Publications

Matthew T. Scoggins, Matthew Ho, Francisco Villaescusa-Navarro, Drew Jamieson, Ludvig Doeser, Greg L. Bryan (2025). Learning the Universe: $3\ h^{-1}{\rm Gpc}$ Tests of a Field Level $N$-body Simulation Emulator. arXiv e-prints.

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Chirag Modi, Shivam Pandey, Matthew Ho, ChangHoon Hahn, Bruno Régaldo-Saint Blancard, Benjamin Wandelt (2025). Sensitivity analysis of simulation-based inference for galaxy clustering. Monthly Notices of the Royal Astronomical Society.

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Deaglan J. Bartlett, Matthew Ho, Benjamin D. Wandelt (2024). Bye-bye, Local-in-matter-density Bias: The Statistics of the Halo Field Are Poorly Determined by the Local Mass Density. The Astrophysical Journal Letters.

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Chang-Goo Kim, Eve C. Ostriker, Jeong-Gyu Kim, Munan Gong, Greg L. Bryan, Drummond B. Fielding, Sultan Hassan, Matthew Ho, Sarah M. R. Jeffreson, Rachel S. Somerville, Ulrich P. Steinwandel (2024). Metallicity Dependence of Pressure-regulated Feedback-modulated Star Formation in the TIGRESS-NCR Simulation Suite. The Astrophysical Journal.

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Matthew Ho, Deaglan J. Bartlett, Nicolas Chartier, Carolina Cuesta-Lazaro, Simon Ding, Axel Lapel, Pablo Lemos, Christopher C. Lovell, T. Lucas Makinen, Chirag Modi, Viraj Pandya, Shivam Pandey, Lucia A. Perez, Benjamin Wandelt, Greg L. Bryan (2024). LtU-ILI: An All-in-One Framework for Implicit Inference in Astrophysics and Cosmology. The Open Journal of Astrophysics.

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M. Ntampaka, Matthew Ho, B. Nord (2024). Building Trustworthy Machine Learning Models for Astronomy. Astronomical Data Analysis Software and Systems XXXI.

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Ronan Legin, Matthew Ho, Pablo Lemos, Laurence Perreault-Levasseur, Shirley Ho, Yashar Hezaveh, Benjamin Wandelt (2024). Posterior sampling of the initial conditions of the universe from non-linear large scale structures using score-based generative models. Monthly Notices of the Royal Astronomical Society.

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Antoine Bourdin, Ronan Legin, Matthew Ho, Alexandre Adam, Yashar Hezaveh, Laurence Perreault-Levasseur (2024). Inpainting Galaxy Counts onto N-Body Simulations over Multiple Cosmologies and Astrophysics. ICML 2024 AI for Science Workshop.

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D. Huppenkothen, M. Ntampaka, Matthew Ho, M. Fouesneau, B. Nord, J. E. G. Peek, M. Walmsley, J. F. Wu, C. Avestruz, T. Buck, M. Brescia, D. P. Finkbeiner, A. D. Goulding, T. Kacprzak, P. Melchior, M. Pasquato, N. Ramachandra, Y.-S. Ting, G. Van De Ven, S. Villar, V. A. Villar, E. Zinger (2023). Constructing Impactful Machine Learning Research for Astronomy: Best Practices for Researchers and Reviewers. arXiv e-prints.

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Matthew Ho, John Soltis, Arya Farahi, Daisuke Nagai, August Evrard, Michelle Ntampaka (2023). Benchmarks and explanations for deep learning estimates of X-ray galaxy cluster masses. Monthly Notices of the Royal Astronomical Society.

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John Soltis, Michelle Ntampaka, John F. Wu, John ZuHone, August Evrard, Arya Farahi, Matthew Ho, Daisuke Nagai (2022). A Machine-learning Approach to Enhancing eROSITA Observations. The Astrophysical Journal.

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Matthew Ho, Michelle Ntampaka, Markus Michael Rau, Minghan Chen, Alexa Lansberry, Faith Ruehle, Hy Trac (2022). The dynamical mass of the Coma cluster from deep learning. Nature Astronomy.

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M. Aguena, C. Avestruz, C. Combet, S. Fu, R. Herbonnet, A. I. Malz, M. Penna-Lima, M. Ricci, S. D. P. Vitenti, L. Baumont, H. Fan, M. Fong, Matthew Ho, M. Kirby, C. Payerne, D. Boutigny, B. Lee, B. Liu, T. McClintock, H. Miyatake, C. Sifón, A. Von Der Linden, H. Wu, M. Yoon, LSST Dark Energy Science Collaboration (2021). CLMM: a LSST-DESC cluster weak lensing mass modeling library for cosmology. Monthly Notices of the Royal Astronomical Society.

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Matthew Ho, Arya Farahi, Markus Michael Rau, Hy Trac (2021). Approximate Bayesian Uncertainties on Deep Learning Dynamical Mass Estimates of Galaxy Clusters. The Astrophysical Journal.

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Arya Farahi, Matthew Ho, Hy Trac (2020). Aging haloes: implications of the magnitude gap on conditional statistics of stellar and gas properties of massive haloes. Monthly Notices of the Royal Astronomical Society.

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Matthew Ho, Markus Michael Rau, Michelle Ntampaka, Arya Farahi, Hy Trac, Barnabás Póczos (2019). A Robust and Efficient Deep Learning Method for Dynamical Mass Measurements of Galaxy Clusters. The Astrophysical Journal.

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