arXiv subjects

Luca Naso

Publications and source records attributed to Luca Naso.

4 recordsLinked to original sources

arXiv2025

Addressing prior dependence in hierarchical Bayesian modeling for PTA data analysis I: Methodology and implementation

Complex inference tasks, such as those encountered in Pulsar Timing Array (PTA) data analysis, rely on Bayesian frameworks. The high-dimensional parameter space and the strong interdependencies among astrophysical, pulsar noise, and nuisance parameters introduce significant challenges for efficient learning and robust inference. These challenges are emblematic of broader issues in decision science, where model over-parameterization and prior sensitivity can compromise both computational tractability and the reliability of the results. We address these issues in the framework of hierarchical Bayesian modeling by introducing a reparameterization strategy. Our approach employs Normalizing Flows (NFs) to decorrelate the parameters governing hierarchical priors from those of astrophysical interest. The use of NF-based mappings provides both the flexibility to realize the reparametrization and the tractability to preserve proper probability densities. We further adopt i-nessai, a flow-guided nested sampler, to accelerate exploration of complex posteriors. This unified use of NFs improves statistical robustness and computational efficiency, providing a principled methodology for addressing hierarchical Bayesian inference in PTA analysis.

astro-ph.IM↗

arXiv2025

Addressing prior dependence in hierarchical Bayesian modeling for PTA data analysis II: Noise and SGWB inference through parameter decorrelation

Pulsar Timing Arrays (PTA) provide a powerful framework to measure low-frequency gravitational waves, but accuracy and robustness of the results are challenged by complex noise processes that must be accurately modeled. Standard PTA analyses assign fixed uniform noise priors to each pulsar, an approach that can introduce systematic biases when combining the array. To overcome this limitation, we adopt a hierarchical Bayesian modeling strategy in which noise priors are parametrized by higher-level hyperparameters. To mitigate the sensitivity of the inferred parameters to the choice of noise hyperprior, we introduce a reparametrization of the hierarchical model based on the orthogonal projection of hyperparameters onto the physical parameter subspace. The transformation is implemented through Normalizing Flows (NFs), which provide an invertible, tractable representation and preserve shrinkage and inter-pulsar information pooling in the reparametrized model. We also employ i-nessai, a flow-guided nested sampler, to efficiently explore the resulting higher-dimensional parameter space. We apply our method to a minimal 3-pulsar case study, performing a simultaneous inference of noise and stochastic gravitational wave background (SGWB) parameters. Despite the limited dataset, the results consistently show that the reparametrized hierarchical treatment constrains the noise parameters more tightly and partially alleviates the red-noise-SGWB degeneracy, while the orthogonal reparametrization further enhances parameter independence without affecting the correlations intrinsic to the power-law modeling of the physical processes involved.

astro-ph.IM↗

arXiv2013

Magnetic fields in Accretion Discs around Neutron Stars - Consequences for the change of spin

Accretion disks are ubiquitous in the universe and it is generally accepted that magnetic fields play a pivotal role in accretion-disk physics. The spin history of millisecond pulsars, which are usually classified as magnetized neutron stars spun up by an accretion disk, depends sensitively on the magnetic field structure, and yet highly idealized models from the 80s are still being used for calculating the magnetic field components. We present a possible way of improving the currently used models with a semi-analytic approach. The resulting magnetic field profile of both the poloidal and the toroidal component can be very different from the one suggested previously. This might dramatically change our picture of which parts of the disk tend to spin the star up or down.

astro-ph.SR↗

arXiv2012

Modeling the Spatial Distribution of Neutron Stars in the Galaxy

In this paper we investigate the space and velocity distributions of old neutron stars (aged 109 to 1010 yr) in our Galaxy. Galactic old Neutron Stars (NSs) population fills a torus-like area extending to a few tens kiloparsecs above the galactic plane. The initial velocity distribution of NSs is not well known, in this work we adopt a three component initial distribution, as given by the contribution of kick velocities, circular velocities and Maxwellian velocities. For the spatial initial distribution we use a Gamma function. We then use Monte Carlo simulations to follow the evolution of the NSs under the influence of the Paczy¶nski Galactic gravitational potential. Our calculations show that NS orbits have a very large Galactic radial expansion and that their radial distribution peak is quite close to their progenitors' one. We also study the NS vertical distribution and find that it can well be described by a double exponential low. Finally, we investigate the correlation of the vertical and radial distribution and study the radial dependence of scale-heights.

astro-ph.HE↗