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The first successful detection of gravitational waves by ground-based observatories, such as the Laser Interferometer Gravitational-Wave Observatory (LIGO), marked a revolutionary breakthrough in our comprehension of the Universe. However, due to the unprecedented sensitivity required to make such observations, gravitational-wave detectors also capture disruptive noise sources called glitches, potentially masking or appearing as gravitational wave signals themselves. To address this problem, a community-science project, Gravity Spy, incorporates human insight and machine learning to classify glitches in LIGO data. The machine learning classifier, integrated into the project since 2017, has evolved over time to accommodate increasing numbers of glitch classes. Despite its success, limitations have arisen in the ongoing LIGO fourth observing run (O4) due to its architecture’s simplicity, which led to poor generalization and inability to handle multi-time window inputs effectively. We propose an advanced classifier for O4 glitches. Our contributions include evaluating fusion strategies for multi-time window inputs, using label smoothing to counter noisy labels, and enhancing interpretability through attention module-generated weights. This development seeks to enhance glitch classification, aiding in the ongoing exploration of gravitational-wave phenomena. 

ABSTRACT Detailed radiative transfer simulations of kilonova spectra play an essential role in multimessenger astrophysics. Using the simulation results in parameter inference studies requires building a surrogate model from the simulation outputs to use in algorithms requiring sampling. In this work, we present kilonovanet, an implementation of conditional variational autoencoders (cVAEs) for the construction of surrogate models of kilonova spectra. This method can be trained on spectra directly, removing overhead time of pre-processing spectra, and greatly speeds up parameter inference time. We build surrogate models of three state-of-the-art kilonova simulation data sets and present in-depth surrogate error evaluation methods, which can in general be applied to any surrogate construction method. By creating synthetic photometric observations from the spectral surrogate, we perform parameter inference for the observed light-curve data of GW170817 and compare the results with previous analyses. Given the speed with which kilonovanet performs during parameter inference, it will serve as a useful tool in future gravitational wave observing runs to quickly analyse potential kilonova candidates. 

All ten LIGO/Virgo binary black hole (BH-BH) coalescences reported following the O1/O2 runs have near-zero effective spins. There are only three potential explanations for this. If the BH spin magnitudes are large, then: (i) either both BH spin vectors must be nearly in the orbital plane or (ii) the spin angular momenta of the BHs must be oppositely directed and similar in magnitude. Then there is also the possibility that (iii) the BH spin magnitudes are small. We consider the third hypothesis within the framework of the classical isolated binary evolution scenario of the BH-BH merger formation. We test three models of angular momentum transport in massive stars: a mildly efficient transport by meridional currents (as employed in the Geneva code), an efficient transport by the Tayler-Spruit magnetic dynamo (as implemented in the MESA code), and a very-efficient transport (as proposed by Fuller et al.) to calculate natal BH spins. We allow for binary evolution to increase the BH spins through accretion and account for the potential spin-up of stars through tidal interactions. Additionally, we update the calculations of the stellar-origin BH masses, including revisions to the history of star formation and to the chemical evolution across cosmic time. We find that we can simultaneously match the observed BH-BH merger rate density and BH masses and BH-BH effective spins. Models with efficient angular momentum transport are favored. The updated stellar-mass weighted gas-phase metallicity evolution now used in our models appears to be key for obtaining an improved reproduction of the LIGO/Virgo merger rate estimate. Mass losses during the pair-instability pulsation supernova phase are likely to be overestimated if the merger GW170729 hosts a BH more massive than 50  M ⊙ . We also estimate rates of black hole-neutron star (BH-NS) mergers from recent LIGO/Virgo observations. If, in fact. angular momentum transport in massive stars is efficient, then any (electromagnetic or gravitational wave) observation of a rapidly spinning BH would indicate either a very effective tidal spin up of the progenitor star (homogeneous evolution, high-mass X-ray binary formation through case A mass transfer, or a spin- up of a Wolf-Rayet star in a close binary by a close companion), significant mass accretion by the hole, or a BH formation through the merger of two or more BHs (in a dense stellar cluster). 

The magnetar SGR 1935+2154 is the only known Galactic source of fast radio bursts (FRBs). FRBs from SGR 1935+2154 were first detected by the Canadian Hydrogen Intensity Mapping Experiment (CHIME)/FRB and the Survey for Transient Astronomical Radio Emission 2 in 2020 April, after the conclusion of the LIGO, Virgo, and KAGRA Collaborations’ O3 observing run. Here, we analyze four periods of gravitational wave (GW) data from the GEO600 detector coincident with four periods of FRB activity detected by CHIME/FRB, as well as X-ray glitches and X-ray bursts detected by NICER and NuSTAR close to the time of one of the FRBs. We do not detect any significant GW emission from any of the events. Instead, using a short-duration GW search (for bursts ≤1 s) we derive 50% (90%) upper limits of 10⁴⁸(10⁴⁹) erg for GWs at 300 Hz and 10⁴⁹(10⁵⁰) erg at 2 kHz, and constrain the GW-to-radio energy ratio to ≤10¹⁴−10¹⁶. We also derive upper limits from a long-duration search for bursts with durations between 1 and 10 s. These represent the strictest upper limits on concurrent GW emission from FRBs.