Investigating the noise residuals around the gravitational wave event GW150914

We use the Pearson cross-correlation statistic proposed by Liu and Jackson, and employed by Creswell et al., to look for statistically significant correlations between the LIGO Hanford and Livingston detectors at the time of the binary black hole merger GW150914. We compute this statistic for the calibrated strain data released by LIGO, using both the residuals provided by LIGO and using our own subtraction of a maximum-likelihood waveform that is constructed to model binary black hole mergers in general relativity. To assign a significance to the values obtained, we calculate the cross-correlation of both simulated Gaussian noise and data from the LIGO detectors at times during which no detection of gravitational waves has been claimed. We find that after subtracting the maximum likelihood waveform there are no statistically significant correlations between the residuals of the two detectors at the time of GW150914.Comment: 14 pages, 7 figures. Minor text and figure changes in final v3. Notebooks for generating the results are available at https://github.com/gwastro/gw150914_investigatio

Utilizing the null stream of Einstein Telescope

Among third-generation ground-based gravitational-wave detectors proposed for the next decade, Einstein Telescope provides a unique kind of null stream \unicode{x2014} the signal-free linear combination of data \unicode{x2014} that enables otherwise inaccessible tests of the noise models. We project and showcase challenges in modeling the noise in the 2030-s and how it will affect the performance of third-generation detectors. We find that the null stream of Einstein Telescope is capable of entirely eliminating transient detector glitches that are known to limit current gravitational-wave searches. The techniques we discuss are computationally efficient and do not require a-priori knowledge about glitch models. Furthermore, we show how the null stream can be used to provide an unbiased estimation of the noise power spectrum necessary for online and offline data analyses even with multiple loud signals in band. We overview other approaches to utilizing the null stream. Finally, we comment on the limitations and future challenges of null stream analyses for Einstein Telescope and arbitrary detector networks

Detection of gravitational-wave signals from binary neutron star mergers using machine learning

As two neutron stars merge, they emit gravitational waves that can potentially be detected by Earth-bound detectors. Matched-filtering-based algorithms have traditionally been used to extract quiet signals embedded in noise. We introduce a novel neural-network-based machine learning algorithm that uses time series strain data from gravitational-wave detectors to detect signals from nonspinning binary neutron star mergers. For the Advanced LIGO design sensitivity, our network has an average sensitive distance of 130 Mpc at a false-alarm rate of ten per month. Compared to other state-of-the-art machine learning algorithms, we find an improvement by a factor of 4 in sensitivity to signals with a signal-to-noise ratio between 8 and 15. However, this approach is not yet competitive with traditional matched-filtering-based methods. A conservative estimate indicates that our algorithm introduces on average 10.2 s of latency between signal arrival and generating an alert. We give an exact description of our testing procedure, which can be applied not only to machine-learning-based algorithms but all other search algorithms as well. We thereby improve the ability to compare machine learning and classical searches. © 2020 authors. Published by the American Physical Society

Uncoupling of Brain Activity from Movement Defines Arousal States in Drosophila

AbstractBackground: An animal's state of arousal is fundamental to all of its behavior. Arousal is generally ascertained by measures of movement complemented by brain activity recordings, which can provide signatures independently of movement activity. Here we examine the relationships among movement, arousal state, and local field potential (LFP) activity in the Drosophila brain.Results: We have measured the correlation between local field potentials (LFPs) in the brain and overt movements of the fruit fly during different states of arousal, such as spontaneous daytime waking movement, visual arousal, spontaneous night-time movement, and stimulus-induced movement. We found that the correlation strength between brain LFP activity and movement was dependent on behavioral state and, to some extent, on LFP frequency range. Brain activity and movement were uncoupled during the presentation of visual stimuli and also in the course of overnight experiments in the dark. Epochs of low correlation or uncoupling were predictive of increased arousal thresholds even in moving flies and thus define a distinct state of arousal intermediate between sleep and waking in the fruit fly.Conclusions: These experiments indicate that the relationship between brain LFPs and movement in the fruit fly is dynamic and that the degree of coupling between these two measures of activity defines distinct states of arousal

Potential Gravitational-wave and Gamma-ray Multi-messenger Candidate from 2015 October 30

We present a search for binary neutron star (BNS) mergers that produced gravitational waves during the first observing run of the Advanced Laser Interferometer Gravitational-Wave Observatory (LIGO), and gamma-ray emission seen by either the Swift-Burst Alert Telescope (BAT) or the Fermi-Gamma-ray Burst Monitor (GBM), similar to GW170817 and GRB 170817A. We introduce a new method using a combined ranking statistic to detect sources that do not produce significant gravitational-wave or gamma-ray burst candidates individually. The current version of this search can increase by 70% the detections of joint gravitational-wave and gamma-ray signals. We find one possible candidate observed by LIGO and Fermi-GBM, 1-OGC 151030, at a false alarm rate of 1 in 13 yr. If astrophysical, this candidate would correspond to a merger at Mpc with source-frame chirp mass of . If we assume that the viewing angle must be <30° to be observed by Fermi-GBM, our estimate of the distance would become Mpc. By comparing the rate of BNS mergers to our search-estimated rate of false alarms, we estimate that there is a 1 in 4 chance that this candidate is astrophysical in origin

Low significance of evidence for black hole echoes in gravitational wave data

Recent detections of merging black holes allow observational tests of the nature of these objects. In some proposed models, non-trivial structure at or near the black hole horizon could lead to echo signals in gravitational wave data. Recently, Abedi et al. claimed tentative evidence for repeating damped echo signals following the gravitational-wave signals of the binary black hole merger events recorded in the first observational period of the Advanced LIGO interferometers. We reanalyse the same data, addressing some of the shortcomings of their method using more background data and a modified procedure. We find a reduced statistical significance for the claims of evidence for echoes, calculating increased p-values for the null hypothesis of echo-free noise. The reduced significance is entirely consistent with noise, and so we conclude that the analysis of Abedi et al. does not provide any observational evidence for the existence of Planck-scale structure at black hole horizons.Comment: As accepted by Physical Review

Training Strategies for Deep Learning Gravitational-Wave Searches

Compact binary systems emit gravitational radiation which is potentially detectable by current Earth bound detectors. Extracting these signals from the instruments' background noise is a complex problem and the computational cost of most current searches depends on the complexity of the source model. Deep learning may be capable of finding signals where current algorithms hit computational limits. Here we restrict our analysis to signals from non-spinning binary black holes and systematically test different strategies by which training data is presented to the networks. To assess the impact of the training strategies, we re-analyze the first published networks and directly compare them to an equivalent matched-filter search. We find that the deep learning algorithms can generalize low signal-to-noise ratio (SNR) signals to high SNR ones but not vice versa. As such, it is not beneficial to provide high SNR signals during training, and fastest convergence is achieved when low SNR samples are provided early on. During testing we found that the networks are sometimes unable to recover any signals when a false alarm probability $<10^{-3}$ is required. We resolve this restriction by applying a modification we call unbounded Softmax replacement (USR) after training. With this alteration we find that the machine learning search retains $\geq 97.5\%$ of the sensitivity of the matched-filter search down to a false-alarm rate of 1 per month

Search for Coincident Gravitational Wave and Long Gamma-Ray Bursts from 4-OGC and the Fermi-GBM/Swift-BAT Catalog

The recent discovery of a kilonova associated with an apparent long-durationgamma-ray burst has challenged the typical classification that long gamma-raybursts originate from the core collapse of massive stars and short gamma-raybursts are from compact binary coalescence. The kilonova indicates a neutronstar merger origin and suggests the viability of gravitational-wave and longgamma-ray burst multimessenger astronomy. Gravitational waves play a crucialrole by providing independent information for the source properties. This workrevisits the archival 2015-2020 LIGO/Virgo gravitational-wave candidates fromthe 4-OGC catalog which are consistent with a binary neutron star or neutronstar-black hole merger and the long-duration gamma-ray bursts from theFermi-GBM and Swift-BAT catalogs. We search for spatial and temporalcoincidence with up to 10 s time lag between gravitational-wave candidates andthe onset of long-duration gamma-ray bursts. The most significant candidateassociation has only a false alarm rate of once every two years; given theLIGO/Virgo observational period, this is consistent with a null result. Wereport an exclusion distance for each search candidate for a fiducialgravitational-wave signal and conservative viewing angle assumptions.<br

Empirically Derived Integrated Stellar Yields of Fe-Peak Elements

We present here the initial results of a new study of massive star yields of Fe-peak elements. We have compiled from the literature a database of carefully determined solar neighborhood stellar abundances of seven iron-peak elements, Ti, V, Cr, Mn, Fe, Co, and Ni and then plotted [X/Fe] versus [Fe/H] to study the trends as functions of metallicity. Chemical evolution models were then employed to force a fit to the observed trends by adjusting the input massive star metallicity-sensitive yields of Kobayashi et al. Our results suggest that yields of Ti, V, and Co are generally larger as well as anticorrelated with metallicity, in contrast to the Kobayashi et al. predictions. We also find the yields of Cr and Mn to be generally smaller and directly correlated with metallicity compared to the theoretical results. Our results for Ni are consistent with theory, although our model suggests that all Ni yields should be scaled up slightly. The outcome of this exercise is the computation of a set of integrated yields, i.e., stellar yields weighted by a slightly flattened time-independent Salpeter initial mass function and integrated over stellar mass, for each of the above elements at several metallicity points spanned by the broad range of observations. These results are designed to be used as empirical constraints on future iron-peak yield predictions by stellar evolution modelers. Special attention is paid to the interesting behavior of [Cr/Co] with metallicity -- these two elements have opposite slopes -- as well as the indirect correlation of [Ti/Fe] with [Fe/H]. These particular trends, as well as those exhibited by the inferred integrated yields of all iron-peak elements with metallicity, are discussed in terms of both supernova nucleosynthesis and atomic physics.Comment: 27 pages, 6 figures; Accepted for Publication in the Astrophysical Journa