arXiv subjects

Ryan Magee

Publications and source records attributed to Ryan Magee.

At least 37 records · Page 2Linked to original sources

arXiv2023

Improved ranking statistics of the GstLAL inspiral search for compact binary coalescences

Starting from May 2023, the LIGO Scientific, Virgo and KAGRA Collaboration is planning to conduct the fourth observing run with improved detector sensitivities and an expanded detector network including KAGRA. Accordingly, it is vital to optimize the detection algorithm of low-latency search pipelines, increasing their sensitivities to gravitational waves from compact binary coalescences. In this work, we discuss several new features developed for ranking statistics of GstLAL-based inspiral pipeline, which mainly consist of: the signal contamination removal, the bank-$ξ^2$ incorporation, the upgraded $ρ-ξ^2$ signal model and the integration of KAGRA. An injection study demonstrates that these new features improve the pipeline's sensitivity by approximately 15% to 20%, paving the way to further multi-messenger observations during the upcoming observing run.

astro-ph.IM↗

arXiv2022

Metric Assisted Stochastic Sampling (MASS) search for gravitational waves from binary black hole mergers

We present a novel gravitational wave detection algorithm that conducts a matched filter search stochastically across the compact binary parameter space rather than relying on a fixed bank of template waveforms. This technique is competitive with standard template-bank-driven pipelines in both computational cost and sensitivity. However, the complexity of the analysis is simpler allowing for easy configuration and horizontal scaling across heterogeneous grids of computers. To demonstrate the method we analyze approximately one month of public LIGO data from July 27 00:00 2017 UTC - Aug 25 22:00 2017 UTC and recover eight known confident gravitational wave candidates. We also inject simulated binary black hole (BBH) signals to demonstrate the sensitivity.

gr-qc↗

arXiv2022

A binary tree approach to template placement for searches for gravitational waves from compact binary mergers

We demonstrate a new geometric method for fast template placement for searches for gravitational waves from the inspiral, merger and ringdown of compact binaries. The method is based on a binary tree decomposition of the template bank parameter space into non-overlapping hypercubes. We use a numerical approximation of the signal overlap metric at the center of each hypercube to estimate the number of templates required to cover the hypercube and determine whether to further split the hypercube. As long as the expected number of templates in a given cube is greater than a given threshold, we split the cube along its longest edge according to the metric. When the expected number of templates in a given hypercube drops below this threshold, the splitting stops and a template is placed at the center of the hypercube. Using this method, we generate aligned-spin template banks covering the mass range suitable for a search of Advanced LIGO data. The aligned-spin bank required ~24 CPU-hours and produced 2 million templates. In general, we find that other methods, namely stochastic placement, produces a more strictly bounded loss in match between waveforms, with the same minimal match between waveforms requiring about twice as many templates with our proposed algorithm. Though we note that the average match is higher, which would lead to a higher detection efficiency. Our primary motivation is not to strictly minimize the number of templates with this algorithm, but rather to produce a bank with useful geometric properties in the physical parameter space coordinates. Such properties are useful for population modeling and parameter estimation.

gr-qc↗

arXiv2022

Realistic observing scenarios for the next decade of early warning detection of binary neutron stars

We describe realistic observing scenarios for early warning detection of binary neutron star mergers with the current generation of ground-based gravitational-wave detectors as these approach design sensitivity. Using Fisher analysis, we estimate that Advanced LIGO and Advanced Virgo will detect one signal before merger in their fourth observing run provided they maintain a 70\% duty cycle. 60\% of all observations and 8\% of those detectable 20 seconds before merger will be localized to $\lesssim 100 \thinspace \mathrm{deg}^2$. If KAGRA is able to achieve a 25 Mpc horizon, these prospects increase to $\lesssim 2$ early detections with 70\% of all BNS localized to $\lesssim 100 \thinspace \mathrm{deg}^2$ by merger. As the AHKLV network approaches design sensitivity over the next $\sim10$ years, we expect up to 1 (14) detections made 100 (10) seconds before merger. Although adding detectors to the HLV network impacts the detection rate at $\lesssim 50\%$ level, it improves localization prospects and increases the completeness of compact binary surveys. Given uncertainties in sensitivities, participating detectors, and duty cycles, we consider 103 future detector configurations so electromagnetic observers can tailor preparations towards their preferred models.

