Kevin Schawinski

Physics and Astronomy · Physical Sciences

74h-index21.0kcitations393works2.22yr avg
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Research Topics

Galaxies: Formation, Evolution, Phenomena(204), Astronomy and Astrophysical Research(171), Astrophysical Phenomena and Observations(112), Gamma-ray bursts and supernovae(75), Stellar, planetary, and galactic studies(68)

Publications391 total

Sparks of Science: Hypothesis Generation Using Structured Paper Data
arXiv (Cornell University)·2025· 1 citedOpen Access
A Survey on Hypothesis Generation for Scientific Discovery in the Era of Large Language Models
arXiv (Cornell University)·2025· 1 citedOpen Access
Automatic Machine Learning Framework to Study Morphological Parameters of AGN Host Galaxies within z < 1.4 in the Hyper Supreme-Cam Wide Survey
The Astrophysical Journal·2025· 2 citedOpen Access
Automatic Machine Learning Framework to Study Morphological Parameters of AGN Host Galaxies within $z < 1.4$ in the Hyper Supreme-Cam Wide Survey
arXiv (Cornell University)·2025Open Access
Radio Galaxy Zoo data release 1: 100185 radio source classifications from the FIRST and ATLAS surveys
Monthly Notices of the Royal Astronomical Society·2024· 5 citedOpen Access
Radio Galaxy Zoo Data Release 1: 100,185 radio source classifications from the FIRST and ATLAS surveys
arXiv (Cornell University)·2024Open Access
pathfinder: A Semantic Framework for Literature Review and Knowledge Discovery in Astronomy
The Astrophysical Journal Supplement Series·2024· 7 citedOpen Access
pathfinder: A Semantic Framework for Literature Review and Knowledge Discovery in Astronomy
arXiv (Cornell University)·2024Open Access
AstroLLaMA-Chat: Scaling AstroLLaMA with Conversational and Diverse Datasets
Research Notes of the AAS·2024· 6 citedOpen Access
AstroLLaMA-Chat: Scaling AstroLLaMA with Conversational and Diverse Datasets
arXiv (Cornell University)·2024Open Access
AstroLLaMA: Towards Specialized Foundation Models in Astronomy
arXiv (Cornell University)·2023· 4 citedOpen Access
Using Machine Learning to Determine Morphologies of z < 1 AGN Host Galaxies in the Hyper Suprime-Cam Wide Survey
The Astrophysical Journal·2023· 4 citedOpen Access
AstroLLaMA: Towards Specialized Foundation Models in Astronomy
2023· 13 citedOpen Access
Using Machine Learning to Determine Morphologies of $z<1$ AGN Host Galaxies in the Hyper Suprime-Cam Wide Survey
arXiv (Cornell University)·2022Open Access
GaMPEN: A Machine-learning Framework for Estimating Bayesian Posteriors of Galaxy Morphological Parameters
The Astrophysical Journal·2022· 11 citedOpen Access
GaMPEN: A Machine Learning Framework for Estimating Bayesian Posteriors of Galaxy Morphological Parameters
arXiv (Cornell University)·2022· 1 citedOpen Access
BASS. XXV. DR2 Broad-line-based Black Hole Mass Estimates and Biases from Obscuration
The Astrophysical Journal Supplement Series·2022· 59 citedOpen Access
BASS. XXX. Distribution Functions of DR2 Eddington Ratios, Black Hole Masses, and X-Ray Luminosities
The Astrophysical Journal Supplement Series·2022Open Access
BASS. XXIV. The BASS DR2 Spectroscopic Line Measurements and AGN Demographics
The Astrophysical Journal Supplement Series·2022Open Access
BASS. XXVI. DR2 Host Galaxy Stellar Velocity Dispersions
The Astrophysical Journal Supplement Series·2022· 46 citedOpen Access
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Frequent Co-authors

Chris Lintott(23), S. P. Bamford(18), R. C. Nichol(18), Alexander S. Szalay(16), D. Thomas(15), M. Jordan Raddick(14), Phil Murray(13), Jan Vandenberg(13), Anže Slosar(12), Dan Andreescu(12), Ezequiel Treister(12), Karen L. Masters(11), Sugata Kaviraj(11), M. Sarzi(9), Sukyoung K. Yi(9), C. M. Urry(9), Brooke Simmons(8), William C. Keel(7), Edward M. Edmondson(7), Kate Land(6)