ForestFlow: predicting the Lyman-<i>α</i> forest clustering from linear to nonlinear scales

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 – massive neutrinos and curvature – 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.

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Publication Details

Journal
Astronomy and Astrophysics
Published
2025-01-10
DOI
https://doi.org/10.1051/0004-6361/202452039
Citations
1
Primary Topic
Soil Geostatistics and Mapping
Type
article
Field-Weighted Citation Impact
0.80

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article

ForestFlow: predicting the Lyman-α forest clustering from linear to nonlinear scales

D. Kirkby, D. Brooks, Paul Martini, A. Muñoz-Gutiérrez et al.
1 citations
Astronomy and Astrophysics
Soil Geostatistics and Mapping
0.80
article

ForestFlow: predicting the Lyman-α forest clustering from linear to nonlinear scales

D. Kirkby, D. Brooks, Paul Martini, A. Muñoz-Gutiérrez, Martine Lokken, Michael S. Schubnell, L. Cabayol-Garcia, S Ferraro, Graziano Rossi, Gregory Tarlé, Andreu Font-Ribera, David Sprayberry, Gustavo Niz, Ignasi Pérez-Ràfols, Axel de la Macorra, B. A. Weaver, M. Landriau, Marc Manera, Adam Lambert, J. Aguilar, Anthony Kremin, Eusebio Sanchez, Satya Gontcho A Gontcho, ENRIQUE GAZTAÑAGA, J. Chaves-Montero, J. E. Forero-Romero, Steven P. Ahlen, Davide Bianchi, R. Miquel, R. Kehoe, T. Claybaugh, S. Cole, Klaus Honscheid, G. Gutiérrez
article en
1 citations

Abstract

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 – massive neutrinos and curvature – 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.

Astronomy and AstrophysicsVol. 694
Boston University (US), Institució Catalana de Recerca i Estudis Avançats (ES), Universidad Complutense de Madrid (ES), Universitat Autònoma de Barcelona (ES), Universidad de Guanajuato (MX), Fermi National Accelerator Laboratory (US), Lawrence Berkeley National Laboratory (US), Universidad de Los Andes (CO), Southern Methodist University (US), Institute for High Energy Physics (ES), University of Milan (IT), Durham University (GB), University of California, Irvine (US), University of Michigan (US), Sejong University (KR), Institut d'Estudis Espacials de Catalunya (ES), Community Science and Data Center (US), Centro de Investigación en Materiales Avanzados (MX), NSF NOIRLab (US), Port d'Informació Científica (ES), Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (ES), University College London (GB), The Ohio State University (US), University of Portsmouth (GB), Universidad Nacional Autónoma de México (MX), University of California, Berkeley (US), Universitat Politècnica de Catalunya (ES)
National Science Foundation, U.S. Department of Energy, Gordon and Betty Moore Foundation, Centres de Recerca de Catalunya, European Commission, Generalitat de Catalunya, Ministerio de Ciencia e Innovación, Commissariat à l'Énergie Atomique et aux Énergies Alternatives, Institut de Física d'Altes Energies, Office of Science, Science and Technology Facilities Council, European Research Council, Division of Astronomical Sciences
Openalex Percentile: Top 33%
Soil Geostatistics and Mapping
0.80
DOIOpen Access PDFOpenAlex
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