arXiv subjects

Sébastien Pierre

Publications and source records attributed to Sébastien Pierre.

4 recordsLinked to original sources

arXiv2026

$B$-sure. Part II. Scattering transforms as robustness test for tensor-to-scalar ratio detection from CMB observations

Galactic foregrounds represent a major contamination to the measurement of primordial $B$-modes from observations of the Cosmic Microwave Background polarisation. Even after the application of component separation algorithms, foreground residuals may potentially still bias the estimate of the tensor-to-scalar ratio $r$, causing a false detection. In this work, we present the methodology of a robustness test for the validation of an eventual detection of primordial $B$-modes, as obtained by a future, LiteBIRD-like satellite experiment. The goal of the test is to identify the foreground residuals contamination by looking for non-Gaussian properties in the CMB $B$-modes map, recovered through blind component separation algorithms. We adopt scattering transforms (ST) as our summary statistics sensitive to the non-Gaussian features of foreground residuals and to their correlation with foregrounds tracer maps. We characterise and validate the methodology on realistic sky simulations with different levels of foregrounds complexity. The proposed test is able to identify a bias on the tensor-to-scalar ratio of $\gtrsim 10^{-3}$ in $\sim 90\%$ of our simulations, with this bias value being of the same order of the accuracy targeted by LiteBIRD. Additionally, for our particular experimental configuration, the test is passed when the bias is lower than the sensitivity on the $r$ parameter, and no warning is raised. These results provide an important step forward in the development of statistical tools for validating future measurement of cosmological parameters, against foregrounds contamination.

astro-ph.CO↗

arXiv2026

Bayesian imaging inverse problem with scattering transform

Bayesian imaging inverse problems in astrophysics and cosmology remain challenging, particularly in low-data regimes, due to complex forward operators and the frequent lack of well-motivated priors for non-Gaussian signals. In this paper, we introduce a Bayesian approach that addresses these difficulties by relying on a low-dimensional representation of physical fields built from Scattering Transform statistics. This representation enables inference to be performed in a compact model space, where we recover a posterior distribution over signal models that are consistent with the observed data. We propose an iterative adaptive algorithm to efficiently approximate this posterior distribution. We apply our method to a large-scale structure column density field from the Quijote simulations, using a realistic instrumental forward operator. We demonstrate both accurate statistical inference and deterministic signal reconstruction from a single contaminated image, without relying on any external prior distribution for the field of interest. These results demonstrate that Scattering Transform statistics provide an effective representation for solving complex imaging inverse problems in challenging low-data regimes. Our approach opens the way to new applications for non-Gaussian astrophysical and cosmological signals for which little or no prior modeling is available.

astro-ph.IM↗

arXiv2025

Mitigating Model Misspecification in Simulation-Based Inference for Galaxy Clustering

Simulation-based inference (SBI) has become an important tool in cosmology for extracting additional information from observational data using simulations. However, all cosmological simulations are approximations of the actual universe, and SBI methods can be sensitive to model misspecification - particularly when the observational data lie outside the support of the training distribution. We present a method to improve the robustness of cosmological analyses under such conditions. Our approach first identifies and discards components of the summary statistics that exhibit inconsistency across related simulators, then learns a transformation that brings the observation back within the support of the training distribution. We apply our method in the context of a recent SimBIG SBI galaxy clustering analysis using the wavelet scattering transform (WST) summary statistic. The original analysis struggled to produce robust constraints for certain subsets of WST coefficients, where the observational data appeared out-of-distribution (OOD) relative to the training data. We show that our method enables robust cosmological inference and resolves OOD issues, while preserving most of the constraining power. In particular, the improved SimBIG WST analysis yields $Λ$CDM constraints of $Ω_m = 0.32^{+0.02}_{-0.02}$ and $σ_8 = 0.80^{+0.02}_{-0.02}$, which are respectively $1.4\times$ and $3.1\times$ tighter than those from a standard perturbation-theory-based power spectrum analysis, confirming the significant information gain of WST summary statistics. The proposed method is easily applicable to other cosmological SBI contexts and represents a step toward more robust SBI pipelines.

astro-ph.CO↗

arXiv2023

Content Censorship in the InterPlanetary File System

The InterPlanetary File System (IPFS) is currently the largest decentralized storage solution in operation, with thousands of active participants and millions of daily content transfers. IPFS is used as remote data storage for numerous blockchain-based smart contracts, Non-Fungible Tokens (NFT), and decentralized applications. We present a content censorship attack that can be executed with minimal effort and cost, and that prevents the retrieval of any chosen content in the IPFS network. The attack exploits a conceptual issue in a core component of IPFS, the Kademlia Distributed Hash Table (DHT), which is used to resolve content IDs to peer addresses. We provide efficient detection and mitigation mechanisms for this vulnerability. Our mechanisms achieve a 99.6\% detection rate and mitigate 100\% of the detected attacks with minimal signaling and computational overhead. We followed responsible disclosure procedures, and our countermeasures are scheduled for deployment in the future versions of IPFS.

cs.CR↗