico M. Di Teodoro, Josh E. G. Peek, John F. Wu
Contamination from galaxy fragments, identified as sources, is a major issue in large photometric galaxy catalogs. In this paper, we prove that this problem can be easily addressed…
Identifying AGN host galaxies with convolutional neural networks
Ziting Guo, John F. Wu, Chelsea E. Sharon
Active galactic nuclei (AGN) are supermassive black holes with luminous accretion disks found in some galaxies, and are thought to play an important role in galaxy evolution. Howev…
Target Selection and Sample Characterization for the DESI LOW-Z Secondary Target Program
Elise Darragh-Ford, John F. Wu, Yao-Yuan Mao +39
We introduce the DESI LOW-Z Secondary Target Survey, which combines the wide-area capabilities of the Dark Energy Spectroscopic Instrument (DESI) with an efficient, low-redshift ta…
Identification of Galaxy-Galaxy Strong Lens Candidates in the DECam Local Volume Exploration Survey Using Machine Learning
E. A. Zaborowski, A. Drlica-Wagner, F. Ashmead +88
We perform a search for galaxy-galaxy strong lens systems using a convolutional neural network (CNN) applied to imaging data from the first public data release of the DECam Local V…
A Machine Learning Approach to Enhancing eROSITA Observations
John Soltis, Michelle Ntampaka, John Wu +5
The eROSITA X-ray telescope, launched in 2019, is predicted to observe roughly 100,000 galaxy clusters. Follow-up observations of these clusters from Chandra, for example, will be…
LADUMA: Discovery of a luminous OH megamaser at
Marcin Glowacki, Jordan D. Collier, Amir Kazemi-Moridani +65
In the local Universe, OH megamasers (OHMs) are detected almost exclusively in infrared-luminous galaxies, with a prevalence that increases with IR luminosity, suggesting that they…
The DECam Local Volume Exploration Survey Data Release 2
A. Drlica-Wagner, P. S. Ferguson, M. Adamów +121
We present the second public data release (DR2) from the DECam Local Volume Exploration survey (DELVE). DELVE DR2 combines new DECam observations with archival DECam data from the…
Extending the SAGA Survey (xSAGA) I: Satellite Radial Profiles as a Function of Host Galaxy Properties
John F. Wu, J. E. G. Peek, Erik J. Tollerud +6
We present "Extending the Satellites Around Galactic Analogs Survey" (xSAGA), a method for identifying low- galaxies on the basis of optical imaging, and results on the spatial…