arXiv subjects

Shreyas Tiruvaskar

Publications and source records attributed to Shreyas Tiruvaskar.

4 recordsLinked to original sources

arXiv2026

Bayesian leave-one-out cross-validation for astrophysical model comparison using gravitational-wave background data

Previous work showed that ultralight-dark-matter solitons can provide dynamical friction for supermassive black-hole binaries, suppressing low-frequency power in the pulsar-timing-array gravitational-wave background and constraining the particle mass and effective ultralight-dark-matter fraction. Here we extend that analysis by comparing the predictive performance of four models: simplified and realistic ultralight-dark-matter implementations, a phenomenological environmental-hardening model, and a gravitational-wave-only model. We use Bayesian leave-one-out cross-validation on the five lowest pulsar-timing-array frequency bins. The phenomenological model gives the largest expected log predictive density, but its advantage over the other models is not large compared with the estimated standard errors. The current data therefore do not decisively prefer one model overall. The clearest pairwise result is within the ultralight-dark-matter framework: the simplified model outperforms the realistic implementation in all five frequency bins. Current pulsar-timing-array data are therefore compatible with ultralight-dark-matter-induced low-frequency suppression, but do not yet distinguish ultralight-dark-matter significantly from more generic environmental descriptions of supermassive-black-hole-binary evolution.

astro-ph.CO↗

arXiv2026

Replacing Gaussian Processes with Neural Networks in Pulsar Timing Array Inference of the Gravitational-Wave Background

Bayesian inference of nanohertz gravitational-wave background models in pulsar timing array analyses often relies on Gaussian-process interpolators to avoid repeated, computationally expensive strain-spectrum calculations. However, Gaussian-process training becomes a bottleneck for large training sets. We test whether probabilistic neural networks can replace Gaussian processes in this role for both a self-interacting dark matter model and a phenomenological environmental model. We find that neural networks recover consistent posteriors while significantly reducing both training and Markov chain Monte Carlo runtime, with the largest gains for the more computationally demanding model.

astro-ph.CO↗

arXiv2025

Ultralight Dark Matter Constraints from NanoHertz Gravitational Waves

We investigate the impact of ultralight dark matter (ULDM) on the mergers of supermassive black holes (SMBH) and the resulting stochastic gravitational wave background. ULDM is based on exceptionally light particles and yields galactic halos with dense central solitons. This increases the drag experienced by binary SMBH, decreasing merger times and potentially suppressing gravitational radiation from the binary at low frequencies. We develop semi-analytic models for the decay of SMBH binaries in ULDM halos and use current pulsar timing array (PTA) measurements to constrain the ULDM particle mass and its fractional contribution to the dark matter content of the universe. We find a median ULDM particle mass of $7. \times 10^{-22}$ eV and show that scaling relations suggest that the drag remains effective at relatively low ULDM fractions, which are consistent with all other constraints on the model. Consequently, future pulsar timing measurements will be a sensitive probe of any ULDM contribution to the overall dark matter content of the universe.

astro-ph.CO↗

arXiv2025

Self-Interacting Dark-Matter Spikes and the Final-Parsec Problem: Bayesian constraints from the NANOGrav 15-Year Gravitational-Wave Background

A self-interacting dark-matter (SIDM) density spike around merging supermassive black holes (SMBHs) may be able to supply the dynamical friction needed to shrink binaries from $\sim 1\, \mathrm{pc}$ to $\sim 10^{-2} \,\mathrm{pc}$, thereby resolving the long-standing "final-parsec problem". Embedding the binary-halo system in a cosmological population model, we evolve the inspiral under the combined influence of gravitational-wave (GW) emission and SIDM drag, compute the resulting nanohertz GW background, and confront it with the NANOGrav 15-year pulsar-timing data. A six-parameter Bayesian analysis, performed with a Gaussian-process-accelerated Markov chain Monte Carlo, yields posterior constraints on the cross-section per unit mass and maximum circular velocity values that were consistent with independent galaxy-rotation and cluster-lensing limits. Within this parameter space, the SIDM spike remains intact, supplies sufficient friction to overcome the stellar depletion barrier, and produces a characteristic-strain spectrum that matches the NANOGrav signal as well as phenomenological astrophysical models.

astro-ph.CO↗