M. Zevin

ILLUMINATING BLACK HOLE BINARY FORMATION CHANNELS with SPINS in ADVANCED LIGO

The recent detections of the binary black hole mergers GW150914 and GW151226 have inaugurated the field of gravitational-wave astronomy. For the two main formation channels that have been proposed for these sources, isolated binary evolution in galactic fields and dynamical formation in dense star clusters, the predicted masses and merger rates overlap significantly, complicating any astrophysical claims that rely on measured masses alone. Here, we examine the distribution of spin–orbit misalignments expected for binaries from the field and from dense star clusters. Under standard assumptions for black hole natal kicks, we find that black hole binaries similar to GW150914 could be formed with significant spin–orbit misalignment only through dynamical processes. In particular, these heavy-black hole binaries can only form with a significant spin–orbit anti-alignment in the dynamical channel. Our results suggest that future detections of merging black hole binaries with measurable spins will allow us to identify the main formation channel for these systems.

Constraining Formation Models of Binary Black Holes with Gravitational-wave Observations

Gravitational waves (GWs) from binary black hole (BBH) mergers provide a new probe of massive-star evolution and the formation channels of binary compact objects. By coupling the growing sample of BBH systems with population synthesis models, we can begin to constrain the parameters of such models and glean unprecedented knowledge about the inherent physical processes that underpin binary stellar evolution. In this study, we apply a hierarchical Bayesian model to mass measurements from a synthetic GW sample to constrain the physical prescriptions in population models and the relative fraction of systems generated from various channels. We employ population models of two canonical formation scenarios in our analysis --- isolated binary evolution involving a common-envelope phase and dynamical formation within globular clusters --- with model variations for different black hole natal kick prescriptions. We show that solely with chirp mass measurements, it is possible to constrain natal kick prescriptions and the relative fraction of systems originating from each formation channel with

Gravity Spy: Integrating Advanced LIGO Detector Characterization, Machine Learning, and Citizen Science

\mathcal{O}(100)

ASTROPHYSICAL PRIOR INFORMATION AND GRAVITATIONAL-WAVE PARAMETER ESTIMATION

of confident detections. This framework can be extended to include additional formation scenarios, model parameters, and measured properties of the compact binary.

Improvements in Gravitational-Wave Sky Localization with Expanded Networks of Interferometers

With the first direct detection of gravitational waves, the advanced laser interferometer gravitational-wave observatory (LIGO) has initiated a new field of astronomy by providing an alternative means of sensing the universe. The extreme sensitivity required to make such detections is achieved through exquisite isolation of all sensitive components of LIGO from non-gravitational-wave disturbances. Nonetheless, LIGO is still susceptible to a variety of instrumental and environmental sources of noise that contaminate the data. Of particular concern are noise features known as glitches, which are transient and non-Gaussian in their nature, and occur at a high enough rate so that accidental coincidence between the two LIGO detectors is non-negligible. Glitches come in a wide range of time-frequency-amplitude morphologies, with new morphologies appearing as the detector evolves. Since they can obscure or mimic true gravitational-wave signals, a robust characterization of glitches is paramount in the effort to achieve the gravitational-wave detection rates that are predicted by the design sensitivity of LIGO. This proves a daunting task for members of the LIGO Scientific Collaboration alone due to the sheer amount of data. In this paper we describe an innovative project that combines crowdsourcing with machine learning to aid in the challenging task of categorizing all of the glitches recorded by the LIGO detectors. Through the Zooniverse platform, we engage and recruit volunteers from the public to categorize images of time-frequency representations of glitches into pre-identified morphological classes and to discover new classes that appear as the detectors evolve. In addition, machine learning algorithms are used to categorize images after being trained on human-classified examples of the morphological classes. Leveraging the strengths of both classification methods, we create a combined method with the aim of improving the efficiency and accuracy of each individual classifier. The resulting classification and characterization should help LIGO scientists to identify causes of glitches and subsequently eliminate them from the data or the detector entirely, thereby improving the rate and accuracy of gravitational-wave observations. We demonstrate these methods using a small subset of data from LIGOs first observing run.

Deep multi-view models for glitch classification

The detection of electromagnetic counterparts to gravitational waves (GWs) has great promise for the investigation of many scientific questions. While it is well known that certain orientation parameters can reduce uncertainty in other related parameters, it was also hoped that the detection of an electromagnetic signal in conjunction with a GW could augment the measurement precision of the mass and spin from the gravitational signal itself. That is, knowledge of the sky location, inclination, and redshift of a binary could break degeneracies between these extrinsic, coordinate-dependent parameters and the physical parameters that are intrinsic to the binary. In this paper, we investigate this issue by assuming perfect knowledge of extrinsic parameters, and assessing the maximal impact of this knowledge on our ability to extract intrinsic parameters. We recover similar gains in extrinsic recovery to earlier work; however, we find only modest improvements in a few intrinsic parameters—namely the primary components spin. We thus conclude that, even in the best case, the use of additional information from electromagnetic observations does not improve the measurement of the intrinsic parameters significantly.

Direct: Deep Discriminative Embedding for Clustering of Ligo Data.

A milestone of multi-messenger astronomy has been achieved with the detection of gravitational waves from a binary neutron star merger accompanied by observations of several associated electromagnetic counterparts. Joint observations can reveal details of the engines that drive the electromagnetic and gravitational-wave emission. However, locating and identify an electromagnetic counterparts to a gravitational-wave event is heavily reliant on localization of the source through gravitational-wave information. We explore the sky localization of a simulated set of neutron star mergers as the worldwide network of gravitational-wave detectors evolves through the next decade, performing the first such study for neutron star -- black hole binary sources. Currently, three detectors are observing with additional detectors in Japan and India expected to become operational in the coming years. With three detectors, we recover a median neutron star -- black hole binary sky localization of 60 deg

Eccentric Black Hole Mergers in Dense Star Clusters: The Role of Binary-Binary Encounters

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Machine learning for Gravity Spy: Glitch classification and dataset

at the 90\% credible level. As all five detectors become operational, sources can be localized to a median of 11 deg

A Framework for Reconstructing the Complete Evolutionary History of Low-Mass X-ray Binaries

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