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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.

Papers published on a yearly basis

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Journal ArticleDOI
TL;DR: In this paper, the authors report on the mechanical loss from bulk and shear stresses in thin film, ion beam deposited, titania-doped tantala, and calculate the elastic energy in shear and bulk stresses in the coating.

20 citations

Posted Content
TL;DR: In this article, the authors summarize the sensitivity achieved by the LIGO and Virgo gravitational wave detectors for compact binary coalescence (CBC) searches during the fifth science run and the first science run.
Abstract: We summarize the sensitivity achieved by the LIGO and Virgo gravitational wave detectors for compact binary coalescence (CBC) searches during LIGO's fifth science run and Virgo's first science run. We present noise spectral density curves for each of the four detectors that operated during these science runs which are representative of the typical performance achieved by the detectors for CBC searches. These spectra are intended for release to the public as a summary of detector performance for CBC searches during these science runs.

19 citations

Journal ArticleDOI
TL;DR: In this article, an all-sky search for periodic gravitational waves in the frequency range 50-1000 Hz with the first derivative of frequency in the range $-8.9 \times 10^{-10}$ Hz/s to zero in two years of data collected during LIGO's fifth science run.
Abstract: We report on an all-sky search for periodic gravitational waves in the frequency range $\mathrm{50-1000 Hz}$ with the first derivative of frequency in the range $-8.9 \times 10^{-10}$ Hz/s to zero in two years of data collected during LIGO's fifth science run. Our results employ a Hough transform technique, introducing a $\chi^2$ test and analysis of coincidences between the signal levels in years 1 and 2 of observations that offers a significant improvement in the product of strain sensitivity with compute cycles per data sample compared to previously published searches. Since our search yields no surviving candidates, we present results taking the form of frequency dependent, 95$%$ confidence upper limits on the strain amplitude $h_0$. The most stringent upper limit from year 1 is $1.0\times 10^{-24}$ in the $\mathrm{158.00-158.25 Hz}$ band. In year 2, the most stringent upper limit is $\mathrm{8.9\times10^{-25}}$ in the $\mathrm{146.50-146.75 Hz}$ band. This improved detection pipeline, which is computationally efficient by at least two orders of magnitude better than our flagship Einstein$@$Home search, will be important for "quick-look" searches in the Advanced LIGO and Virgo detector era.

18 citations

Journal Article
TL;DR: In this article, the high-energy-neutrino follow-up observations of the first gravitational wave transient GW150914 observed by the Advanced LIGO detectors on September 14, 2015 are presented.
Abstract: We present the high-energy-neutrino follow-up observations of the first gravitational wave transient GW150914 observed by the Advanced LIGO detectors on September 14, 2015. We search for coincident neutrino candidates within the data recorded by the IceCube and Antares neutrino detectors. A possible joint detection could be used in targeted electromagnetic follow-up observations, given the significantly better angular resolution of neutrino events compared to gravitational waves. We find no neutrino candidates in both temporal and spatial coincidence with the gravitational wave event. Within ±500 s of the gravitational wave event, the number of neutrino candidates detected by IceCube and Antares were three and zero, respectively. This is consistent with the expected atmospheric background, and none of the neutrino candidates were directionally coincident with GW150914. We use this nondetection to constrain neutrino emission from the gravitational-wave event.

14 citations

Journal ArticleDOI
TL;DR: In this article, the authors report on the development and extensive characterization of co-sputtered tantala-zirconia thin films, with the goal to decrease coating Brownian noise in present and future gravitational-wave detectors.
Abstract: We report on the development and extensive characterization of co-sputtered tantala-zirconia thin films, with the goal to decrease coating Brownian noise in present and future gravitational-wave detectors We tested a variety of sputtering processes of different energies and deposition rates, and we considered the effect of different values of cation ratio $\eta =$ Zr/(Zr+Ta) and of post-deposition heat treatment temperature $T_a$ on the optical and mechanical properties of the films Co-sputtered zirconia proved to be an efficient way to frustrate crystallization in tantala thin films, allowing for a substantial increase of the maximum annealing temperature and hence for a decrease of coating mechanical loss The lowest average coating loss was observed for an ion-beam sputtered sample with $\eta = 0485 \pm 0004$ annealed at 800 $^{\circ}$C, yielding $\overline{\varphi} = 18 \times 10^{-4}$ All coating samples showed cracks after annealing Although in principle our measurements are sensitive to such defects, we found no evidence that our results were affected The issue could be solved, at least for ion-beam sputtered coatings, by decreasing heating and cooling rates down to 7 $^{\circ}$C/h While we observed as little optical absorption as in the coatings of current gravitational-wave interferometers (05 parts per million), further development will be needed to decrease light scattering and avoid the formation of defects upon annealing

13 citations


Cited by
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Journal ArticleDOI

[...]

08 Dec 2001-BMJ
TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
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 …

33,785 citations

Journal ArticleDOI
TL;DR: SciPy as discussed by the authors is an open source scientific computing library for the Python programming language, which 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.
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.

12,774 citations

Journal ArticleDOI
16 Sep 2020-Nature
TL;DR: In this paper, the authors review how a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring and analysing scientific data, and their evolution into a flexible interoperability layer between increasingly specialized computational libraries is discussed.
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.

7,624 citations

Journal ArticleDOI
B. P. Abbott1, Richard J. Abbott1, T. D. Abbott2, Fausto Acernese3  +1131 moreInstitutions (123)
TL;DR: The association of 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.
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.

7,327 citations

Journal ArticleDOI
TL;DR: SciPy as discussed by the authors is an open-source scientific computing library for the Python programming language, which 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.
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.

6,244 citations