MG-NECOLA: A Field-Level Emulator for $f(R)$ Gravity and Massive Neutrino Cosmologies
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| Format: | Preprint |
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2026
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| author | Orjuela-Quintana, J. Bayron Reyes, Mauricio Giusarma, Elena Baldi, Marco Kaushal, Neerav Valenzuela-Toledo, César A. |
| author_facet | Orjuela-Quintana, J. Bayron Reyes, Mauricio Giusarma, Elena Baldi, Marco Kaushal, Neerav Valenzuela-Toledo, César A. |
| contents | Accurate modeling of non-linear gravitational dynamics is essential for constraining extensions to the standard cosmological model using large-scale structure observations. While high-resolution $N$-body simulations provide the required fidelity, they are computationally prohibitive for the large ensembles needed to analyze Modified Gravity (MG) scenarios. We present MG-NECOLA, a field-level emulator based on a convolutional neural network that upgrades fast, approximate MG-PICOLA simulations to near--$N$-body accuracy at a fraction of the computational cost. Trained on a suite of QUIJOTE_MG simulations for $f(R)$ gravity, MG-NECOLA achieves nearly sub-percent accuracy ($\lesssim 1\%$) in both the matter power spectrum and bispectrum up to $k \simeq 1~h\,\mathrm{Mpc}^{-1}$. Crucially, although being trained on a fixed cosmology, the network generalizes robustly to cosmologies outside its training manifold keeping the error below $5\%$. It successfully recovers the General Relativity limit ($Λ$CDM) without introducing spurious MG signals and accurately captures the power suppression induced by massive neutrinos ($M_ν\leq 0.4$ eV), despite being trained on cosmologies with massless neutrinos. The pipeline delivers a speed-up factor of $\sim 1500\times$ relative to full $N$-body runs, generating a high-fidelity realization in O$(10^3)$ CPU seconds compared to O$(10^6)$ for the baseline. This accuracy-efficiency trade-off establishes MG-NECOLA as a powerful tool for generating the massive mock catalogs required for next-generation galaxy surveys. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2604_19613 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | MG-NECOLA: A Field-Level Emulator for $f(R)$ Gravity and Massive Neutrino Cosmologies Orjuela-Quintana, J. Bayron Reyes, Mauricio Giusarma, Elena Baldi, Marco Kaushal, Neerav Valenzuela-Toledo, César A. Cosmology and Nongalactic Astrophysics Accurate modeling of non-linear gravitational dynamics is essential for constraining extensions to the standard cosmological model using large-scale structure observations. While high-resolution $N$-body simulations provide the required fidelity, they are computationally prohibitive for the large ensembles needed to analyze Modified Gravity (MG) scenarios. We present MG-NECOLA, a field-level emulator based on a convolutional neural network that upgrades fast, approximate MG-PICOLA simulations to near--$N$-body accuracy at a fraction of the computational cost. Trained on a suite of QUIJOTE_MG simulations for $f(R)$ gravity, MG-NECOLA achieves nearly sub-percent accuracy ($\lesssim 1\%$) in both the matter power spectrum and bispectrum up to $k \simeq 1~h\,\mathrm{Mpc}^{-1}$. Crucially, although being trained on a fixed cosmology, the network generalizes robustly to cosmologies outside its training manifold keeping the error below $5\%$. It successfully recovers the General Relativity limit ($Λ$CDM) without introducing spurious MG signals and accurately captures the power suppression induced by massive neutrinos ($M_ν\leq 0.4$ eV), despite being trained on cosmologies with massless neutrinos. The pipeline delivers a speed-up factor of $\sim 1500\times$ relative to full $N$-body runs, generating a high-fidelity realization in O$(10^3)$ CPU seconds compared to O$(10^6)$ for the baseline. This accuracy-efficiency trade-off establishes MG-NECOLA as a powerful tool for generating the massive mock catalogs required for next-generation galaxy surveys. |
| title | MG-NECOLA: A Field-Level Emulator for $f(R)$ Gravity and Massive Neutrino Cosmologies |
| topic | Cosmology and Nongalactic Astrophysics |
| url | https://arxiv.org/abs/2604.19613 |