arXiv subjects

El Mehdi Zahraoui

Publications and source records attributed to El Mehdi Zahraoui.

5 recordsLinked to original sources

arXiv2026

Probing the magnetic field of a coronal mass ejection with PSR J1022+1001

We investigate whether low-frequency pulsar observations can provide LoS magnetic field estimates and whether these are consistent with synthetic LoS signatures extracted from a three-dimensional CME reconstruction constrained by Solar Orbiter data. We analyze a CME occultation of the LoS to PSR J1022+1001 on 20 August 2021 at a projected heliocentric distance of 24.6 R_sun, observed simultaneously with LOFAR and NenuFAR. From LOFAR, we derive time-resolved dispersion measure (DM) and rotation measure (RM) and isolate the CME contributions using background estimates for interstellar, solar wind and ionospheric components. We then infer the density-weighted LoS-averaged magnetic field component _PSR from the ratio delta-RM/delta-DM. In parallel, we reconstruct the CME using a semi-empirical 3DCORE model fitted to Solar Orbiter in-situ magnetic field observations at 0.65 au. We sample the modeled magnetic field along the pulsar LoS using fixed spatial sampling points and compute synthetic LoS-averaged signatures _3D for different flux rope configurations. The derived _PSR increases from approximately -9 nT to a peak near 63 nT during the observed interval. Comparison with synthetic signatures shows that the polarity and temporal evolution of the LoS signal are strongly dependent on the flux rope configuration and only a South-West-North (SWN) configuration (confirmed by Solar Orbiter in-situ data) reproduces the observed sign and overall evolution, whereas alternative configurations are incompatible. The modeled amplitudes, however, are systematically larger than the pulsar-derived values by roughly a factor of five. We show that simultaneous low-frequency pulsar DM and RM measurements can provide LoS magnetic field estimates for a CME and can be used to test CME magnetic structure against data-constrained three-dimensional reconstructions.

astro-ph.SR↗

arXiv2026

Insights into the Pulsar Timing Array hypothesis space

We present novel insights into the pulsar-noise model space in pulsar timing array (PTA) experiments. Through a comparative analysis of the same Parkes PTA second data release observations processed with the 2020 and 2023 pipelines, we show that data processing materially changes the distribution of posterior support across competing noise hypotheses and increases sensitivity to weak contributions such as the solar wind. A solar-wind component appears in the highest-posterior hypothesis for ten pulsars under the 2023 pipeline, against four under the 2020 pipeline, while the corresponding mean electron density estimates are consistent with PPTA DR3 results. Furthermore, hypothesis-space analysis exposes model competition and degeneracies that are hidden by single-model summaries. Approximately 75% of the pulsars retain substantial posterior support for alternative noise descriptions. Therefore, understanding the nature of the pulsar noise hypothesis space is crucial for robust inference and nanohertz gravitational-wave background searches.

astro-ph.HE↗

arXiv2026

Enhancing evidence estimation through informed probability density approximation

We introduce the Morph approximation, a class of product approximations of probability densities that selects low-order disjoint parameter blocks by maximizing the sum of their total correlations. We use the posterior approximation via Morph as the importance distribution in optimal bridge sampling. We denote this procedure by MorphZ, which serves as a post-processing estimator of the marginal likelihood. The MorphZ estimator requires only posterior samples, and is fully agnostic regarding the choice of sampler. We evaluate MorphZ's performance across statistical benchmarks, pulsar timing array (PTA) models, compact binary coalescence (CBC) gravitational-wave (GW) simulations and the GW150914 event. Across these applications, spanning low to high dimensionalities, MorphZ yields accurate evidence estimates at substantially reduced computational cost relative to standard approaches. We have found that when these approaches fail to provide accurate estimates, MorphZ has proven to either resolve the estimation failure or significantly improve the results. Its bridge sampling relative error diagnostic provides conservative uncertainty estimates. Because MorphZ operates directly on posterior draws, it complements exploration-oriented samplers by enabling fast and reliable evidence estimation, while it can be seamlessly integrated into existing inference workflows.

astro-ph.IM↗

arXiv2025

Generalized Steppingstone Sampling: Efficient marginal likelihood estimation in gravitational wave analysis of Pulsar Timing Array data

Globally, Pulsar Timing Array (PTA) experiments have revealed evidence supporting an existing gravitational wave background (GWB) signal in the PTA data set. Apart from acquiring more observations, the sensitivity of PTA experiments can be increased by improving the accuracy of the noise modeling. In PTA data analysis, noise modeling is conducted primarily using Bayesian statistics, relying on the marginal likelihood and the Bayes factor to assess the evidence. We introduce generalized steppingstone (GSS) as an efficient and accurate marginal likelihood estimation method for the PTA-Bayesian framework. This method enables low-cost estimates with high accuracy, especially when comparing expensive models such as the Hellings-Downs (HD) model or the overlap reduction function model (ORF). We demonstrate the efficiency and the accuracy of GSS for model selection and evidence calculation by reevaluating the evidence of previous analyses from the North American Nanohertz Observatory for Gravitational Waves (NANOGrav) 15 yr data set and the European PTA (EPTA) second data release. We find similar evidence for the GWB compared to the one reported by the NANOGrav 15-year data set. Compared to the evidence reported for the EPTA second data release, we find a substantial increase in evidence supporting GWB across all data sets.

astro-ph.IM↗

arXiv2025

LIGO Core-Collapse Supernova Detection using Convolution Neural Networks

Core-Collapse Supernovae (CCSNe) remain a critical focus in the search for gravitational waves (GWs) in modern astronomy. Their detection and subsequent analysis will enhance our understanding of the explosion mechanisms in massive stars. This paper investigates a combination of time-frequency analysis tools with convolutional neural network (CNN) to enhance the detection of GWs originating from CCSNe. The CNN was trained on simulated CCSNe signals and Advanced LIGO (aLIGO) noise in two instances, using spectrograms computed from two time-frequency transformations: the short-time Fourier transform (STFT) and the Q-transform. The algorithm detects CCSNe signals based on their time-frequency spectrograms. Our CNN model achieves a near 100% true positive rate for CCSNe GW events with a signal-to-noise ratio (SNR) greater than 0.5 in our test set. We also found that the STFT outperforms the Q-transform for SNRs below 0.5.

astro-ph.IM↗