arXiv subjects

Airene Ahuja

Publications and source records attributed to Airene Ahuja.

3 recordsLinked to original sources

arXiv2026

Evidence for Enhancement in the Rate of Fast Radio Bursts Toward Galaxy Clusters

Massive galaxy clusters can introduce gravitational lensing and populations of suppressed star formation member galaxies into the line of sight, potentially changing the distribution of observable sources toward them from that of the average sky. As a result, Fast Radio Bursts (FRBs) aligned with galaxy clusters can provide a unique window into parameter spaces of the FRB population that other lines of sight do not afford. In this study, we demonstrate that the second CHIME/FRB baseband catalog contains FRBs emitted from within or behind clusters identified from the latest DECaLS data release; we isolate a sample of 26 FRBs where this is likely, including one repeating FRB and two FRBs that intersect the Coma cluster. Comparing our results against a range of simulated FRB populations, we conclude that the number of these associations represents a $3σ$ rate enhancement toward galaxy clusters, constituting $1.4\pm0.4\%$ of the FRBs detected in the second CHIME/FRB baseband catalog. We suggest that this enhancement is caused by an equal proportion of member galaxies hosting additional FRBs, and gravitational lensing magnifying background sources. We demonstrate that such contributions are sensitive to alternative progenitor channels and high redshift evolution in the FRB population, providing a future avenue for constraining these features. Considering the redshifts and masses of the associated clusters, we identify FRB 20211113A, which is aligned with the strong gravitational lens Abell 2218 as a potential lensed candidate for further consideration.

astro-ph.CO↗

arXiv2026

GSpyNetTree-O4: an event validation tool used in the fourth LIGO-Virgo-KAGRA observing run

The frequent presence of non-Gaussian transient noise, or glitches, in gravitational-wave detector data can affect gravitational-wave searches, parameter estimation, and downstream analyses. To identify and mitigate transient noise near gravitational-wave candidates in a timely manner, the LIGO-Virgo-KAGRA Collaboration employs the Data Quality Report. In the fourth observing run, GSpyNetTree-O4 was deployed within this framework as a tool for glitch classification and event validation. We describe GSpyNetTree-O4 and the main developments relative to its predecessor, GSpyNetTree. The most important update was a new architecture that allowed the simultaneous identification of glitches and gravitational-wave signals when both were present in the same input. We also expanded and augmented the training set with examples in which simulated gravitational-wave signals overlapped with real glitches, and applied $60\,\mathrm{Hz}$ calibration corrections to better match the data expected during the fourth observing run. On test data, the low-mass, high-mass, and extremely high-mass classifiers identified $97.9\%$, $97.7\%$, and $95.4\%$ of glitches, respectively. Among samples without a glitch, including gravitational-wave-only and No Glitch samples, the classifiers correctly reported no data-quality issues in $97.1\%$, $96.6\%$, and $96.0\%$ of cases, respectively. We further assessed the robustness of GSpyNetTree-O4 on unseen glitch morphologies, a small set of Virgo glitches from the fourth observing run, and different choices of the $Q$-value used to construct the time-frequency inputs. GSpyNetTree-O4 was successfully deployed as a Data Quality Report tool and increased automation in gravitational-wave event validation workflows.

gr-qc↗

arXiv2026

Rapid data quality investigations of gravitational-wave events with the Data Quality Report Builder toolkit

We present the Data Quality Report Builder toolkit, DQRbuild, a suite of data quality tools that have been developed to vet gravitational-wave events in preparation for the fourth LIGO-Virgo-KAGRA observing run. We explain the main functionality and the many scientific tests that we support. To validate the performance of the tools included in the toolkit, we run a series of tests on all significant candidates shared as public alerts in the third observing run to compare against what was manually reported using human intervention. We find that these automated tools can now identify 96% of the problems identified by humans during this previous observing run, with a 24% false alarm rate. We conclude with a commentary on the prospects and potential challenges for fully automating the process of vetting the data quality for gravitational-wave events identified in future observing runs.

astro-ph.IM↗