| The camels multifield data set: Learning the universe’s fundamental parameters with artificial intelligence F Villaescusa-Navarro, S Genel, D Angles-Alcazar, L Thiele, R Dave, ... The Astrophysical Journal Supplement Series 259 (2), 61, 2022 | 73 | 2022 |
| The CAMELS project: public data release F Villaescusa-Navarro, S Genel, D Anglés-Alcázar, LA Perez, ... The Astrophysical Journal Supplement Series 265 (2), 54, 2023 | 43 | 2023 |
| Disentangling magnification in combined shear-clustering analyses L Thiele, CAJ Duncan, D Alonso Monthly Notices of the Royal Astronomical Society 491 (2), 1746-1758, 2020 | 39 | 2020 |
| Accurate analytic model for the weak lensing convergence one-point probability distribution function and its autocovariance L Thiele, JC Hill, KM Smith Physical Review D 102 (12), 123545, 2020 | 34 | 2020 |
| Massive vector fields in Kerr-Newman and Kerr-Sen black hole spacetimes R Cayuso, OJC Dias, F Gray, D Kubizňák, A Margalit, JE Santos, ... Journal of High Energy Physics 2020 (4), 1-26, 2020 | 26 | 2020 |
| Can small-scale baryon inhomogeneities resolve the Hubble tension? An investigation with ACT DR4 L Thiele, Y Guan, JC Hill, A Kosowsky, DN Spergel Physical Review D 104 (6), 063535, 2021 | 25 | 2021 |
| The SZ flux-mass (Y–M) relation at low-halo masses: improvements with symbolic regression and strong constraints on baryonic feedback D Wadekar, L Thiele, JC Hill, S Pandey, F Villaescusa-Navarro, ... Monthly Notices of the Royal Astronomical Society 522 (2), 2628-2643, 2023 | 22 | 2023 |
| Multifield cosmology with artificial intelligence F Villaescusa-Navarro, D Anglés-Alcázar, S Genel, DN Spergel, Y Li, ... arXiv preprint arXiv:2109.09747, 2021 | 21 | 2021 |
| Predicting the impact of feedback on matter clustering with machine learning in CAMELS AM Delgado, D Anglés-Alcázar, L Thiele, S Pandey, K Lehman, ... Monthly Notices of the Royal Astronomical Society 526 (4), 5306-5325, 2023 | 20 | 2023 |
| Augmenting astrophysical scaling relations with machine learning: Application to reducing the Sunyaev–Zeldovich flux–mass scatter D Wadekar, L Thiele, F Villaescusa-Navarro, JC Hill, M Cranmer, ... Proceedings of the National Academy of Sciences 120 (12), e2202074120, 2023 | 19 | 2023 |
| Teaching Neural Networks to Generate Fast Sunyaev–Zel’dovich Maps L Thiele, F Villaescusa-Navarro, DN Spergel, D Nelson, A Pillepich The Astrophysical Journal 902 (2), 129, 2020 | 19 | 2020 |
| Percent-level constraints on baryonic feedback with spectral distortion measurements L Thiele, D Wadekar, JC Hill, N Battaglia, J Chluba, ... Physical Review D 105 (8), 083505, 2022 | 17 | 2022 |
| Accurate analytic model for the thermal Sunyaev-Zel’dovich one-point probability distribution function L Thiele, JC Hill, KM Smith Physical Review D 99 (10), 103511, 2019 | 17 | 2019 |
| Cosmology from weak lensing peaks and minima with Subaru Hyper Suprime-Cam Survey first-year data GA Marques, J Liu, M Shirasaki, L Thiele, D Grandón, KM Huffenberger, ... Monthly Notices of the Royal Astronomical Society 528 (3), 4513-4527, 2024 | 15 | 2024 |
| BISOU: a balloon project to measure the CMB spectral distortions B Maffei, MH Abitbol, N Aghanim, J Aumont, E Battistelli, J Chluba, ... The Sixteenth Marcel Grossmann Meeting on Recent Developments in Theoretical …, 2023 | 14 | 2023 |
| Robust marginalization of baryonic effects for cosmological inference at the field level F Villaescusa-Navarro, S Genel, D Angles-Alcazar, DN Spergel, Y Li, ... arXiv preprint arXiv:2109.10360, 2021 | 13 | 2021 |
| Cosmological constraints from the Subaru Hyper Suprime-Cam year 1 shear catalogue lensing convergence probability distribution function L Thiele, GA Marques, J Liu, M Shirasaki Physical Review D 108 (12), 123526, 2023 | 12 | 2023 |
| Principal tensor strikes again: separability of vector equations with torsion R Cayuso, F Gray, D Kubizňák, A Margalit, RG Souza, L Thiele Physics Letters B 795, 650-656, 2019 | 12 | 2019 |
| An exploration of the properties of cluster profiles for the thermal and kinetic Sunyaev–Zel’dovich effects BKK Lee, WR Coulton, L Thiele, S Ho Monthly Notices of the Royal Astronomical Society 517 (1), 420-436, 2022 | 10 | 2022 |
| Predicting the thermal Sunyaev–Zel’dovich field using modular and equivariant set-based neural networks L Thiele, M Cranmer, W Coulton, S Ho, DN Spergel Machine Learning: Science and Technology 3 (3), 035002, 2022 | 7 | 2022 |
