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Search for gravitational waves from Scorpius X-1 with a hidden Markov model in O3 LIGO data
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Investigating the mixing between two black hole populations in LIGO-Virgo-KAGRA GWTC-3
Here, we introduce a population model to analyze the mixing between hypothesised power-law and ~ 35M ⊙ Gaussian bump black hole populations in the latest gravitational wave catalog, GWTC 3, estimating their co-location and separation. We find a relatively low level of mixing, $3.1^{+5.0}_{-3.1}$%, between the power-law and Gaussian populations, compared to the percentage of mergers containing two Gaussian bump black holes, $5.0^{+3.2}_{-1.7}$%. Our analysis indicates that black holes within the Gaussian bump are generally separate from the power-law population, with only a minor fraction engaging in mixing and contributing to the $\mathcal{M}$ ~ 14M ⊙ peak in the chirp mass. This leads us to identify a distinct population of Binary Gaussian Black Holes (BGBHs) that arise from mergers within the Gaussian bump. We suggest that current theories for the formation of the massive 35M ⊙ Gaussian bump population may need to reevaluate the underlying mechanisms that drive the preference for BGBHs.
Implications for First-Order Cosmological Phase Transitions from the Third LIGO-Virgo Observing Run
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Scalable Variational Gaussian Processes for Crowdsourcing: Glitch Detection in LIGO
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Inference-Optimized AI and High Performance Computing for Gravitational Wave Detection at Scale
We introduce an ensemble of artificial intelligence models for gravitational wave detection that we trained in the Summit supercomputer using 32 nodes, equivalent to 192 NVIDIA V100 GPUs, within 2 h. Once fully trained, we optimized these models for accelerated inference using NVIDIA TensorRT. We deployed our inference-optimized AI ensemble in the ThetaGPU supercomputer at Argonne Leadership Computer Facility to conduct distributed inference. Using the entire ThetaGPU supercomputer, consisting of 20 nodes each of which has 8 NVIDIA A100 Tensor Core GPUs and 2 AMD Rome CPUs, our NVIDIA TensorRT-optimized AI ensemble processed an entire month of advanced LIGO data (including Hanford and Livingston data streams) within 50 s. Our inference-optimized AI ensemble retains the same sensitivity of traditional AI models, namely, it identifies all known binary black hole mergers previously identified in this advanced LIGO dataset and reports no misclassifications, while also providing a 3X inference speedup compared to traditional artificial intelligence models. We used time slides to quantify the performance of our AI ensemble to process up to 5 years worth of advanced LIGO data. In this synthetically enhanced dataset, our AI ensemble reports an average of one misclassification for every month of searched advanced LIGO data. We also present the receiver operating characteristic curve of our AI ensemble using this 5 year long advanced LIGO dataset. This approach provides the required tools to conduct accelerated, AI-driven gravitational wave detection at scale.
Gravitational waves from supercooled phase transitions in conformal Majoron models of neutrino mass
We study supercooled first-order phase transitions above the QCD scale in a wide class of conformal Majoron-like U(1)′ models that explain the totality of active neutrino oscillation data and produce a detectable stochastic gravitational wave background (SGWB) at LIGO, LISA and ET. We place constraints on the U(1)′ breaking scale and gauge coupling using current LIGO-Virgo-Kagra data. We find that strong supercooling can be ruled out in large regions of parameter space if a SGWB is not detected by these experiments. A null signal at LIGO and ET will disfavor a type-I seesaw scale above 10 14 GeV, while a positive signal is a signature of heavy right-handed neutrinos. On the other hand, LISA will be sensitive to seesaw scales as low as a TeV, and could detect a SGWB even if the right-handed neutrinos are decoupled.
Optical follow-up of gravitational wave triggers with DECam during the first two LIGO/VIRGO observing runs
Gravitational wave (GW) events detectable by LIGO and Virgo have several possible progenitors, including black hole mergers, neutron star mergers, black hole–neutron star mergers, supernovae, and cosmic string cusps. A subset of GW events is expected to produce electromagnetic (EM) emission that, once detected, will provide complementary information about their astrophysical context. To that end, the LIGO–Virgo Collaboration (LVC) sends GW candidate alerts to the astronomical community so that searches for their EM counterparts can be pursued. The DESGW group, consisting of members of the Dark Energy Survey (DES), the LVC, and other members of the astronomical community, uses the Dark Energy Camera (DECam) to perform a search and discovery program for optical signatures of LVC GW events. DESGW aims to use a sample of GW events as standard sirens for cosmology. Due to the short decay timescale of the expected EM counterparts and the need to quickly eliminate survey areas with no counterpart candidates, it is critical to complete the initial analysis of each night’s images as quickly as possible. We discuss our search area determination, imaging pipeline, and candidate selection processes. We review results from the DESGW program during the first two LIGO–Virgo observing campaigns and introduce other science applications that our pipeline enables.