astro-ph.HE↗

arXiv2021

A gravitational-wave limit on the Chandrasekhar mass of dark matter

We explore a new paradigm to study dissipative dark matter models using gravitational-wave observations. We consider a dark atomic model which predicts the formation of binary black holes such as GW190425 while obeying constraints from large-scale structure, and improving on the missing satellite problem. Using LIGO and Virgo gravitational-wave data from 12th September 2015 to 1st October 2019, we show that interpreting GW190425 as a dark matter black-hole binary limits the Chandrasekhar mass for dark matter to be below 1.4 $M_\odot$ at $> 99.9\%$ confidence implying that the dark proton is heavier than 0.95 GeV, while also suggesting that the molecular energy-level spacing of dark molecules lies near $10^{-3}$ eV and constraining the cooling rate of dark matter at low temperatures.

astro-ph.CO↗

arXiv2021

Early warning of coalescing neutron-star and neutron-star-black-hole binaries from nonstationary noise background using neural networks

The success of the multi-messenger astronomy relies on gravitational-wave observatories like LIGO and Virgo to provide prompt warning of merger events involving neutron stars (including both binary neutron stars and neutron-star-black-holes), which further depends critically on the low-frequency sensitivity of LIGO as a typical binary neutron star stays in this band for minutes. However, the current sub-60 Hz sensitivity of LIGO has not yet reached its design target and the excess noise can be more than an order of magnitude below 20 Hz. It is limited by nonlinearly coupled noises from auxiliary control loops which are also nonstationary, posing challenges to realistic early-warning pipelines. Nevertheless, machine-learning-based neural networks provide ways to simultaneously improve the low-frequency sensitivity and mitigate its nonstationarity, and detect the real-time gravitational-wave signal with a very short computational time. We propose to achieve this by inputting both the main gravitational-wave readout and key auxiliary witnesses to a compound neural network. Using simulated data with characteristic representing the real LIGO detectors, our machine-learning-based neural networks can reduce nonlinearly coupled noise by about a factor of 5 and allows a typical binary neutron star (neutron-star-black-hole) to be detected 100 s (10 s) before the merger at a distance of 40 Mpc (160 Mpc). If one can further reduce the noise to the fundamental limit, our neural networks can achieve detection out to a distance of 80 Mpc and 240 Mpc for binary neutron stars and neutron-star-black-holes, respectively. It thus demonstrates that utilizing machine-learning-based neural networks is a promising direction for the timely detection of the coalescence of electromagnetically bright LIGO/Virgo sources.

gr-qc↗

arXiv2021

Template bank for spinning compact binary mergers in the second observation run of Advanced LIGO and the first observation run of Advanced Virgo

We describe the methods used to construct the aligned-spin template bank of gravitational waveforms used by the GstLAL-based inspiral pipeline to analyze data from the second observing run of Advanced LIGO and the first observing run of advanced Virgo. The bank expands upon the parameter space covered during the first observing run, including coverage for merging compact binary systems with total mass between 2 $\mathrm{M}_{\odot}$ and 400 $\mathrm{M}_{\odot}$ and mass ratios between 1 and 97.988. Thus the systems targeted include merging neutron star-neutron star systems, neutron star-black hole binaries, and black hole-black hole binaries expanding into the intermediate-mass range. Component masses less than 2 $\mathrm{M}_{\odot}$ have allowed (anti-)aligned spins between $\pm0.05$ while component masses greater than 2 $\mathrm{M}_{\odot}$ have allowed (anti-)aligned between $\pm0.999$. The bank placement technique combines a stochastic method with a new grid-bank method to better isolate noisy templates, resulting in a total of 677,000 templates.

astro-ph.IM↗

arXiv2021

First demonstration of early warning gravitational wave alerts

Gravitational-wave observations became commonplace in Advanced LIGO-Virgo's recently concluded third observing run. 56 non-retracted candidates were identified and publicly announced in near real time. Gravitational waves from binary neutron star mergers, however, remain of special interest since they can be precursors to high-energy astrophysical phenomena like $γ$-ray bursts and kilonovae. While late-time electromagnetic emissions provide important information about the astrophysical processes within, the prompt emission along with gravitational waves uniquely reveals the extreme matter and gravity during - and in the seconds following - merger. Rapid communication of source location and properties from the gravitational-wave data is crucial to facilitate multi-messenger follow-up of such sources. This is especially enabled if the partner facilities are forewarned via an early-warning (pre-merger) alert. Here we describe the commissioning and performance of such a low-latency infrastructure within LIGO-Virgo. We present results from an end-to-end mock data challenge that detects binary neutron star mergers and alerts partner facilities before merger. We set expectations for these alerts in future observing runs.

astro-ph.HE↗

arXiv2020

Automating the Inclusion of Subthreshold Signal-to-Noise Ratios for Rapid Gravitational-Wave Localization

