Constraining Primordial Magnetic Fields with Weak Lensing
Abstract
Introducing primordial magnetic fields (PMFs) into the standard cosmological model, ΛCDM, has an impact on the matter power spectrum at the linear level, resulting in an increase of power on small scales. In this work, we use the enhanced spectrum as the initial condition for dark-matter-only N-body simulations and study its non-linear evolution. From the resulting non-linear matter power spectrum, we calculate the convergence power spectrum of weak lensing (Cκ) and show how the presence of PMFs causes an enhancement at high multipoles. We then construct an emulator for Cκ that also includes intrinsic alignment and baryon feedback. We perform MCMC analyses using mock data generated for different PMF models and for three values of the maximum multipole, ℓmax=5000, $10000$, and $20000$. The PMFs models are characterized by the two parameters that define the power-law power spectrum of the magnetic field PB(k)∝ B1Mpc2 knB, where B1Mpc is the amplitude at a scale of $1$ Mpc and nB is the power spectral index. For ΛCDM mocks, corresponding to B1Mpc=0, we obtain approximate upper limits of B1Mpc<1.6, $1.0$, and 0.8 nG at the 95% confidence level for the three values of ℓmax respectively. For non-zero input magnetic fields, the recovery of B1Mpc and nB depends strongly on the PMFs model and on the maximum multipole considered. Models with nB=−2.1 are more easily constrained than models with nB=−2.7, since they produce a stronger enhancement of the convergence power spectrum. We find no clear degeneracy between the PMFs parameters and the intrinsic alignment or baryon feedback parameters, while B1Mpc and nB can be degenerate. We characterize this degeneracy through linear fits to selected $2$D posterior distributions.
Related
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BibTeX
@misc{dambrosio2026constraining,
title = {{Constraining Primordial Magnetic Fields with Weak Lensing}},
author = {Anna D'Ambrosio and Matteo Viel},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.00485},
eprint = {2610.00485},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.00485},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Constraining Primordial Magnetic Fields with Weak Lensing
AU - Anna D'Ambrosio
AU - Matteo Viel
PY - 2026
DA - 2026/10/01
PB - arXiv
DO - 10.48550/arxiv.2610.00485
UR - https://arxiv.org/abs/2610.00485
AB - Introducing primordial magnetic fields (PMFs) into the standard cosmological model, ΛCDM, has an impact on the matter power spectrum at the linear level, resulting in an increase of power on small scales. In this work, we use the enhanced spectrum as the initial condition for dark-matter-only N-body simulations and study its non-linear evolution. From the resulting non-linear matter power spectrum, we calculate the convergence power spectrum of weak lensing (C_κ) and show how the presence of PMFs causes an enhancement at high multipoles. We then construct an emulator for C_κ that also includes intrinsic alignment and baryon feedback. We perform MCMC analyses using mock data generated for different PMF models and for three values of the maximum multipole, ℓ_max=5000, $10000$, and $20000$. The PMFs models are characterized by the two parameters that define the power-law power spectrum of the magnetic field P_B(k)∝ B_1Mpc^2 k^n_B, where B_1Mpc is the amplitude at a scale of $1$ Mpc and n_B is the power spectral index. For ΛCDM mocks, corresponding to B_1Mpc=0, we obtain approximate upper limits of B_1Mpc<1.6, $1.0$, and 0.8 nG at the 95% confidence level for the three values of ℓ_max respectively. For non-zero input magnetic fields, the recovery of B_1Mpc and n_B depends strongly on the PMFs model and on the maximum multipole considered. Models with n_B=−2.1 are more easily constrained than models with n_B=−2.7, since they produce a stronger enhancement of the convergence power spectrum. We find no clear degeneracy between the PMFs parameters and the intrinsic alignment or baryon feedback parameters, while B_1Mpc and n_B can be degenerate. We characterize this degeneracy through linear fits to selected $2$D posterior distributions.
ER -