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Article ; Online: Beyond the Horizon Distance: LIGO-Virgo can Boost Gravitational-Wave Detection Rates by Exploiting the Mass Distribution of Neutron Stars.

Bartos, I / Márka, S

2015 Volume 115, Issue 23, Page(s) 231101

Abstract: The masses of neutron stars in neutron star binaries are observed to fall in a narrow mass range around ∼1.33M_{⊙}. We explore the advantage of focusing on this region of the parameter space in gravitational-wave searches. We find that an all-sky ( ... ...

Abstract	The masses of neutron stars in neutron star binaries are observed to fall in a narrow mass range around ∼1.33M_{⊙}. We explore the advantage of focusing on this region of the parameter space in gravitational-wave searches. We find that an all-sky (externally triggered) search with an optimally reduced template bank is expected to detect 14% (61%) more binary mergers than without the reduction. A reduced template bank can also represent significant improvement in technical cost. We also develop a more detailed search method using binary mass distribution, and find a sensitivity increase similar to that due to the reduced template bank.
Language	English
Publishing date	2015-12-04
Publishing country	United States
Document type	Journal Article
ZDB-ID	208853-8
ISSN	1079-7114 ; 0031-9007
ISSN (online)	1079-7114
ISSN	0031-9007
DOI	10.1103/PhysRevLett.115.231101
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Article ; Online: A nearby neutron-star merger explains the actinide abundances in the early Solar System.

Bartos, Imre / Marka, Szabolcs

Nature

2019 Volume 569, Issue 7754, Page(s) 85–88

Abstract: A growing body of evidence indicates that binary neutron-star mergers are the primary origin of heavy elements produced exclusively through rapid neutron ... ...

Abstract	A growing body of evidence indicates that binary neutron-star mergers are the primary origin of heavy elements produced exclusively through rapid neutron capture
Language	English
Publishing date	2019-05-01
Publishing country	England
Document type	Journal Article
ZDB-ID	120714-3
ISSN	1476-4687 ; 0028-0836
ISSN (online)	1476-4687
ISSN	0028-0836
DOI	10.1038/s41586-019-1113-7
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Zs.A 26: Show issues			Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (1.OG) ab Jg. 2022: Lesesaal (EG)
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Article ; Online: AGN as potential factories for eccentric black hole mergers.

Samsing, J / Bartos, I / D'Orazio, D J / Haiman, Z / Kocsis, B / Leigh, N W C / Liu, B / Pessah, M E / Tagawa, H

Nature

2022 Volume 603, Issue 7900, Page(s) 237–240

Abstract: There is some weak evidence that the black hole merger named GW190521 had a non-zero ... ...

Abstract	There is some weak evidence that the black hole merger named GW190521 had a non-zero eccentricity
Language	English
Publishing date	2022-03-09
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	120714-3
ISSN	1476-4687 ; 0028-0836
ISSN (online)	1476-4687
ISSN	0028-0836
DOI	10.1038/s41586-021-04333-1
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Zs.A 26: Show issues			Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (1.OG) ab Jg. 2022: Lesesaal (EG)
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Article ; Online: Infused ice can multiply IceCube's sensitivity.

Bartos, Imre / Marka, Zsuzsa / Marka, Szabolcs

Nature communications

2018 Volume 9, Issue 1, Page(s) 1236

Abstract: The IceCube Neutrino Observatory is the world's largest neutrino detector with a cubic-kilometer instrumented volume at the South Pole. It is preparing for a major upgrade that will significantly increase its sensitivity. A promising technological ... ...

Abstract	The IceCube Neutrino Observatory is the world's largest neutrino detector with a cubic-kilometer instrumented volume at the South Pole. It is preparing for a major upgrade that will significantly increase its sensitivity. A promising technological innovation investigated for this upgrade is wavelength shifting optics. Augmenting sensors with such optics could increase the photo-collection area of IceCube's digital optical modules, and shift the incoming photons' wavelength to where these modules are the most sensitive. Here we investigate the use of IceCube's drill holes as wavelength shifting optics. We calculate the sensitivity enhancement due to increasing the ice's refractive index in the holes, and infusing wavelength-shifting substrate into the ice. We find that, with adequate wavelength-shifter infusion, every ~0.05 increase in the ice's refractive index will increase IceCube's photon sensitivity by 100%, opening the possibility for the substantial, cost-effective expansion of IceCube's reach.
Language	English
Publishing date	2018-03-26
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	2041-1723
ISSN (online)	2041-1723
DOI	10.1038/s41467-018-03693-z
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Article ; Online: Gravitational-wave localization alone can probe origin of stellar-mass black hole mergers.