The accurate localization of gravitational-wave (GW) events in low-latency is a crucial element in the search for further multimessenger signals from these cataclysmic events. The localization of these events in low-latency uses signal-to-noise ratio (SNR) time-series from matched-filtered searches which identify candidate events. Here we report on an improvement to the GstLAL-based inspiral pipeline, the low-latency pipeline that identified GW170817 and GW190425, which automates the use of SNRs from all detectors in the network in rapid localization of GW events. This improvement was incorporated into the detection pipeline prior to the recent third observing run of the Advanced LIGO and Advanced Virgo detector network. Previously for this pipeline, manual intervention was required to use SNRs from all detectors if a candidate GW event was below an SNR threshold for any detector in the network. The use of SNRs from subthreshold events can meaningfully decrease the area of the 90% confidence region estimated by rapid localization. To demonstrate this, we present a study of the simulated detections of $\mathcal{O}(2\times10^4)$ binary neutron stars using a network mirroring the second observational run of the Advanced LIGO and Virgo detectors. When incorporating subthreshold SNRs in rapid localization, we find that the fraction of events that can be localized down to $100~\mathrm{deg}^2$ or smaller increases by a factor 1.18.

astro-ph.IM↗

arXiv2020

Incorporation of Statistical Data Quality Information into the GstLAL Search Analysis

We present updates to GstLAL, a matched filter gravitational-wave search pipeline, in Advanced LIGO and Virgo's third observing run. We discuss the incorporation of statistical data quality information into GstLAL's multi-dimensional likelihood ratio ranking statistic and additional improvements to search for gravitational wave candidates found in only one detector. Statistical data quality information is provided by iDQ, a data quality pipeline that infers the presence of short-duration transient noise in gravitational-wave data using the interferometer's auxiliary state, which has operated in near real-time since before LIGO's first observing run in 2015. We look at the performance and impact on noise rejection by the inclusion of iDQ information in GstLAL's ranking statistic, and discuss GstLAL results in the GWTC-2 catalog, focusing on two case studies; GW190424A, a single-detector gravitational-wave event found by GstLAL and a period of time in Livingston impacted by a thunderstorm.

gr-qc↗

arXiv2020

GstLAL: A software framework for gravitational wave discovery

The GstLAL library, derived from Gstreamer and the LIGO Algorithm Library, supports a stream-based approach to gravitational-wave data processing. Although GstLAL was primarily designed to search for gravitational-wave signatures of merging black holes and neutron stars, it has also contributed to other gravitational-wave searches, data calibration, and detector-characterization efforts. GstLAL has played an integral role in all of the LIGO-Virgo collaboration detections, and its low-latency configuration has enabled rapid electromagnetic follow-up for dozens of compact binary candidates.

astro-ph.IM↗

arXiv2020

An early warning system for electromagnetic follow-up of gravitational-wave events

Binary neutron stars (BNSs) will spend $\simeq 10$ -- 15 minutes in the band of Advanced LIGO and Virgo detectors at design sensitivity. Matched-filtering of gravitational-wave (GW) data could in principle accumulate enough signal-to-noise ratio (SNR) to identify a forthcoming event tens of seconds before the companions collide and merge. Here we report on the design and testing of an early warning gravitational-wave detection pipeline. Early warning alerts can be produced for sources that are at low enough redshift so that a large enough SNR accumulates $\sim 10 - 60\,\rm s$ before merger. We find that about 7% (respectively, 49%) of the total detectable BNS mergers will be detected $60\, \rm s$ ($10\, \rm s$) before the merger. About 2% of the total detectable BNS mergers will be detected before merger and localized to within $100\, \rm \text{deg}^2$ (90% credible interval). Coordinated observing by several wide-field telescopes could capture the event seconds before or after the merger. LIGO-Virgo detectors at design sensitivity could facilitate observing at least one event at the onset of merger.

astro-ph.HE↗

arXiv2020

Improving the background estimation technique in the GstLAL inspiral pipeline with the time-reversed template bank

Background estimation is important for determining the statistical significance of a gravitational-wave event. Currently, the background model is constructed numerically from the strain data using estimation techniques that insulate the strain data from any potential signals. However, as the observation of gravitational-wave signals become frequent, the effectiveness of such insulation will decrease. Contamination occurs when signals leak into the background model. In this work, we demonstrate an improved background estimation technique for the searches of gravitational waves (GWs) from binary neutron star coalescences by time-reversing the modeled GW waveforms. We found that the new method can robustly avoid signal contamination at a signal rate of about one per 20 seconds and retain a clean background model in the presence of signals.

