Papers › ForestFlow: predicting the Lyman-α forest clustering from linear to nonlinear scales
ForestFlow: predicting the Lyman-α forest clustering from linear to nonlinear scales
J. Chaves-Montero, L. Cabayol-Garcia, M. Lokken, A. Font-Ribera, J. Aguilar, S. Ahlen, D. Bianchi, D. Brooks, T. Claybaugh, S. Cole, A. de la Macorra, S. Ferraro, J. E. Forero-Romero, E. Gaztañaga, S. Gontcho A Gontcho, G. Gutierrez, K. Honscheid, R. Kehoe, D. Kirkby, A. Kremin, A. Lambert, M. Landriau, M. Manera, P. Martini, R. Miquel, A. Muñoz-Gutiérrez, G. Niz, I. Pérez-Ràfols, G. Rossi, E. Sanchez, M. Schubnell, D. Sprayberry, G. Tarlé, B. A. Weaver
The archive published only this paper's code-link row. Authors, date and abstract are from arXiv's metadata (CC0), read from the Kaggle arXiv metadata snapshot of 2026-09-12 where its title matched the archive's; the title is the archive's.
On large scales, the Lyman-α forest provides insights into the expansion history of the Universe, while on small scales, it imposes strict constraints on the growth history, the nature of dark matter, and the sum of neutrino masses. This work introduces ForestFlow, a novel framework that bridges the gap between large- and small-scale analyses, which have traditionally relied on distinct modeling approaches. Using conditional normalizing flows, ForestFlow predicts the two Lyman-α linear biases (b_δ and b_η) and six parameters describing small-scale deviations of the three-dimensional flux power spectrum (P_(3D)) from linear theory as a function of cosmology and intergalactic medium physics. These are then combined with a Boltzmann solver to make consistent predictions, from arbitrarily large scales down to the nonlinear regime, for P_(3D) and any other statistics derived from it. Trained on a suite of 30 fixed-and-paired cosmological hydrodynamical simulations spanning redshifts from z=2 to 4.5, ForestFlow achieves 3 and 1.5\% precision in describing P_(3D) and the one-dimensional flux power spectrum (P_(1D)) from linear scales to k=5 Mpc⁻¹ and k_∥=4 Mpc⁻¹, respectively. Thanks to its conditional parameterization, ForestFlow shows similar performance for ionization histories and two ΛCDM model extensions x2013 massive neutrinos and curvature x2013 even though none of these are included in the training set. This framework will enable full-scale cosmological analyses of Lyman-α forest measurements from the DESI survey.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections