0000000001287679
AUTHOR
Juan Calderón Bustillo
GW190521 as a Merger of Proca Stars: A Potential New Vector Boson of 8.7×10−13 eV
Advanced LIGO-Virgo have reported a short gravitational-wave signal (GW190521) interpreted as a quasicircular merger of black holes, one at least populating the pair-instability supernova gap, that formed a remnant black hole of ${M}_{f}\ensuremath{\sim}142\text{ }\text{ }{M}_{\ensuremath{\bigodot}}$ at a luminosity distance of ${d}_{L}\ensuremath{\sim}5.3\text{ }\text{ }\mathrm{Gpc}$. With barely visible pre-merger emission, however, GW190521 merits further investigation of the pre-merger dynamics and even of the very nature of the colliding objects. We show that GW190521 is consistent with numerically simulated signals from head-on collisions of two (equal mass and spin) horizonless vecto…
Confusing Head-On Collisions with Precessing Intermediate-Mass Binary Black Hole Mergers
We report a degeneracy between the gravitational-wave signals from quasi-circular precessing black-hole mergers and those from extremely eccentric mergers, namely head-on collisions. Performing model selection on numerically simulated signals of head-on collisions using models for quasi-circular binaries we find that, for signal-to-noise ratios of 15 and 25, typical of Advanced LIGO observations, head-on mergers with respective total masses of $M\in (125,300)M_\odot$ and $M\in (200,440)M_\odot$ would be identified as precessing quasi-circular intermediate-mass black hole binaries, located at a much larger distance. Ruling out the head-on scenario would require to perform model selection usi…
Convolutional Neural Networks for the classification of glitches in gravitational-wave data streams
We investigate the use of Convolutional Neural Networks (including the modern ConvNeXt network family) to classify transient noise signals (i.e.~glitches) and gravitational waves in data from the Advanced LIGO detectors. First, we use models with a supervised learning approach, both trained from scratch using the Gravity Spy dataset and employing transfer learning by fine-tuning pre-trained models in this dataset. Second, we also explore a self-supervised approach, pre-training models with automatically generated pseudo-labels. Our findings are very close to existing results for the same dataset, reaching values for the F1 score of 97.18% (94.15%) for the best supervised (self-supervised) m…