arXiv subjects

Erik Katsavounidis

Publications and source records attributed to Erik Katsavounidis.

At least 37 records · Page 2Linked to original sources

arXiv2023

Applications of Deep Learning to physics workflows

Modern large-scale physics experiments create datasets with sizes and streaming rates that can exceed those from industry leaders such as Google Cloud and Netflix. Fully processing these datasets requires both sufficient compute power and efficient workflows. Recent advances in Machine Learning (ML) and Artificial Intelligence (AI) can either improve or replace existing domain-specific algorithms to increase workflow efficiency. Not only can these algorithms improve the physics performance of current algorithms, but they can often be executed more quickly, especially when run on coprocessors such as GPUs or FPGAs. In the winter of 2023, MIT hosted the Accelerating Physics with ML at MIT workshop, which brought together researchers from gravitational-wave physics, multi-messenger astrophysics, and particle physics to discuss and share current efforts to integrate ML tools into their workflows. The following white paper highlights examples of algorithms and computing frameworks discussed during this workshop and summarizes the expected computing needs for the immediate future of the involved fields.

hep-ex↗

arXiv2023

QoQ: a Q-transform based test for Gravitational Wave transient events

The observation of transient gravitational waves is hindered by the presence of transient noise, colloquially referred to as glitches. These glitches can often be misidentified as gravitational waves by searches for unmodeled transients using the excess-power type of methods and sometimes even excite template waveforms for compact binary coalescences while using matched filter techniques. They thus create a significant background in the searches. This background is more critical in getting identified promptly and efficiently within the context of real-time searches for gravitational-wave transients. Such searches are the ones that have enabled multi-messenger astrophysics with the start of the Advanced LIGO and Advanced Virgo data taking in 2015 and they will continue to enable the field for further discoveries. With this work we propose and demonstrate the use of a signal-based test that quantifies the fidelity of the time-frequency decomposition of the putative signal based on first principles on how astrophysical transients are expected to be registered in the detectors and empirically measuring the instrumental noise. It is based on the Q-transform and a measure of the occupancy of the corresponding time-frequency pixels over select time-frequency volumes; we call it ``QoQ''. Our method shows a 40% reduction in the number of retraction of public alerts that were issued by the LIGO-Virgo-KAGRA collaborations during the third observing run with negligible loss in sensitivity. Receiver Operator Characteristic measurements suggest the method can be used in online and offline searches for transients, reducing their background significantly.

gr-qc↗

arXiv2023

Searching for Gravitational-Wave Counterparts using the Transiting Exoplanet Survey Satellite

In 2017, the LIGO and Virgo gravitational wave (GW) detectors, in conjunction with electromagnetic (EM) astronomers, observed the first GW multi-messenger astrophysical event, the binary neutron star (BNS) merger GW170817. This marked the beginning of a new era in multi-messenger astrophysics. To discover further GW multi-messenger events, we explore the synergies between the Transiting Exoplanet Survey Satellite (TESS) and GW observations triggered by the LIGO-Virgo-KAGRA Collaboration (LVK) detector network. TESS's extremely wide field of view of ~2300 deg^2 means that it could overlap with large swaths of GW localizations, which can often span hundreds of deg^2 or more. In this work, we use a recently developed transient detection pipeline to search TESS data collected during the LVK's third observing run, O3, for any EM counterparts. We find no obvious counterparts brighter than about 17th magnitude in the TESS bandpass. Additionally, we present end-to-end simulations of BNS mergers, including their detection in GWs and simulations of light curves, to identify TESS's kilonova discovery potential for the LVK's next observing run (O4). In the most optimistic case, TESS will observe up to one GW-found BNS merger counterpart per year. However, TESS may also find up to five kilonovae which did not trigger the LVK network, emphasizing that EM-triggered GW searches may play a key role in future kilonova detections. We also discuss how TESS can help place limits on EM emission from binary black hole mergers, and rapidly exclude large sky areas for poorly localized GW events.

astro-ph.HE↗

arXiv2022

An Isotropy Measurement with Gravitational Wave Observations

We constrain the distribution of merging compact binaries across the celestial sphere using the GWTC-3 catalog from the LIGO-Virgo-KAGRA Collaborations' (LVK) third observing run. With 63 confident detections from O3, we constrain the relative variability (standard deviation) of the rate density across the sky to be $\lesssim 16\%$ at 90\% confidence assuming the logarithm of the rate density is described by a Gaussian random field with correlation length $\geq 10^\circ$. This tightens to $\lesssim 3.5\%$ when the correlation length is $\geq 20^\circ$. While the new O3 data provides the tightest constraints on anisotropies available to-date, we do not find overwhelming evidence in favor of isotropy, either. A simple counting experiment favors an isotropic distribution by a factor of $\mathcal{B}^\mathrm{iso}_\mathrm{ani} = 3.7$, which is nonetheless an improvement of more than a factor of two compared to analogous analyses based on only the first and second observing runs of the LVK.

