Designing a template bank to observe compact binary coalescences in Advanced LIGO's second observing run

We describe the methodology and novel techniques used to construct a set of waveforms, or template bank, applicable to searches for compact binary coalescences in Advanced LIGO's second observing run. This template bank is suitable for observing systems composed of two neutron stars, two black holes, or a neutron star and a black hole. The Post-Newtonian formulation is used to model waveforms with total mass less than 4 $M_{\odot}$ and the most recent effective-one-body model, calibrated to numerical relativity to include the merger and ringdown, is used for total masses greater than 4 $M_{\odot}$. The effects of spin precession, matter, orbital eccentricity and radiation modes beyond the quadrupole are neglected. In contrast to the template bank used to search for compact binary mergers in Advanced LIGO's first observing run, here we are including binary-black-hole systems with total mass up to several hundreds of solar masses, thereby improving the ability to observe such systems. We introduce a technique to vary the starting frequency of waveform filters so that our bank can simultaneously contain binary-neutron-star and high-mass binary-black hole waveforms. We also introduce a lower-bound on the filter waveform length, to exclude very short-duration, high-mass templates whose sensitivity is strongly reduced by the characteristics and performance of the interferometers.Comment: 10 pages, 8 figure

Numerical Relativity Injection Infrastructure

This document describes the new Numerical Relativity (NR) injection infrastructure in the LIGO Algorithms Library (LAL), which henceforth allows for the usage of NR waveforms as a discrete waveform approximant in LAL. With this new interface, NR waveforms provided in the described format can directly be used as simulated GW signals ("injections") for data analyses, which include parameter estimation, searches, hardware injections etc. As opposed to the previous infrastructure, this new interface natively handles sub-dominant modes and waveforms from numerical simulations of precessing binary black holes, making them directly accessible to LIGO analyses. To correctly handle precessing simulations, the new NR injection infrastructure internally transforms the NR data into the coordinate frame convention used in LAL.Comment: 20 pages, 2 figures, technical repor

When can gravitational-wave observations distinguish between black holes and neutron stars?

Gravitational-wave observations of compact binaries have the potential to uncover the distribution of masses and angular momenta of black holes and neutron stars in the universe. The binary components' physical parameters can be inferred from their effect on the phasing of the gravitational-wave signal, but a partial degeneracy between the components' mass ratio and their angular momenta limits our ability to measure the individual component masses. At the typical signal amplitudes expected by the Advanced Laser Interferometer Gravitational-wave Observatory (signal-to-noise ratios between 10 and 20), we show that it will in many cases be difficult to distinguish whether the components are neutron stars or black holes. We identify when the masses of the binary components could be unambiguously measured outside the range of current observations: a system with a chirp mass $\mathcal{M} \le 0.871$ M$_\odot$ would unambiguously contain the smallest-mass neutron star observed, and a system with \mathcal{M} \ge 2.786 \Msun must contain a black hole. However, additional information would be needed to distinguish between a binary containing two 1.35 M$_\odot$ neutron stars and an exotic neutron-star--black-hole binary. We also identify those configurations that could be unambiguously identified as black-hole binaries, and show how the observation of an electromagnetic counterpart to a neutron-star--black-hole binary could be used to constrain the black-hole spin.Comment: 5 pages, 4 figures. Final version to be published in Ap.J.Let

ArchEnemy: Removing scattered-light glitches from gravitational wave data

Data recorded by gravitational wave detectors includes many non-astrophysical transient noise bursts, the most common of which is caused by scattered-light within the detectors. These so-called ``glitches'' in the data impact the ability to both observe and characterize incoming gravitational wave signals. In this work we use a scattered-light glitch waveform model to identify and characterize scattered-light glitches in a representative stretch of gravitational wave data. We identify $2749$ scattered-light glitches in $5.96$ days of LIGO-Hanford data and $1306$ glitches in $5.93$ days of LIGO-Livingston data taken from the third LIGO-Virgo observing run. By subtracting identified scattered-light glitches we demonstrate an increase in the sensitive volume of the gravitational wave search for binary black hole signals by $\sim1\%$.Comment: 30 pages + acknowledgements and references, 13 figure

Application of Artificial Neural Network to Search for Gravitational-Wave Signals Associated with Short Gamma-Ray Bursts

We apply a machine learning algorithm, the artificial neural network, to the search for gravitational-wave signals associated with short gamma-ray bursts. The multi-dimensional samples consisting of data corresponding to the statistical and physical quantities from the coherent search pipeline are fed into the artificial neural network to distinguish simulated gravitational-wave signals from background noise artifacts. Our result shows that the data classification efficiency at a fixed false alarm probability is improved by the artificial neural network in comparison to the conventional detection statistic. Therefore, this algorithm increases the distance at which a gravitational-wave signal could be observed in coincidence with a gamma-ray burst. In order to demonstrate the performance, we also evaluate a few seconds of gravitational-wave data segment using the trained networks and obtain the false alarm probability. We suggest that the artificial neural network can be a complementary method to the conventional detection statistic for identifying gravitational-wave signals related to the short gamma-ray bursts.Comment: 30 pages, 10 figure

Lensed or not lensed: Determining lensing magnifications for binary neutron star mergers from a single detection

Advanced LIGO and Advanced Virgo could observe the first lensed gravitational wave sources in the coming years, while the future Einstein Telescope could observe hundreds of lensed events. It is, therefore, crucial to develop methodologies to distinguish between lensed from unlensed gravitational-wave observations. A lensed signal not identified as such will lead to biases during the interpretation of the source. In particular, sources will appear to have intrinsically higher masses. No robust method currently exists to distinguish between the magnification bias caused by lensing and intrinsically high-mass sources. In this work, we show how to recognize lensed and unlensed binary neutron star systems through the measurement of their tidal effects for highly magnified sources as a proof-of-principle. The proposed method could be used to identify lensed binary neutron stars, which are the chief candidate for lensing cosmography studies. We apply our method on GW190425, finding no evidence in favor of lensing, mainly due to the poor measurement of the event's tidal effects. However, we expect that future detections with better tidal measurements can yield better constraints.Comment: 12 pages, 7 figure

Investigating the medium range order in amorphous Ta₂O₅ coatings

Ion-beam sputtered amorphous heavy metal oxides, such as Ta2O5, are widely used as the high refractive index layer of highly reflective dielectric coatings. Such coatings are used in the ground based Laser Interferometer Gravitational-wave Observatory (LIGO), in which mechanical loss, directly related to Brownian thermal noise, from the coatings forms an important limit to the sensitivity of the LIGO detector. It has previously been shown that heat-treatment and TiO2 doping of amorphous Ta2O5 coatings causes significant changes to the levels of mechanical loss measured and is thought to result from changes in the atomic structure. This work aims to find ways to reduce the levels of mechanical loss in the coatings by understanding the atomic structure properties that are responsible for it, and thus helping to increase the LIGO detector sensitivity. Using a combination of Reduced Density Functions (RDFs) from electron diffraction and Fluctuation Electron Microscopy (FEM), we probe the medium range order (in the 2-3 nm range) of these amorphous coatings