astro-ph.IM↗

arXiv2020

A self-consistent method to estimate the rate of compact binary coalescences with a Poisson mixture model

The recently published GWTC-1 - a journal article summarizing the search for gravitational waves (GWs) from coalescing compact binaries in data produced by the LIGO-Virgo network of ground-based detectors during their first and second observing runs - quoted estimates for the rates of binary neutron star, neutron star black hole binary, and binary black hole mergers, as well as assigned probabilities of astrophysical origin for various significant and marginal GW candidate events. In this paper, we delineate the formalism used to compute these rates and probabilities, which assumes that triggers above a low ranking statistic threshold, whether of terrestrial or astrophysical origin, occur as independent Poisson processes. In particular, we include an arbitrary number of astrophysical categories by redistributing, via mass-based template weighting, the foreground probabilities of candidate events, across source classes. We evaluate this formalism on synthetic GW data, and demonstrate that this method works well for the kind of GW signals observed during the first and second observing runs.

astro-ph.HE↗

arXiv2019

Fast evaluation of multi-detector consistency for real-time gravitational wave searches

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 collected. In contrast, subsequent parameter estimation may require hundreds of hours of computational time to analyze the same one second of gravitational wave data. The real-time identification requirement necessitates efficient and often approximate methods for signal analysis. We describe one piece of real-time gravitational-wave identification: an efficient method for ascertaining a signal's consistency between multiple gravitational wave detectors suitable for real-time gravitational wave searches for compact binary mergers. This technique was used in analyses of Advanced LIGO's second observing run and Advanced Virgo's first observing run.

gr-qc↗

arXiv2019

Sub-threshold binary neutron star search in Advanced LIGO's first observing run

We present a search for gravitational waves from double neutron star binaries inspirals in Advanced LIGO's first observing run. The search considers a narrow range of binary chirp masses motivated by the population of known double neutron star binaries in the nearby universe. This search differs from previously published results by providing the most sensitive published survey of neutron stars in Advanced LIGO's first observing run within this narrow mass range and including times when only one of the two LIGO detectors was in operation in the analysis. The search was sensitive to binary neutron star inspirals to an average distance of ~85 Mpc over 93.2 days. We do not identify any unambiguous gravitational wave signals in our sample of 103 sub-threshold candidates with false-alarm-rates of less than one per day. However, given the expected binary neutron star merger rate of R = 100 - 4000 Gpc^(-3) yr^(-1), we expect O(1) gravitational wave events within our candidate list. This suggests the possibility that one or more of these candidates is in fact a binary neutron star merger. Although the contamination fraction in our candidate list is ~99%, it might be possible to correlate these events with other messengers to identify a potential multi-messenger signal. We provide an online candidate list with the times and sky locations for all events in order to enable multi-messenger searches.

gr-qc↗

arXiv2019

The GstLAL Search Analysis Methods for Compact Binary Mergers in Advanced LIGO's Second and Advanced Virgo's First Observing Runs

After their successful first observing run (September 12, 2015 - January 12, 2016), the Advanced LIGO detectors were upgraded to increase their sensitivity for the second observing run (November 30, 2016 - August 26, 2017). The Advanced Virgo detector joined the second observing run on August 1, 2017. We discuss the updates that happened during this period in the GstLAL-based inspiral pipeline, which is used to detect gravitational waves from the coalescence of compact binaries both in low latency and an offline configuration. These updates include deployment of a zero-latency whitening filter to reduce the over-all latency of the pipeline by up to 32 seconds, incorporation of the Virgo data stream in the analysis, introduction of a single-detector search to analyze data from the periods when only one of the detectors is running, addition of new parameters to the likelihood ratio ranking statistic, increase in the parameter space of the search, and introduction of a template mass-dependent glitch-excision thresholding method.

gr-qc↗

arXiv2018

Methods for the detection of gravitational waves from sub-solar mass ultracompact binaries

We describe detection methods for extensions of gravitational wave searches to sub-solar mass compact binaries. Sub-solar mass searches were previously carried out using Initial LIGO, and Advanced LIGO boasts a detection volume approximately 1000 times bigger than Initial LIGO at design sensitivity. Low masses present computational difficulties, and we suggest a way to rein in the increase while retaining a sensitivity much greater than previous searches. Sub-solar mass compact objects are of particular interest because they are not expected to form astrophysically. If detected they could be evidence of primordial black holes (PBH). We consider a particular model of PBH binary formation that would allow LIGO/Virgo to place constraints on this population within the context of dark matter, and we demonstrate how to obtain conservative bounds for the upper limit on the dark matter fraction.

astro-ph.IM↗