arXiv subjects

Weichangfeng Guo

Publications and source records attributed to Weichangfeng Guo.

6 recordsLinked to original sources

arXiv2026

MAGIKS: Multimodal Alignment and Gravitational-Wave-Conditioned Interaction for Kilonova Search

We present MAGIKS, a multimodal framework for gravitational-wave (GW)-triggered kilonova identification that formulates counterpart search as query-conditioned gallery retrieval. Following an align-before-fuse design, MAGIKS combines GW source parameters and sky-localization maps with irregularly sampled multiband optical light curves and sky coordinates. A time-aware supervised contrastive objective provides coarse cross-modal alignment, while dual cross-attention evaluates consistency between optical coordinates and GW localization, and between GW parameters and optical evolution. We train and evaluate MAGIKS on simulated binary neutron star and neutron star-black hole mergers, Rubin Observatory kilonova light curves from regular survey observations and target-of-opportunity follow-up, GW-detected mergers without Rubin-detectable kilonova counterparts, and realistic non-kilonova contaminants. In independent galleries with one true counterpart and time- and sky-compatible distractors, MAGIKS outperforms optical-only and sky-localization-only ranking, with increasing gains as galleries grow. At 5000 candidates, it ranks the counterpart first for 39.8% of queries, versus 4.8% and 0.2% for these baselines. In a GW170817 study, we generate counterfactual Rubin observations using released GW parameters and three-dimensional localization, the measured counterpart position, and host distance. At the same gallery size, MAGIKS achieves top-1 and top-10 retrieval rates of 87.3% and 95.7%. It correctly classifies 94.2% of associated and non-associated GW-optical pairs. In a separate crossed-pair experiment, its matching score responds significantly to controlled changes in all five tested GW parameters. These results support GW-conditioned kilonova candidate prioritisation for future multi-messenger follow-up.

astro-ph.HE↗

arXiv2026

A temporally clustered gravitational wave pair and a Type IIb supernova

Compact object mergers are generally expected long after the stellar collapse that forms their remnants. Here we report two marginal gravitational wave (GW) candidates, S190910d and S190910h, separated by 7.1 hr, whose three-dimensional localizations overlap at the position and distance of the Type IIb supernova SN 2019rsq. The GW pair lies in the extreme tail of the joint distribution of temporal separation and localization overlap, with a pairwise null probability of $7.6 \times 10^{-5}$ under our Monte Carlo null test. Independent re-analyses recover both as signal-like candidates. Under a common-origin hypothesis, S190910h remains consistent with a low-mass compact binary with source-frame masses near $2.5 + 1.3\,M_\odot$, whereas S190910d favours a strongly asymmetric $\sim 34 + 3\,M_\odot$ system whose observed amplitude is compatible with the supernova distance. SN 2019rsq is a rapidly evolving stripped-envelope Type IIb explosion whose light curve is consistent with stellar collapse occurring near the GW activity. Although the supernova coincidence alone is not statistically rare, its timing and physical properties provide a stellar collapse anchor for the unusual GW pair. A fragmenting collapsar disk provides a plausible framework that can accommodate the component masses, hour-scale delays and supernova properties, raising the possibility that multiple compact object mergers can accompany a single stellar collapse episode.

astro-ph.HE↗

arXiv2026

Searching for binary black hole mergers with deep learning in Advanced LIGO's third observing run

The detection of gravitational waves from compact binary coalescences has provided significant insights into our Universe, and the discovery of new and unique gravitational wave candidates from independent searches remains an ongoing field of research. In this work, we built a hybrid search pipeline that combines matched filtering and deep learning to identify stellar-mass binary black hole candidates from detector strain data. We first present results from a targeted injection study to benchmark the sensitivity of our method and compare it with existing search pipelines. We demonstrate that our hybrid approach has comparable sensitivity for injections with a source-frame chirp mass greater than 25$\,$M$_{\odot}$, and below this threshold our sensitivity drops off for signals with a network SNR less than 15. We also observe that our search method can identify a significant population of unique candidates. Furthermore, we conduct an offline search for gravitational wave candidates in the third observing run of the LIGO-Virgo-KAGRA Collaboration (LVK), yielding 31 candidates previously reported by the LVK with a probability of astrophysical origin $p_{\rm astro}\geq0.5$. We identify two other candidates: one previously reported only in a search conducted by the Institute for Advanced Study, and one previously unreported promising new candidate with a $p_{\rm astro}$ of 0.63. This unique candidate has a high chirp mass and a high probability that the primary black hole is an intermediate-mass black hole.

gr-qc↗

arXiv2024

Low-latency gravitational wave alert products and their performance at the time of the fourth LIGO-Virgo-KAGRA observing run

Multi-messenger searches for BNS and NSBH mergers are currently one of the most exciting areas of astronomy. The search for joint electromagnetic and neutrino counterparts to GWs has resumed with O4. To support this effort, public semi-automated data products are sent in near real-time and include localization and source properties to guide complementary observations. In preparation for O4, we have conducted a study using a simulated population of compact binaries and a MDC in the form of a real-time replay to optimize and profile the software infrastructure and scientific deliverables. End-to-end performance was tested, including data ingestion, running online search pipelines, performing annotations, and issuing alerts to the astrophysics community. We present an overview of the low-latency infrastructure and the performance of the data products that are now being released during O4 based on the MDC. We report the expected median latency for the preliminary alert of full bandwidth searches (29.5s) and show consistency and accuracy of released data products using the MDC. For the first time, we report the expected median latency for triggers from early warning searches (-3.1s), which are new in O4 and target neutron star mergers during inspiral phase. This paper provides a performance overview for LVK low-latency alert infrastructure and data products using the MDC and serves as a useful reference for the interpretation of O4 detections.

astro-ph.HE↗

arXiv2023

Applications of Deep Learning to physics workflows

Modern large-scale physics experiments create datasets with sizes and streaming rates that can exceed those from industry leaders such as Google Cloud and Netflix. Fully processing these datasets requires both sufficient compute power and efficient workflows. Recent advances in Machine Learning (ML) and Artificial Intelligence (AI) can either improve or replace existing domain-specific algorithms to increase workflow efficiency. Not only can these algorithms improve the physics performance of current algorithms, but they can often be executed more quickly, especially when run on coprocessors such as GPUs or FPGAs. In the winter of 2023, MIT hosted the Accelerating Physics with ML at MIT workshop, which brought together researchers from gravitational-wave physics, multi-messenger astrophysics, and particle physics to discuss and share current efforts to integrate ML tools into their workflows. The following white paper highlights examples of algorithms and computing frameworks discussed during this workshop and summarizes the expected computing needs for the immediate future of the involved fields.

hep-ex↗

arXiv2022

Mimicking Mergers: Mistaking Black Hole Captures as Mergers

As the number of gravitational wave observations has increased in recent years, the variety of sources has broadened. Here we investigate whether it is possible for the current generation of detectors to distinguish between very short-lived gravitational wave signals from mergers between high-mass black holes, and the signal produced by a close encounter between two black holes which results in gravitational capture, and ultimately a merger. We compare the posterior probability distributions produced by analysing simulated signals from both types of progenitor events, both under ideal and realistic scenarios. We show that while, under ideal conditions it is possible to distinguish both progenitors, under more realistic conditions they are indistinguishable. This has important implications for the interpretation of such short signals, and we therefore advocate that these signals be the focus of additional investigation even when satisfactory results have been achieved from standard analyses.

gr-qc↗