arXiv subjects

Brendan O Connor

Publications and source records attributed to Brendan O Connor.

3 recordsLinked to original sources

arXiv2026

Detecting electromagnetic counterparts to LIGO/Virgo/KAGRA gravitational wave events with DECam: Neutron Star Mergers

With GW170817 being the only multimessenger gravitational wave (GW) event with an associated kilonova detected so far, there exists a pressing need for realistic estimation of the GW localization uncertainties and rates, as well as optimization of available telescope time to enable the detection of new kilonovae. We simulate GW events assuming a data-driven distribution of binary parameters for the LIGO/Virgo/KAGRA (LVK) fourth and fifth observing runs (O4 and O5). We map the binary neutron star (BNS) and neutron star-black hole (NSBH) properties to the kilonova optical light curves. We use the simulated population of kilonovae to generate follow-up observing plans, with the primary goal of optimizing detection with the Gravitational Wave Multi-Messenger Astronomy DECam Survey (GW-MMADS). We explore the dependence of kilonova detectability on the mass, distance, inclination, and spin of the binaries. Assuming that no BNS was detected during O4 until the end of 2024, we present updated GW BNS (NSBH) merger detection rates. We expect to detect BNS (NSBH) kilonovae with DECam at a per-year rate of: $0$-$2.0$ ($0$) in O4, and $2.0$-$19$ ($0$-$1.0$) in O5. We expect the majority of BNS detections and also those accompanied by a detectable kilonova to produce a hypermassive NS remnant, with a significant fraction of the remaining BNSs promptly collapsing to a BH. We release GW simulations and depths required to detect kilonovae based on our predictions to support the astronomical community in their multimessenger follow-up campaigns and analyses.

astro-ph.HE↗

arXiv2023

A re-emerging bright soft-X-ray state of the changing-look Active Galactic Nucleus 1ES~1927+654: a multi-wavelength view

1ES1927+654 is a nearby active galactic nucleus that has shown an enigmatic outburst in optical/UV followed by X-rays, exhibiting strange variability patterns at timescales of months-years. Here we report the unusual X-ray, UV, and radio variability of the source in its post-flare state (Jan 2022- May 2023). Firstly, we detect an increase in the soft X-ray (0.3-2 keV) flux from May 2022- May 2023 by almost a factor of five, which we call the bright-soft-state. The hard X-ray 2-10 keV flux increased by a factor of two, while the UV flux density did not show any significant changes ($\le 30\%$) in the same period. The integrated energy pumped into the soft and hard X-ray during this period of eleven months is $\sim 3.57\times 10^{50}$ erg and $5.9\times 10^{49}$ erg, respectively. From the energetics, it is evident that whatever is producing the soft excess (SE) is pumping out more energy than either the UV or hard X-ray source. Since the energy source presumably is ultimately the accretion of matter onto the SMBH, the SE emitting region must be receiving the majority of this energy. In addition, the source does not follow the typical disc-corona relation found in AGNs, neither in the initial flare (in 2017-2019) nor in the current bright soft state (2022-2023). We found that the core (<1 pc) radio emission at 5 GHz gradually increased till March 2022 but showed a dip in August 2022. The Güdel Benz relation ($L_{\rm radio}/L_{\rm X-ray}\sim 10^{-5}$), however, is still within the expected range for radio-quiet AGN and further follow-up radio observations are currently being undertaken.

astro-ph.HE↗

arXiv2021

A Neural Passage Model for Ad-hoc Document Retrieval

Traditional statistical retrieval models often treat each document as a whole. In many cases, however, a document is relevant to a query only because a small part of it contain the targeted information. In this work, we propose a neural passage model (NPM) that uses passage-level information to improve the performance of ad-hoc retrieval. Instead of using a single window to extract passages, our model automatically learns to weight passages with different granularities in the training process. We show that the passage-based document ranking paradigm from previous studies can be directly derived from our neural framework. Also, our experiments on a TREC collection showed that the NPM can significantly outperform the existing passage-based retrieval models.

cs.IR↗