Matthew Abernathy

Bio: Matthew Abernathy is an academic researcher from American University. The author has contributed to research in topics: Gravitational wave & LIGO. The author has an hindex of 42, co-authored 76 publications receiving 17886 citations. Previous affiliations of Matthew Abernathy include Johns Hopkins University Applied Physics Laboratory & United States Naval Research Laboratory.

Search for Gravitational Waves Associated with Gamma-Ray Bursts During the First Advanced LIGO Observing Run and Implications for the Origin of GRB 150906B

Abstract: We present the results of the search for gravitational waves (GWs) associated with $\gamma$-ray bursts detected during the first observing run of the Advanced Laser Interferometer Gravitational-Wave Observatory (LIGO). We find no evidence of a GW signal for any of the 41 $\gamma$-ray bursts for which LIGO data are available with sufficient duration. For all $\gamma$-ray bursts, we place lower bounds on the distance to the source using the optimistic assumption that GWs with an energy of $10^{-2}M_\odot c^2$ were emitted within the $16$-$500\,$Hz band, and we find a median 90% confidence limit of 71$\,$Mpc at 150$\,$Hz. For the subset of 19 short/hard $\gamma$-ray bursts, we place lower bounds on distance with a median 90% confidence limit of 90$\,$Mpc for binary neutron star (BNS) coalescences, and 150 and 139$\,$Mpc for neutron star-black hole coalescences with spins aligned to the orbital angular momentum and in a generic configuration, respectively. These are the highest distance limits ever achieved by GW searches. We also discuss in detail the results of the search for GWs associated with GRB 150906B, an event that was localized by the InterPlanetary Network near the local galaxy NGC 3313, which is at a luminosity distance of 54$\,$Mpc ($z=0.0124$). Assuming the $\gamma$-ray emission is beamed with a jet half-opening angle $\leq 30^{\circ}$, we exclude a BNS and a neutron star-black hole in NGC 3313 as the progenitor of this event with confidence $>99$%. Further, we exclude such progenitors up to a distance of 102$\,$Mpc and 170$\,$Mpc, respectively.

Sensitivity Achieved by the LIGO and Virgo Gravitational Wave Detectors during LIGO's Sixth and Virgo's Second and Third Science Runs

Abstract: We summarize the sensitivity achieved by the LIGO and Virgo gravitational wave detectors for low-mass compact binary coalescence (CBC) searches during LIGO's sixth science run and Virgo's second and third science runs. We present strain noise power spectral densities (PSDs) which are representative of the typical performance achieved by the detectors in these science runs. The data presented here and in the accompanying web-accessible data files are intended to be released to the public as a summary of detector performance for low-mass CBC searches during S6 and VSR2-3.

Search for Gravitational Waves Associated with γ-ray Bursts Detected by the Interplanetary Network

Abstract: We present the results of a search for gravitational waves associated with 223 gamma-ray bursts (GRBs) detected by the InterPlanetary Network (IPN) in 2005-2010 during LIGO's fifth and sixth science runs and Virgo's first, second and third science runs. The IPN satellites provide accurate times of the bursts and sky localizations that vary significantly from degree scale to hundreds of square degrees. We search for both a well-modeled binary coalescence signal, the favored progenitor model for short GRBs, and for generic, unmodeled gravitational wave bursts. Both searches use the event time and sky localization to improve the gravitational-wave search sensitivity as compared to corresponding all-time, all-sky searches. We find no evidence of a gravitational-wave signal associated with any of the IPN GRBs in the sample, nor do we find evidence for a population of weak gravitational-wave signals associated with the GRBs. For all IPN-detected GRBs, for which a sufficient duration of quality gravitational-wave data is available, we place lower bounds on the distance to the source in accordance with an optimistic assumption of gravitational-wave emission energy of $10^{-2}M_{\odot}c^2$ at 150 Hz, and find a median of 13 Mpc. For the 27 short-hard GRBs we place 90% confidence exclusion distances to two source models: a binary neutron star coalescence, with a median distance of 12Mpc, or the coalescence of a neutron star and black hole, with a median distance of 22 Mpc. Finally, we combine this search with previously published results to provide a population statement for GRB searches in first-generation LIGO and Virgo gravitational-wave detectors, and a resulting examination of prospects for the advanced gravitational-wave detectors.

Supplement: Localization and broadband follow-up of the gravitational-wave transient GW150914

Abstract: This Supplement provides supporting material for arXiv:1602.08492 . We briefly summarize past electromagnetic (EM) follow-up efforts as well as the organization and policy of the current EM follow-up program. We compare the four probability sky maps produced for the gravitational-wave transient GW150914, and provide additional details of the EM follow-up observations that were performed in the different bands.

