arXiv subjects

Jibin He

Publications and source records attributed to Jibin He.

4 recordsLinked to original sources

arXiv2025

A fast deep-learning approach to probing primordial black hole populations in gravitational wave events

Primordial black holes (PBHs), envisioned as a compelling dark matter candidate and a window onto early-Universe physics, may contribute to some of the gravitational-wave (GW) signals detected by the LIGO-Virgo-KAGRA network. Traditional hierarchical Bayesian analysis, which relies on precise GW-event posterior estimates to extract information on potential PBH populations from GW events, becomes computationally demanding for catalogs with a large number of events. Here, we present a fast deep-learning framework, leveraging Transformer and normalizing flows, that maps GW-event posterior samples to joint posterior distributions over the hyperparameters of the PBH population. Our approach yields credible intervals with acceptable accuracy while delivering an order-of-magnitude speedup. These results highlight the potential of deep learning for fast and accurate PBH population studies, and its applicability to next-generation GW detectors when combined with appropriate event-level inference models.

gr-qc↗

arXiv2025

Search for primordial black holes from gravitational wave populations using deep learning

Gravitational waves (GWs) signals detected by the LIGO/Virgo/KAGRA collaboration might be sourced (partly) by the merges of primordial black holes (PBHs). The conventional hierarchical Bayesian inference methods can allow us to study population properties of GW events to search for the hints for PBHs. However, hierarchical Bayesian analysis require an analytic population model, and becomes increasingly computationally expensive as the number of sources grows. In this paper, we present a novel population analysis method based on deep learning, which enables the direct and efficient estimation of PBH population hyperparameters, such as the PBH fraction in dark matter, $f_{\rm PBH}$. Our approach leverages neural posterior estimation combined with conditional normalizing flows and two embedding networks. Our results demonstrate that inference can be performed within seconds, highlighting the promise of deep learning as a powerful tool for population inference with an increasing number of GW signals for next-generation detectors.

gr-qc↗

arXiv2024

Sub-Eddington accreting supermassive primordial black holes explain Little Red Dots

The James Webb Space Telescope (JWST) has uncovered an abundant population of compact, extremely red, and X-ray weak objects at $z\gtrsim4$, knows as ``Little Red Dots" (LRDs). These objects exhibit spectral energy distributions that resemble both active galactic nuclei (AGN) and stellar population templates. However, whether dominated by AGN activity or compact star formation, the high redshifts and masses/luminosities of LRDs, coupled with their significant abundance, present potential challenges to the standard $\Lambda$CDM model. In this work, we proposes a novel cosmic interpretation of this anomaly, suggesting that these LRDs are likely massive galaxies seeded by supermassive primordial black holes (SMPBHs) came into being in the very early universe. We analyze 434 known LRDs from the 0.54 ${\rm deg}^2$ COSMOS-Web survey and test the hypothesis that they originated from SMPBHs assuming sub-Eddington accretion. According to our result, SMPBHs actually could lead to the existence of more LRDs, even at higher redshifts ($z>8$).

astro-ph.GA↗

arXiv2024

Implications of GWTC-3 on primordial black holes from vacuum bubbles

The population of black holes inferred from the detection of gravitational waves by the LIGO-Virgo-KAGRA Collaboration has revealed interesting features in the properties of black holes in the Universe. We analyze the GWTC-3 dataset assuming the detected black holes in each event had an either astrophysical or primordial origin. In particular, we consider astrophysical black holes described by the fiducial \textsc{Power Law + Peak} distribution and primordial black holes whose mass function obeys a broken power law. These primordial black holes can be generated by vacuum bubbles that nucleate during inflation. We find that astrophysical black holes dominate the events with mass less than $\sim 30M_\odot$, whereas primordial black holes are responsible for the massive end, and also for the peak at $\sim 30M_\odot$ in the mass distribution. More than half of the observed events could come from primordial black hole mergers. We also discuss the implications on the primordial black hole formation mechanism and the underlying inflationary model.

astro-ph.CO↗