astro-ph.HE↗

arXiv2022

Physics Community Needs, Tools, and Resources for Machine Learning

Machine learning (ML) is becoming an increasingly important component of cutting-edge physics research, but its computational requirements present significant challenges. In this white paper, we discuss the needs of the physics community regarding ML across latency and throughput regimes, the tools and resources that offer the possibility of addressing these needs, and how these can be best utilized and accessed in the coming years.

cs.LG↗

arXiv2022

An Infrared Search for Kilonovae with the WINTER Telescope. I. Binary Neutron Star Mergers

The Wide-Field Infrared Transient Explorer (WINTER) is a new 1 $\text{deg}^2$ seeing-limited time-domain survey instrument designed for dedicated near-infrared follow-up of kilonovae from binary neutron star (BNS) and neutron star-black hole mergers. WINTER will observe in the near-infrared Y, J, and short-H bands (0.9-1.7 microns, to $\text{J}_{AB}=21$ magnitudes) on a dedicated 1-meter telescope at Palomar Observatory. To date, most prompt kilonova follow-up has been in optical wavelengths; however, near-infrared emission fades more slowly and depends less on geometry and viewing angle than optical emission. We present an end-to-end simulation of a follow-up campaign during the fourth observing run (O4) of the LIGO, Virgo, and KAGRA interferometers, including simulating 625 BNS mergers, their detection in gravitational waves, low-latency and full parameter estimation skymaps, and a suite of kilonova lightcurves from two different model grids. We predict up to five new kilonovae independently discovered by WINTER during O4, given a realistic BNS merger rate. Using a larger grid of kilonova parameters, we find that kilonova emission is $\approx$2 times longer-lived and red kilonovae are detected $\approx$1.5 times further in the infrared than in the optical. For 90% localization areas smaller than 150 (450) $\rm{deg}^{2}$, WINTER will be sensitive to more than 10% of the kilonova model grid out to 350 (200) Mpc. We develop a generalized toolkit to create an optimal BNS follow-up strategy with any electromagnetic telescope and present WINTER's observing strategy with this framework. This toolkit, all simulated gravitational-wave events, and skymaps are made available for use by the community.

astro-ph.HE↗

arXiv2021

Applications and Techniques for Fast Machine Learning in Science

In this community review report, we discuss applications and techniques for fast machine learning (ML) in science -- the concept of integrating power ML methods into the real-time experimental data processing loop to accelerate scientific discovery. The material for the report builds on two workshops held by the Fast ML for Science community and covers three main areas: applications for fast ML across a number of scientific domains; techniques for training and implementing performant and resource-efficient ML algorithms; and computing architectures, platforms, and technologies for deploying these algorithms. We also present overlapping challenges across the multiple scientific domains where common solutions can be found. This community report is intended to give plenty of examples and inspiration for scientific discovery through integrated and accelerated ML solutions. This is followed by a high-level overview and organization of technical advances, including an abundance of pointers to source material, which can enable these breakthroughs.

cs.LG↗

arXiv2021

Hardware-accelerated Inference for Real-Time Gravitational-Wave Astronomy

The field of transient astronomy has seen a revolution with the first gravitational-wave detections and the arrival of multi-messenger observations they enabled. Transformed by the first detection of binary black hole and binary neutron star mergers, computational demands in gravitational-wave astronomy are expected to grow by at least a factor of two over the next five years as the global network of kilometer-scale interferometers are brought to design sensitivity. With the increase in detector sensitivity, real-time delivery of gravitational-wave alerts will become increasingly important as an enabler of multi-messenger followup. In this work, we report a novel implementation and deployment of deep learning inference for real-time gravitational-wave data denoising and astrophysical source identification. This is accomplished using a generic Inference-as-a-Service model that is capable of adapting to the future needs of gravitational-wave data analysis. Our implementation allows seamless incorporation of hardware accelerators and also enables the use of commercial or private (dedicated) as-a-service computing. Based on our results, we propose a paradigm shift in low-latency and offline computing in gravitational-wave astronomy. Such a shift can address key challenges in peak-usage, scalability and reliability, and provide a data analysis platform particularly optimized for deep learning applications. The achieved sub-millisecond scale latency will also be relevant for any machine learning-based real-time control systems that may be invoked in the operation of near-future and next generation ground-based laser interferometers, as well as the front-end collection, distribution and processing of data from such instruments.

