MG-NECOLA: Fast Neural Emulators for Modified Gravity Cosmologies
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908606848303104 |
|---|---|
| author | Orjuela-Quintana, J. Bayron Reyes, Mauricio Giusarma, Elena Villaescusa-Navarro, Francisco Kaushal, Neerav Valenzuela-Toledo, César A. |
| author_facet | Orjuela-Quintana, J. Bayron Reyes, Mauricio Giusarma, Elena Villaescusa-Navarro, Francisco Kaushal, Neerav Valenzuela-Toledo, César A. |
| contents | Observations of the large-scale structure (LSS) provide a powerful test of gravity on cosmological scales, but high-resolution N-body simulations of modified gravity (MG) are prohibitively expensive. We present MG-NECOLA, a convolutional neural network that enhances fast MG-PICOLA simulations to near-N-body fidelity at a fraction of the cost. MG-NECOLA reproduces QUIJOTE-MG N-body results in the power spectrum and bispectrum with better than 1% accuracy down to non-linear scales ($k \simeq 1~h~\mathrm{Mpc}^{-1}$), while reducing computational time by several orders of magnitude. Importantly, although trained only on $f(R)$ models with massless neutrinos, the network generalizes robustly to scenarios with massive neutrinos, preserving accuracy to within 5% at non-linear scales. This combination of precision and robustness establishes MG-NECOLA as a practical emulator for producing large ensembles of high-fidelity simulations, enabling efficient exploration of modified gravity and beyond-$Λ$CDM cosmologies in upcoming surveys. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_20086 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MG-NECOLA: Fast Neural Emulators for Modified Gravity Cosmologies Orjuela-Quintana, J. Bayron Reyes, Mauricio Giusarma, Elena Villaescusa-Navarro, Francisco Kaushal, Neerav Valenzuela-Toledo, César A. Cosmology and Nongalactic Astrophysics Observations of the large-scale structure (LSS) provide a powerful test of gravity on cosmological scales, but high-resolution N-body simulations of modified gravity (MG) are prohibitively expensive. We present MG-NECOLA, a convolutional neural network that enhances fast MG-PICOLA simulations to near-N-body fidelity at a fraction of the cost. MG-NECOLA reproduces QUIJOTE-MG N-body results in the power spectrum and bispectrum with better than 1% accuracy down to non-linear scales ($k \simeq 1~h~\mathrm{Mpc}^{-1}$), while reducing computational time by several orders of magnitude. Importantly, although trained only on $f(R)$ models with massless neutrinos, the network generalizes robustly to scenarios with massive neutrinos, preserving accuracy to within 5% at non-linear scales. This combination of precision and robustness establishes MG-NECOLA as a practical emulator for producing large ensembles of high-fidelity simulations, enabling efficient exploration of modified gravity and beyond-$Λ$CDM cosmologies in upcoming surveys. |
| title | MG-NECOLA: Fast Neural Emulators for Modified Gravity Cosmologies |
| topic | Cosmology and Nongalactic Astrophysics |
| url | https://arxiv.org/abs/2510.20086 |