Bartos, I / Haiman, Z / Marka, Z / Metzger, B D / Stone, N C / Marka, S

Nature communications

2017 Volume 8, Issue 1, Page(s) 831

Abstract: The recent discovery of gravitational waves from stellar-mass binary black hole mergers by the Laser Interferometer Gravitational-wave Observatory opened the door to alternative probes of stellar and galactic evolution, cosmology and fundamental physics. ...

Abstract	The recent discovery of gravitational waves from stellar-mass binary black hole mergers by the Laser Interferometer Gravitational-wave Observatory opened the door to alternative probes of stellar and galactic evolution, cosmology and fundamental physics. Probing the origin of binary black hole mergers will be difficult due to the expected lack of electromagnetic emission and limited localization accuracy. Associations with rare host galaxy types-such as active galactic nuclei-can nevertheless be identified statistically through spatial correlation. Here we establish the feasibility of statistically proving the connection between binary black hole mergers and active galactic nuclei as hosts, even if only a sub-population of mergers originate from active galactic nuclei. Our results are the demonstration that the limited localization of gravitational waves, previously written off as not useful to distinguish progenitor channels, can in fact contribute key information, broadening the range of astrophysical questions probed by binary black hole observations.Binary black hole mergers have recently been observed through the detection of gravitational wave signatures. The authors demonstrate that their association with active galactic nuclei can be made through a statistical spatial correlation.
Language	English
Publishing date	2017-10-10
Publishing country	England
Document type	Journal Article
ISSN	2041-1723
ISSN (online)	2041-1723
DOI	10.1038/s41467-017-00851-7
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: Architectural Optimization and Feature Learning for High-Dimensional Time Series Datasets

Colgan, Robert E. / Yan, Jingkai / Márka, Zsuzsa / Bartos, Imre / Márka, Szabolcs / Wright, John N.

2022

Abstract: As our ability to sense increases, we are experiencing a transition from data-poor problems, in which the central issue is a lack of relevant data, to data-rich problems, in which the central issue is to identify a few relevant features in a sea of ... ...

Abstract	As our ability to sense increases, we are experiencing a transition from data-poor problems, in which the central issue is a lack of relevant data, to data-rich problems, in which the central issue is to identify a few relevant features in a sea of observations. Motivated by applications in gravitational-wave astrophysics, we study the problem of predicting the presence of transient noise artifacts in a gravitational wave detector from a rich collection of measurements from the detector and its environment. We argue that feature learning--in which relevant features are optimized from data--is critical to achieving high accuracy. We introduce models that reduce the error rate by over 60% compared to the previous state of the art, which used fixed, hand-crafted features. Feature learning is useful not only because it improves performance on prediction tasks; the results provide valuable information about patterns associated with phenomena of interest that would otherwise be undiscoverable. In our application, features found to be associated with transient noise provide diagnostic information about its origin and suggest mitigation strategies. Learning in high-dimensional settings is challenging. Through experiments with a variety of architectures, we identify two key factors in successful models: sparsity, for selecting relevant variables within the high-dimensional observations; and depth, which confers flexibility for handling complex interactions and robustness with respect to temporal variations. We illustrate their significance through systematic experiments on real detector data. Our results provide experimental corroboration of common assumptions in the machine-learning community and have direct applicability to improving our ability to sense gravitational waves, as well as to many other problem settings with similarly high-dimensional, noisy, or partly irrelevant data.
Keywords	Computer Science - Machine Learning ; Astrophysics - Instrumentation and Methods for Astrophysics
Subject code	006
Publishing date	2022-02-27
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Boosting the Efficiency of Parametric Detection with Hierarchical Neural Networks

Yan, Jingkai / Colgan, Robert / Wright, John / Márka, Zsuzsa / Bartos, Imre / Márka, Szabolcs

2022

Abstract: Gravitational wave astronomy is a vibrant field that leverages both classic and modern data processing techniques for the understanding of the universe. Various approaches have been proposed for improving the efficiency of the detection scheme, with ... ...