gr-qc↗

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

A Coincidence Null Test for Poisson-Distributed Events

When transient events are observed with multiple sensors, it is often necessary to establish the significance of coincident events. We derive a universal null test for an arbitrary number of sensors motivated by the archetypal detection problem for independent Poisson-distributed events in gravitational-wave detectors such as LIGO and Virgo. In these detectors, transient events may be witnessed by myriad channels that record interferometric signals and the surrounding physical environment. We apply our null test to a broad set of simulated gravitational-wave events as well as to a real gravitational-wave detection to determine which auxiliary channels do and do not witness real gravitational waves, and therefore which are safe to use when constructing vetoes. We also describe how our approach can be used to study detector artifacts and their origin, as well as to quantify the statistical independence of candidate GW signals from noise artifacts observed in auxiliary channels.

gr-qc↗

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

iDQ: Statistical Inference of Non-Gaussian Noise with Auxiliary Degrees of Freedom in Gravitational-Wave Detectors

Gravitational-wave detectors are exquisitely sensitive instruments and routinely enable ground-breaking observations of novel astronomical phenomena. However, they also witness non-stationary, non-Gaussian noise that can be mistaken for astrophysical sources, lower detection confidence, or simply complicate the extraction of signal parameters from noisy data. To address this, we present iDQ, a supervised learning framework to autonomously detect noise artifacts in gravitational-wave detectors based only on auxiliary degrees of freedom insensitive to gravitational waves. iDQ has operated in low latency throughout the advanced detector era at each of the two LIGO interferometers, providing invaluable data quality information about each detection to date in real-time. We document the algorithm, describing the statistical framework and possible applications within gravitational-wave searches. In particular, we construct a likelihood-ratio test that simultaneously accounts for the presence of non-Gaussian noise artifacts and utilizes information from both the observed gravitational-wave strain signal and thousands of auxiliary degrees of freedom. We also present several examples of iDQ's performance with modern interferometers, showing iDQ's ability to autonomously reproduce known data quality monitors and identify noise artifacts not flagged by other analyses.

astro-ph.IM↗

arXiv2020

Noise Reduction in Gravitational-wave Data via Deep Learning

With the advent of gravitational wave astronomy, techniques to extend the reach of gravitational wave detectors are desired. In addition to the stellar-mass black hole and neutron star mergers already detected, many more are below the surface of the noise, available for detection if the noise is reduced enough. Our method (DeepClean) applies machine learning algorithms to gravitational wave detector data and data from on-site sensors monitoring the instrument to reduce the noise in the time-series due to instrumental artifacts and environmental contamination. This framework is generic enough to subtract linear, non-linear, and non-stationary coupling mechanisms. It may also provide handles in learning about the mechanisms which are not currently understood to be limiting detector sensitivities. The robustness of the noise reduction technique in its ability to efficiently remove noise with no unintended effects on gravitational-wave signals is also addressed through software signal injection and parameter estimation of the recovered signal. It is shown that the optimal SNR ratio of the injected signal is enhanced by $\sim 21.6\%$ and the recovered parameters are consistent with the injected set. We present the performance of this algorithm on linear and non-linear noise sources and discuss its impact on astrophysical searches by gravitational wave detectors.

astro-ph.IM↗

arXiv2019

Enabling real-time multi-messenger astrophysics discoveries with deep learning

Multi-messenger astrophysics is a fast-growing, interdisciplinary field that combines data, which vary in volume and speed of data processing, from many different instruments that probe the Universe using different cosmic messengers: electromagnetic waves, cosmic rays, gravitational waves and neutrinos. In this Expert Recommendation, we review the key challenges of real-time observations of gravitational wave sources and their electromagnetic and astroparticle counterparts, and make a number of recommendations to maximize their potential for scientific discovery. These recommendations refer to the design of scalable and computationally efficient machine learning algorithms; the cyber-infrastructure to numerically simulate astrophysical sources, and to process and interpret multi-messenger astrophysics data; the management of gravitational wave detections to trigger real-time alerts for electromagnetic and astroparticle follow-ups; a vision to harness future developments of machine learning and cyber-infrastructure resources to cope with the big-data requirements; and the need to build a community of experts to realize the goals of multi-messenger astrophysics.

gr-qc↗

arXiv2019

The US Program in Ground-Based Gravitational Wave Science: Contribution from the LIGO Laboratory

