arXiv subjects

Andreas Tersenov

Publications and source records attributed to Andreas Tersenov.

3 recordsLinked to original sources

arXiv2026

Mitigating baryonic effects in weak lensing with higher-order statistics

Weak gravitational lensing is a premier cosmological probe, but its small-scale statistical power is compromised by baryonic feedback. Higher-order statistics capture non-Gaussian information that the power spectrum misses, yet their sensitivity to feedback remains a concern for Stage IV surveys. We quantify how unmodeled feedback biases the cosmological parameters inferred from the angular power spectrum (PS), starlet peak counts, and the starlet $\ell_1$-norm, and we determine the scale cuts needed to remove that bias. We also test the Bernardeau-Nishimichi-Taruya (BNT) transform as a strategy for more precise scale cuts. Our analysis is based on the cosmoGRID V1 suite, which imprints feedback on dark matter convergence maps with a baryon correction model, and on simulation-based inference with neural posterior estimation, carried out across footprints ranging from Stage III-like to the full sky. We find that biases grow with survey area, exceeding $2\sigma$ for all three statistics at Stage IV-like footprints, and removing them costs a substantial fraction of the signal. Restricted to these ``baryon-safe'' scales, the starlet $\ell_1$-norm still reaches a figure of merit almost twice that of the PS. The BNT transform localizes the baryonic sensitivity to the lowest transformed redshift bin and improves the PS figure of merit by a factor of $\sim$1.4, while its linear mixing of shape noise inflates the contours of the map-based higher-order statistics. Higher-order statistics therefore deliver a substantial gain over the power spectrum with no baryonic modeling at all, and an even larger one as modeling improves.

astro-ph.CO↗

arXiv2026

A plug-and-play approach with fast uncertainty quantification for weak lensing mass mapping

Upcoming stage-IV surveys such as Euclid and Rubin will deliver vast amounts of high-precision data, opening new opportunities to constrain cosmological models with unprecedented accuracy. A key step in this process is the reconstruction of the dark matter distribution from noisy weak-lensing shear measurements. Current deep-learning-based mass-mapping methods achieve high reconstruction accuracy, but either require retraining a model for each new observed sky region (limiting practicality) or rely on slow Markov chain Monte Carlo sampling. Efficient exploitation of future survey data therefore calls for a new method that is accurate, flexible, and fast at inference. In addition, an uncertainty quantification with coverage guarantees is essential for a reliable cosmological parameter estimation. We introduce PnPMass, a plug-and-play approach for weak-lensing mass mapping. The algorithm produces point estimates by alternating between a gradient descent step with a carefully chosen data fidelity term and a denoising step implemented with a single deep-learning model trained on simulated data corrupted by Gaussian white noise. We also propose a fast sampling-free uncertainty quantification scheme based on moment networks, with calibrated error bars obtained through conformal prediction to ensure coverage guarantees. Finally, we benchmark PnPMass against model-driven and data-driven mass-mapping techniques. PnPMass achieves a performance close to that of the currently best deep-learning methods while offering fast inference. It converges in just a few iterations, and it requires only a single training phase, regardless of the noise covariance of the observations. It therefore combines flexibility, efficiency, and reconstruction accuracy while delivering tighter error bars than existing approaches, making it well suited for upcoming weak-lensing surveys.

astro-ph.CO↗

arXiv2025

Impact of weak-lensing mass-mapping algorithms on cosmology inference

Weak-lensing mass-mapping algorithms, which reconstruct the convergence field from galaxy shear measurements, are crucial for extracting higher-order statistics to constrain cosmological parameters. However, only limited research has explored whether the choice of mass-mapping algorithm affects the inference of cosmological parameters from weak-lensing higher-order statistics. This study aims to evaluate the impact of different mass-mapping algorithms on the inference of cosmological parameters measured with weak-lensing peak counts. We employ Kaiser-Squires, inpainting Kaiser-Squires, and MCALens mass-mapping algorithms to reconstruct the convergence field from simulated weak-lensing data. Using these maps, we compute the peak counts and wavelet peak counts as data vectors and perform Bayesian analysis with MCMC sampling to estimate posterior distributions of cosmological parameters. Our results indicate that the choice of mass-mapping algorithm significantly affects the constraints on cosmological parameters, with the MCALens method improving constraints by up to 157$\%$ compared to the standard Kaiser-Squires method. This improvement arises from MCALens' ability to better capture small-scale structures. In contrast, inpainting Kaiser-Squires yields constraints similar to Kaiser-Squires, indicating a limited benefit from inpainting for cosmological parameter estimation with peaks. The accuracy of mass-mapping algorithms is thus critical for cosmological inference from weak-lensing data. Advanced algorithms like MCALens, which offer superior reconstruction of the convergence field, can substantially enhance the precision of cosmological parameter estimates. These findings underscore the importance of selecting appropriate mass-mapping techniques in weak-lensing studies to fully exploit the potential of higher-order statistics for cosmological research.

astro-ph.CO↗