Matthew Ho
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    • Bye-bye, Local-in-matter-density Bias: The Statistics of the Halo Field Are Poorly Determined by the Local Mass Density
    • Metallicity Dependence of Pressure-regulated Feedback-modulated Star Formation in the TIGRESS-NCR Simulation Suite
    • LtU-ILI: An All-in-One Framework for Implicit Inference in Astrophysics and Cosmology
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    • Benchmarks and explanations for deep learning estimates of X-ray galaxy cluster masses
    • A Machine-learning Approach to Enhancing eROSITA Observations
    • The dynamical mass of the Coma cluster from deep learning
    • CLMM: a LSST-DESC cluster weak lensing mass modeling library for cosmology
    • Approximate Bayesian Uncertainties on Deep Learning Dynamical Mass Estimates of Galaxy Clusters
    • Aging haloes: implications of the magnitude gap on conditional statistics of stellar and gas properties of massive haloes
    • A Robust and Efficient Deep Learning Method for Dynamical Mass Measurements of Galaxy Clusters
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Agentic Systems for Calibrating Cosmological Simulations

Jan 1, 2026ยท
Chaipat Tirapongprasert
Matthew Ho
Matthew Ho
ยท 0 min read
Type
Conference paper
Publication
Machine Learning and the Physical Sciences 2026
Last updated on Jan 1, 2026
Matthew Ho
Authors
Matthew Ho
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← Simulation-based inference for direction reconstruction of ultrahigh-energy cosmic rays with radio arrays Mar 1, 2026
Celia: Catalog Embeddings Learned From Cosmological Simulations Jan 1, 2026 →

ยฉ 2026 Me. This work is licensed under CC BY NC ND 4.0 Photo: Thomas Robert Clarke / Institute for Advanced Study.

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