Methods and results of a search for gravitational waves associated with gamma-ray bursts using the GEO 600, LIGO, and Virgo detectors

Abstract: In this paper we report on a search for short-duration gravitational wave bursts in the frequency range 64 Hz-1792 Hz associated with gamma-ray bursts (GRBs), using data from GEO600 and one of the LIGO or Virgo detectors. We introduce the method of a linear search grid to analyse GRB events with large sky localisation uncertainties such as the localisations provided by the Fermi Gamma-ray Burst Monitor (GBM). Coherent searches for gravitational waves (GWs) can be computationally intensive when the GRB sky position is not well-localised, due to the corrections required for the difference in arrival time between detectors. Using a linear search grid we are able to reduce the computational cost of the analysis by a factor of O(10) for GBM events. Furthermore, we demonstrate that our analysis pipeline can improve upon the sky localisation of GRBs detected by the GBM, if a high-frequency GW signal is observed in coincidence. We use the linear search grid method in a search for GWs associated with 129 GRBs observed satellite-based gamma-ray experiments between 2006 and 2011. The GRBs in our sample had not been previously analysed for GW counterparts. A fraction of our GRB events are analysed using data from GEO600 while the detector was using squeezed-light states to improve its sensitivity; this is the first search for GWs using data from a squeezed-light interferometric observatory. We find no evidence for GW signals, either with any individual GRB in this sample or with the population as a whole. For each GRB we place lower bounds on the distance to the progenitor, assuming a fixed GW emission energy of $10^{-2} M_{\odot}c^{2}$, with a median exclusion distance of 0.8 Mpc for emission at 500 Hz and 0.3 Mpc at 1 kHz. The reduced computational cost associated with a linear search grid will enable rapid searches for GWs associated with Fermi GBM events in the Advanced detector era.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python

Abstract: SciPy is an open source scientific computing library for the Python programming language. SciPy 1.0 was released in late 2017, about 16 years after the original version 0.1 release. SciPy has become a de facto standard for leveraging scientific algorithms in the Python programming language, with more than 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories, and millions of downloads per year. This includes usage of SciPy in almost half of all machine learning projects on GitHub, and usage by high profile projects including LIGO gravitational wave analysis and creation of the first-ever image of a black hole (M87). The library includes functionality spanning clustering, Fourier transforms, integration, interpolation, file I/O, linear algebra, image processing, orthogonal distance regression, minimization algorithms, signal processing, sparse matrix handling, computational geometry, and statistics. In this work, we provide an overview of the capabilities and development practices of the SciPy library and highlight some recent technical developments.

Array programming with NumPy

Abstract: Array programming provides a powerful, compact and expressive syntax for accessing, manipulating and operating on data in vectors, matrices and higher-dimensional arrays. NumPy is the primary array programming library for the Python language. It has an essential role in research analysis pipelines in fields as diverse as physics, chemistry, astronomy, geoscience, biology, psychology, materials science, engineering, finance and economics. For example, in astronomy, NumPy was an important part of the software stack used in the discovery of gravitational waves1 and in the first imaging of a black hole2. Here we review how a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring and analysing scientific data. NumPy is the foundation upon which the scientific Python ecosystem is constructed. It is so pervasive that several projects, targeting audiences with specialized needs, have developed their own NumPy-like interfaces and array objects. Owing to its central position in the ecosystem, NumPy increasingly acts as an interoperability layer between such array computation libraries and, together with its application programming interface (API), provides a flexible framework to support the next decade of scientific and industrial analysis. NumPy is the primary array programming library for Python; here its fundamental concepts are reviewed and its evolution into a flexible interoperability layer between increasingly specialized computational libraries is discussed.

GW170817: observation of gravitational waves from a binary neutron star inspiral

Abstract: On August 17, 2017 at 12∶41:04 UTC the Advanced LIGO and Advanced Virgo gravitational-wave detectors made their first observation of a binary neutron star inspiral. The signal, GW170817, was detected with a combined signal-to-noise ratio of 32.4 and a false-alarm-rate estimate of less than one per 8.0×10^{4} years. We infer the component masses of the binary to be between 0.86 and 2.26 M_{⊙}, in agreement with masses of known neutron stars. Restricting the component spins to the range inferred in binary neutron stars, we find the component masses to be in the range 1.17-1.60 M_{⊙}, with the total mass of the system 2.74_{-0.01}^{+0.04}M_{⊙}. The source was localized within a sky region of 28 deg^{2} (90% probability) and had a luminosity distance of 40_{-14}^{+8} Mpc, the closest and most precisely localized gravitational-wave signal yet. The association with the γ-ray burst GRB 170817A, detected by Fermi-GBM 1.7 s after the coalescence, corroborates the hypothesis of a neutron star merger and provides the first direct evidence of a link between these mergers and short γ-ray bursts. Subsequent identification of transient counterparts across the electromagnetic spectrum in the same location further supports the interpretation of this event as a neutron star merger. This unprecedented joint gravitational and electromagnetic observation provides insight into astrophysics, dense matter, gravitation, and cosmology.

SciPy 1.0: fundamental algorithms for scientific computing in Python.

Abstract: SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments.