The computational cost of searching for gravitational wave (GW) signals in low latency has always been a matter of concern. We present a self-supervised learning model applicable to the GW detection. Based on simulated massive black hole binary signals in synthetic Gaussian noise representative of space-based GW detectors Taiji and LISA sensitivity, and regarding their corresponding datasets as a GW twins in the contrastive learning method, we show that the self-supervised learning may be a highly computationally efficient method for GW signal identification.
We introduce a new method for the identification of galaxy systems in redshift surveys based on the halo model. This method is a modified version of the K-means identification algorithm developed by Yang et al (2005). We have calibrated and tested our algorithms using mock catalogs generated using the Millennium simulations (Springel et al. 2005) and applied them to the NYU-DR7 galaxy catalog (based on the SDSS datasets). Using this local sample of groups and clusters of galaxies we have measured the effect of gravitational redshift produced by their host dark matter haloes. Our results shows …
The primary target for the planned space-borne gravitational wave interferometers DECIGO/BBO is a primordial gravitational wave background (PGWB). However there exist astrophysical foregrounds and among them, gravitational waves from neutron star (NS) binaries are the solid and strong component that must be identified and subtracted. In this paper, we discuss the geometry of detector configurations preferable for identifying the NS/NS binary signals. As a first step, we analytically estimate the minimum signal-to-noise ratios (SNRs) of the binaries for several static detector configurations th…
Similarly to light, gravitational waves can be gravitationally lensed as they propagate near massive astrophysical objects such as galaxies, stars, or black holes. In recent years, forecasts have suggested a reasonable chance of strong gravitational-wave lensing detections with the LIGO-Virgo-KAGRA detector network at design sensitivity. As a consequence, methods to analyse lensed detections have seen rapid development. However, the impact of higher-order modes on the lensing analyses is still under investigation. In this work, we show that the presence of higher-order modes enables the identi…
The data from the ground-based interferometric gravitational wave detectors(GW) is often masqueraded by highly localised short-duration glitches which pose a serious challenge for any transient GW search. In this work, we propose a glitch identification algorithm for the scenario of presence of a glitch along with a long duration GW transient. We device the chirp mass consistency parameter across different Q planes in the Q-transform. We test this algorithm over a variety of short duration glitches from the observing runs along with a long duration binary inspiral signal. The demonstration of …
I define here a novel function on a modeled space of gravitational-wave signals, before studying its properties as a statistic for detection, as an objective function for identification, and as an effective likelihood function for inference. The main motivation behind this work is the open data-analysis problem for signals from extreme-mass-ratio inspirals, which is severely hindered by the presence of strong non-local parameter degeneracy in the signal space. I demonstrate the utility of the proposed function for the analysis of such signals, and suggest various possible directions for future…
We present an algorithm for the identification of transient noise artifacts (glitches) in cross-correlation searches for long O(10s) gravitational-wave transients. The algorithm utilizes the auto-power in each detector as a discriminator between well-behaved Gaussian noise (possibly including a gravitational-wave signal) and glitches. We test the algorithm with both Monte Carlo noise and time-shifted data from the LIGO S5 science run and find that it is effective at removing a significant fraction of glitches while keeping the vast majority (99.6%) of the data. Using an accretion disk instabil…
We present a new ${\it{gating}}$ method to remove non-Gaussian noise transients in gravitational wave data. The method does not rely on any a-priori knowledge on the amplitude or duration of the transient events. In light of the character of the newly released LIGO O3a data, glitch-identification is particularly relevant for searches using this data. Our method preserves more data than previously achieved, while obtaining the same, if not higher, noise reduction. We achieve a $\approx$ 2-fold reduction in zeroed-out data with respect to the gates released by LIGO on the O3a data. We describe t…
Similar to light, gravitational waves (GWs) can be lensed. Such lensing phenomena can magnify the waves, create multiple images observable as repeated events, and superpose several waveforms together, inducing potentially discernible patterns on the waves. In particular, when the lens is small, $\lesssim 10^5 M_\odot$, it can produce lensed images with time delays shorter than the typical gravitational-wave signal length that conspire together to form ``beating patterns''. We present a proof-of-principle study utilizing deep learning for identification of such a lensing signature. We bring the…
We present a general framework for incorporating astrophysical information into Bayesian parameter estimation techniques used by gravitational wave data analysis to facilitate multi-messenger astronomy. Since the progenitors of transient gravitational wave events, such as compact binary coalescences, are likely to be associated with a host galaxy, improvements to the source sky location estimates through the use of host galaxy information are explored. To demonstrate how host galaxy properties can be included, we simulate a population of compact binary coalescences and show that for ~8.5% of s…
Gravitational waves searches for compact binary mergers with LIGO and Virgo are presently a two stage process. First, a gravitational wave signal is identified. Then, an exhaustive search over possible signal parameters is performed. It is critical that the identification stage is efficient in order to maximize the number of gravitational wave sources that are identified. Initial identification of gravitational wave signals with LIGO and Virgo happens in real-time which requires that less than one second of computational time must be used for each one second of gravitational wave data collecte…
The LIGO observatories detect gravitational waves through monitoring changes in the detectors' length down to below $10^{-19}$\,$m/\sqrt{Hz}$ variation---a small fraction of the size of the atoms that make up the detector. To achieve this sensitivity, the detector and its environment need to be closely monitored. Beyond the gravitational wave data stream, LIGO continuously records hundreds of thousands of channels of environmental and instrumental data in order to monitor for possibly minuscule variations that contribute to the detector noise. A particularly challenging issue is the appearance…
In this paper we address both to the problem of identifying the noise Power Spectral Density of interferometric detectors by parametric techniques and to the problem of the whitening procedure of the sequence of data. We will concentrate the study on a Power Spectral Density like the one of the Italian-French detector VIRGO and we show that with a reasonable finite number of parameters we succeed in modeling a spectrum like the theoretical one of VIRGO, reproducing all its features. We propose also the use of adaptive techniques to identify and to whiten on line the data of interferometric det…
An important goal for LIGO (the Laser Interferometer Gravitational-Wave Observatory) and Virgo is to find periodic sources of gravitational waves. The LIGO and Virgo detectors are sensitive to a variety of noise of non-astrophysical origin, such as instrumental artifacts and environmental disturbances. These artifacts make it difficult to know when a signal is due to a gravitational wave or noise. A continuous wave search algorithm, Fscan, and the calculation of the coherence between the gravitational wave channels and auxiliary channels has been developed to identify the source of noise lines…
We present a follow-up method based on supervised machine learning (ML) to improve the performance in the search of gravitational wave (GW) burts from core-collapse supernovae (CCSNe) using the coherent WaveBurst (cWB) pipeline. The ML model discriminates noise from signal events using as features a set of reconstruction parameters provided by cWB. Detected noise events are discarded yielding to a reduction of the false alarm rate (FAR) and of the false alarm probability (FAP) thus enhancing of the statistical significance. We tested the proposed method using strain data from the first half of…
With the increasing sensitivity of advanced gravitational wave detectors, the first joint detection of an electromagnetic and gravitational wave signal from a compact binary merger will hopefully happen within this decade. However, current gravitational-wave likelihood sky areas span $\sim 100-1000\,\textrm{deg}^2$, and thus it is a challenging task to identify which, if any, transient corresponds to the gravitational-wave event. In this study, we make a comparison between recent kilonovae/macronovae lightcurve models for the purpose of assessing potential lightcurve templates for counterpart …
Penrose's identification with warp provides the general framework for constructing the continuous form of impulsive gravitational wave metrics. We present the 2-component spinor formalism for the derivation of the full family of impulsive spherical gravitational wave metrics which brings out the power in identification with warp and leads to the simplest derivation of exact solutions. These solutions of the Einstein vacuum field equations are obtained by cutting Minkowski space into two pieces along a null cone and re-identifying them with warp which is given by an arbitrary non-linear holomor…
A small fraction of the gravitational-wave (GW) signals that will be detected by second and third generation detectors are expected to be strongly lensed by galaxies and clusters, producing multiple observable copies. While optimal Bayesian model selection methods are developed to identify lensed signals, processing tens of thousands (billions) of possible pairs of events detected with second (third) generation detectors is both computationally intensive and time consuming. To mitigate this problem, we propose to use machine learning to rapidly rule out a vast majority of candidate lensed pair…
As gravitational-wave (GW) interferometers become more sensitive and probe ever more distant reaches, the number of detected binary neutron star mergers will increase. However, detecting more events farther away with GWs does not guarantee corresponding increase in the number of electromagnetic counterparts of these events. Current and upcoming wide-field surveys that participate in GW follow-up operations will have to contend with distinguishing the kilonova from the ever increasing number of transients they detect, many of which will be consistent with the GW sky-localization. We have develo…
Max Planck Institute for Gravitational Physics (Albert Einstein Institute)（马克斯·普朗克引力物理研究所（阿尔伯特·爱因斯坦研究所））；Leibniz Universität Hannover（莱布尼茨汉诺威大学）；University of Wisconsin Milwaukee（威斯康星大学密尔沃基分校）
Broad searches for continuous gravitational wave signals rely on hierarchies of follow-up stages for candidates above a given significance threshold. An important step to simplify these follow-ups and reduce the computational cost is to bundle together in a single follow-up nearby candidates. This step is called clustering and we investigate carrying it out with a deep learning network. In our first paper [1], we implemented a deep learning clustering network capable of correctly identifying clusters due to large signals. In this paper, a network is implemented that can detect clusters due to …
Self-supervised reconstruction of transients in data from space-borne gravitational-wave detectors
Center for Gravitational Wave Experiment, National Microgravity Laboratory, Institute of Mechanics, Chinese Academy of Sciences（中国科学院力学研究所微重力实验室引力波实验中心）；Taiji Laboratory for Gravitational Wave Universe (Beijing/Hangzhou), University of Chinese Academy of Sciences (UCAS)（中国科学院大学太极引力波宇宙实验室（北京/杭州））