Recent gravitational-wave observations from the LIGO and Virgo observatories have brought a sense of great excitement to scientists and citizens the world over. Since September 2015,10 binary black hole coalescences and one binary neutron star coalescence have been observed. They have provided remarkable, revolutionary insight into the "gravitational Universe" and have greatly extended the field of multi-messenger astronomy. At present, Advanced LIGO can see binary black hole coalescences out to redshift 0.6 and binary neutron star coalescences to redshift 0.05. This probes only a very small fraction of the volume of the observable Universe. However, current technologies can be extended to construct "$3^\mathrm{rd}$ Generation" (3G) gravitational-wave observatories that would extend our reach to the very edge of the observable Universe. The event rates over such a large volume would be in the hundreds of thousands per year (i.e.tens per hour). Such 3G detectors would have a 10-fold improvement in strain sensitivity over the current generation of instruments, yielding signal-to-noise ratios of 1000 for events like those already seen. Several concepts are being studied for which engineering studies and reliable cost estimates will be developed in the next 5 years.

astro-ph.IM↗

arXiv2018

Beyond single-threshold searches: the Event Stacking Test

We present a new statistical test that examines the consistency of the tails of two empirical distributions at multiple thresholds. Such distributions are often encountered in counting experiments, in physics and elsewhere, where the significance of populations of events is evaluated. This multi-threshold approach has the effect of "stacking" multiple events into the tail bin of the distribution, and thus we call it the Event Stacking Test. This test has the ability to confidently detect inconsistencies composed of multiple events, even if these events are low-significance outliers in isolation. We derive the Event Stacking Test from first principles and show that the p-value it reports is a well-calibrated representation of noise fluctuations. When applying this test to the detection of gravitational-wave transients in LIGO-Virgo data, we find that it performs better than or comparably to other statistical tests historically used within the gravitational-wave community. This test is particularly well-suited for detecting classes of gravitational-wave transients that are minimally-modeled, i.e., gravitational-wave bursts. We show that the Event Stacking Test allows us to set upper limits on the astrophysical rate-density of gravitational-wave bursts that are stricter than those set using other statistical tests by factors of up to 2 - 3.

physics.data-an↗

arXiv2018

Multi-Messenger Astrophysics: Harnessing the Data Revolution

The past year has witnessed discovery of the first identified counterparts to a gravitational wave transient (GW 170817A) and a very high-energy neutrino (IceCube-170922A). These source identifications, and ensuing detailed studies, have realized longstanding dreams of astronomers and physicists to routinely carry out observations of cosmic sources by other than electromagnetic means, and inaugurated the era of "multi-messenger" astronomy. While this new era promises extraordinary physical insights into the universe, it brings with it new challenges, including: highly heterogeneous, high-volume, high-velocity datasets; globe-spanning cross-disciplinary teams of researchers, regularly brought together into transient collaborations; an extraordinary breadth and depth of domain-specific knowledge and computing resources required to anticipate, model, and interpret observations; and the routine need for adaptive, distributed, rapid-response observing campaigns to fully exploit the scientific potential of each source. We argue, therefore, that the time is ripe for the community to conceive and propose an Institute for Multi-Messenger Astrophysics that would coordinate its resources in a sustained and strategic fashion to efficiently address these challenges, while simultaneously serving as a center for education and key supporting activities. In this fashion, we can prepare now to realize the bright future that we see, beyond, through these newly opened windows onto the universe.

astro-ph.IM↗

arXiv2018

Observational implications of lowering the LIGO-Virgo alert threshold

The recent detection of the binary-neutron-star merger associated with GW170817 by both LIGO-Virgo and the network of electromagnetic-spectrum observing facilities around the world has made the multi-messenger detection of gravitational-wave events a reality. These joint detections allow us to probe gravitational-wave sources in greater detail and provide us with the possibility of confidently establishing events that would not have been detected in gravitational-wave data alone. In this paper, we explore the prospects of using the electromagnetic follow-up of low-significance gravitational-wave event candidates to increase the sample of confident detections with electromagnetic counterparts. We find that the gravitational-wave alert threshold change that would roughly double the number of detectable astrophysical events would increase the false-alarm rate by more than 5 orders of magnitude from 1 per 100 years to more than 1000 per year. We find that the localization costs of following-up low-significance candidates are marginal, as the same changes to false-alarm rate only increase distance/area localizations by less than a factor of 2 and increase volume localization by less than a factor of 4. We argue that EM follow-up thresholds for low-significance candidates should be set on the basis of alert purity ($P_\text{astro}$) and not false-alarm rate. Ideally, such estimates of $P_\text{astro}$ would be provided by LIGO-Virgo, but in their absence we provide estimates of the average purity of the gravitational-wave candidate alerts issued by LIGO-Virgo as a function of false-alarm rate for various LIGO-Virgo observing epochs.

astro-ph.HE↗