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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2025
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| Online Access: | https://arxiv.org/abs/2507.00351 |
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| _version_ | 1866911125676752896 |
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| author | Bluck, Asa F. L. Piotrowska, Joanna M. Goubert, Paul Maiolino, Roberto Casimiro, Camilo Franco, Thomas Pinto Cea, Nicolas |
| author_facet | Bluck, Asa F. L. Piotrowska, Joanna M. Goubert, Paul Maiolino, Roberto Casimiro, Camilo Franco, Thomas Pinto Cea, Nicolas |
| contents | We present Dark from Light (DfL) - a novel method to infer the dark sector in wide-field galaxy surveys, leveraging a machine learning approach trained on contemporary cosmological simulations. The aim of this algorithm is to provide a fast, straightforward, and accurate route to estimating dark matter halo masses and group membership in wide-field spectroscopic galaxy surveys. This approach requires a highly limited number of input parameters and yields full probability distribution functions for the output halo masses. To achieve this, we train a series of Random Forest (RF) regression models on the IllustrisTNG and EAGLE simulations at z=0-3, which provide model-dependent mappings from luminous tracers to dark matter halo properties. We incorporate the individual regression models into a virial group-finding algorithm (DfL), which outputs halo properties for observational-like input data. We test the method at z=0-2 for both the EAGLE and IllustrisTNG models, as well as in a cross-validation mode. We demonstrate that known halo masses can be recovered with a mean systematic bias of $\langle b \rangle = \pm 0.10\,$dex (resulting from simulation choice), a mean statistical uncertainty of $\langle σ\rangle = 0.12 \,$dex across epochs, and a central - (core) satellite classification accuracy of 96%. We establish that this approach yields superior halo mass recovery to standard abundance matching applied to groups identified through a friends-of-friends algorithm. Additionally, we compare the outputs of DfL to observational constraints on the $M_* - M_{\rm Halo}$ relation from strong gravitational lensing at $z \sim 0$, demonstrating the promise of this novel approach. Finally, we systematically quantify how DfL performs on observational-like input data with varying stellar mass uncertainty and spectroscopic incompleteness, enabling robust error calibration. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_00351 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Dark from light (DfL): Inferring halo properties from luminous tracers with machine learning trained on cosmological simulations. I. Method, proof of concept & preliminary testing Bluck, Asa F. L. Piotrowska, Joanna M. Goubert, Paul Maiolino, Roberto Casimiro, Camilo Franco, Thomas Pinto Cea, Nicolas Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies Instrumentation and Methods for Astrophysics We present Dark from Light (DfL) - a novel method to infer the dark sector in wide-field galaxy surveys, leveraging a machine learning approach trained on contemporary cosmological simulations. The aim of this algorithm is to provide a fast, straightforward, and accurate route to estimating dark matter halo masses and group membership in wide-field spectroscopic galaxy surveys. This approach requires a highly limited number of input parameters and yields full probability distribution functions for the output halo masses. To achieve this, we train a series of Random Forest (RF) regression models on the IllustrisTNG and EAGLE simulations at z=0-3, which provide model-dependent mappings from luminous tracers to dark matter halo properties. We incorporate the individual regression models into a virial group-finding algorithm (DfL), which outputs halo properties for observational-like input data. We test the method at z=0-2 for both the EAGLE and IllustrisTNG models, as well as in a cross-validation mode. We demonstrate that known halo masses can be recovered with a mean systematic bias of $\langle b \rangle = \pm 0.10\,$dex (resulting from simulation choice), a mean statistical uncertainty of $\langle σ\rangle = 0.12 \,$dex across epochs, and a central - (core) satellite classification accuracy of 96%. We establish that this approach yields superior halo mass recovery to standard abundance matching applied to groups identified through a friends-of-friends algorithm. Additionally, we compare the outputs of DfL to observational constraints on the $M_* - M_{\rm Halo}$ relation from strong gravitational lensing at $z \sim 0$, demonstrating the promise of this novel approach. Finally, we systematically quantify how DfL performs on observational-like input data with varying stellar mass uncertainty and spectroscopic incompleteness, enabling robust error calibration. |
| title | Dark from light (DfL): Inferring halo properties from luminous tracers with machine learning trained on cosmological simulations. I. Method, proof of concept & preliminary testing |
| topic | Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies Instrumentation and Methods for Astrophysics |
| url | https://arxiv.org/abs/2507.00351 |