Data-Driven Population Inference from Gravitational-Wave Sources and Electromagnetic Counterparts

Gravitational-wave (GW) astronomy has presented an unprecedented way to view the universe and study populations of astrophysical objects such as merging compact binaries containing black holes (BHs) and neutron stars (NSs). With the latest catalog of observations detected by the Advanced LIGO-Virgo detector network, recent analyses are placing interesting constraints on the population of BHs and NSs in these binaries. In particular, we are learning a great deal about how these binaries are distributed as a function of their masses. Another aspect of GW astronomy that has the potential to provide insights into fundamental physics is the multi-messenger follow up of the potential kilonova from binary mergers involving NSs. Observations or non-detections of kilonovae can be used to learn more about the formation of heavy elements via r-process nucleosynthesis as well as to shed light on the inner mechanisms of such mergers. This dissertation presents two studies that focus on inferring population properties from compact binaries using data-driven methods. The first is using the flexible approach of Gaussian processes to model the mass distribution of compact binaries and the second is developing a hierarchical Bayesian inference framework to infer kilonova population properties using non-detections in electromagnetic surveys

Non-parametric inference of the population of compact binaries from gravitational wave observations using binned Gaussian processes

The observation of gravitational waves from multiple compact binary coalescences by the LIGO-Virgo-KAGRA detector networks has enabled us to infer the underlying distribution of compact binaries across a wide range of masses, spins, and redshifts. In light of the new features found in the mass spectrum of binary black holes and the uncertainty regarding binary formation models, non-parametric population inference has become increasingly popular. In this work, we develop a data-driven clustering framework that can identify features in the component mass distribution of compact binaries simultaneously with those in the corresponding redshift distribution, from gravitational wave data in the presence of significant measurement uncertainties, while making very few assumptions on the functional form of these distributions. Our generalized model is capable of inferring correlations among various population properties such as the redshift evolution of the shape of the mass distribution itself, in contrast to most existing non-parametric inference schemes. We test our model on simulated data and demonstrate the accuracy with which it can re-construct the underlying distributions of component masses and redshifts. We also re-analyze public LIGO-Virgo-KAGRA data from events in GWTC-3 using our model and compare our results with those from some alternative parametric and non-parametric population inference approaches. Finally, we investigate the potential presence of correlations between mass and redshift in the population of binary black holes in GWTC-3 (those observed by the LIGO-Virgo-KAGRA detector network in their first 3 observing runs), without making any assumptions about the specific nature of these correlations.Comment: Upload accepted versio

A self-consistent method to estimate the rate of compact binary coalescences with a Poisson mixture model

The recently published GWTC-1 (Abbott B P et al (LIGO Scientific Collaboration and Virgo Collaboration) 2019 Phys. Rev. X 9 031040)—a journal article summarizing the search for gravitational waves (GWs) from coalescing compact binaries in data produced by the LIGO-Virgo network of ground-based detectors during their first and second observing runs—quoted estimates for the rates of binary neutron star, neutron star black hole binary, and binary black hole mergers, as well as assigned probabilities of astrophysical origin for various significant and marginal GW candidate events. In this paper, we delineate the formalism used to compute these rates and probabilities, which assumes that triggers above a low ranking statistic threshold, whether of terrestrial or astrophysical origin, occur as independent Poisson processes. In particular, we include an arbitrary number of astrophysical categories by redistributing, via mass-based template weighting, the foreground probabilities of candidate events, across source classes. We evaluate this formalism on synthetic GW data, and demonstrate that this method works well for the kind of GW signals observed during the first and second observing runs

A self-consistent method to estimate the rate of compact binary coalescences with a Poisson mixture model

The recently published GWTC-1 - a journal article summarizing the search for gravitational waves (GWs) from coalescing compact binaries in data produced by the LIGO-Virgo network of ground-based detectors during their first and second observing runs - quoted estimates for the rates of binary neutron star, neutron star black hole binary, and binary black hole mergers, as well as assigned probabilities of astrophysical origin for various significant and marginal GW candidate events. In this paper, we delineate the formalism used to compute these rates and probabilities, which assumes that triggers above a low ranking statistic threshold, whether of terrestrial or astrophysical origin, occur as independent Poisson processes. In particular, we include an arbitrary number of astrophysical categories by redistributing, via mass-based template weighting, the foreground probabilities of candidate events, across source classes. We evaluate this formalism on synthetic GW data, and demonstrate that this method works well for the kind of GW signals observed during the first and second observing runs.Comment: 19 pages, 5 figure

A self-consistent method to estimate the rate of compact binary coalescences with a Poisson mixture model

The recently published GWTC-1 - a journal article summarizing the search for gravitational waves (GWs) from coalescing compact binaries in data produced by the LIGO-Virgo network of ground-based detectors during their first and second observing runs - quoted estimates for the rates of binary neutron star, neutron star black hole binary, and binary black hole mergers, as well as assigned probabilities of astrophysical origin for various significant and marginal GW candidate events. In this paper, we delineate the formalism used to compute these rates and probabilities, which assumes that triggers above a low ranking statistic threshold, whether of terrestrial or astrophysical origin, occur as independent Poisson processes. In particular, we include an arbitrary number of astrophysical categories by redistributing, via mass-based template weighting, the foreground probabilities of candidate events, across source classes. We evaluate this formalism on synthetic GW data, and demonstrate that this method works well for the kind of GW signals observed during the first and second observing runs.Comment: 19 pages, 5 figure

An Early-warning System for Electromagnetic Follow-up of Gravitational-wave Events

Binary neutron stars (BNSs) will spend ≃10–15 minutes in the band of Advanced Laser Interferometer Gravitational-Wave Observatory (LIGO) and Virgo detectors at design sensitivity. Matched-filtering of gravitational-wave (GW) data could in principle accumulate enough signal-to-noise ratio (S/N) to identify a forthcoming event tens of seconds before the companions collide and merge. Here we report on the design and testing of an early-warning GW detection pipeline. Early-warning alerts can be produced for sources that are at low enough redshift so that a large enough S/N accumulates ~10–60 s before merger. We find that about 7% (49%) of the total detectable BNS mergers will be detected 60 s (10 s) before the merger. About 2% of the total detectable BNS mergers will be detected before merger and localized to within 100 deg² (90% credible interval). Coordinated observing by several wide-field telescopes could capture the event seconds before or after the merger. LIGO–Virgo detectors at design sensitivity could facilitate observing at least one event at the onset of merger