Abstract	Gravitational wave astronomy is a vibrant field that leverages both classic and modern data processing techniques for the understanding of the universe. Various approaches have been proposed for improving the efficiency of the detection scheme, with hierarchical matched filtering being an important strategy. Meanwhile, deep learning methods have recently demonstrated both consistency with matched filtering methods and remarkable statistical performance. In this work, we propose Hierarchical Detection Network (HDN), a novel approach to efficient detection that combines ideas from hierarchical matching and deep learning. The network is trained using a novel loss function, which encodes simultaneously the goals of statistical accuracy and efficiency. We discuss the source of complexity reduction of the proposed model, and describe a general recipe for initialization with each layer specializing in different regions. We demonstrate the performance of HDN with experiments using open LIGO data and synthetic injections, and observe with two-layer models a $79\%$ efficiency gain compared with matched filtering at an equal error rate of $0.2\%$. Furthermore, we show how training a three-layer HDN initialized using two-layer model can further boost both accuracy and efficiency, highlighting the power of multiple simple layers in efficient detection.
Keywords	Astrophysics - Instrumentation and Methods for Astrophysics ; Computer Science - Machine Learning ; General Relativity and Quantum Cosmology
Subject code	006
Publishing date	2022-07-23
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Detecting and Diagnosing Terrestrial Gravitational-Wave Mimics Through Feature Learning

Colgan, Robert E. / Márka, Zsuzsa / Yan, Jingkai / Bartos, Imre / Wright, John N. / Márka, Szabolcs

2022

Abstract: As engineered systems grow in complexity, there is an increasing need for automatic methods that can detect, diagnose, and even correct transient anomalies that inevitably arise and can be difficult or impossible to diagnose and fix manually. Among the ... ...

Abstract	As engineered systems grow in complexity, there is an increasing need for automatic methods that can detect, diagnose, and even correct transient anomalies that inevitably arise and can be difficult or impossible to diagnose and fix manually. Among the most sensitive and complex systems of our civilization are the detectors that search for incredibly small variations in distance caused by gravitational waves -- phenomena originally predicted by Albert Einstein to emerge and propagate through the universe as the result of collisions between black holes and other massive objects in deep space. The extreme complexity and precision of such detectors causes them to be subject to transient noise issues that can significantly limit their sensitivity and effectiveness. In this work, we present a demonstration of a method that can detect and characterize emergent transient anomalies of such massively complex systems. We illustrate the performance, precision, and adaptability of the automated solution via one of the prevalent issues limiting gravitational-wave discoveries: noise artifacts of terrestrial origin that contaminate gravitational wave observatories' highly sensitive measurements and can obscure or even mimic the faint astrophysical signals for which they are listening. Specifically, we demonstrate how a highly interpretable convolutional classifier can automatically learn to detect transient anomalies from auxiliary detector data without needing to observe the anomalies themselves. We also illustrate several other useful features of the model, including how it performs automatic variable selection to reduce tens of thousands of auxiliary data channels to only a few relevant ones; how it identifies behavioral signatures predictive of anomalies in those channels; and how it can be used to investigate individual anomalies and the channels associated with them.
Keywords	Astrophysics - Instrumentation and Methods for Astrophysics ; Computer Science - Machine Learning ; General Relativity and Quantum Cosmology
Subject code	006
Publishing date	2022-03-09
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Search for binary black hole mergers in the third observing run of Advanced LIGO-Virgo using coherent WaveBurst enhanced with machine learning

Mishra, T. / O'Brien, B. / Szczepanczyk, M. / Vedovato, G. / Bhaumik, S. / Gayathri, V. / Prodi, G. / Salemi, F. / Milotti, E. / Bartos, I. / Klimenko, S.

2022

Abstract: In this work, we use the coherent WaveBurst (cWB) pipeline enhanced with machine learning (ML) to search for binary black hole (BBH) mergers in the Advanced LIGO-Virgo strain data from the third observing run (O3). We detect, with equivalent or higher ... ...

Abstract	In this work, we use the coherent WaveBurst (cWB) pipeline enhanced with machine learning (ML) to search for binary black hole (BBH) mergers in the Advanced LIGO-Virgo strain data from the third observing run (O3). We detect, with equivalent or higher significance, all gravitational-wave (GW) events previously reported by the standard cWB search for BBH mergers in the third GW Transient Catalog (GWTC-3). The ML-enhanced cWB search identifies five additional GW candidate events from the catalog that were previously missed by the standard cWB search. Moreover, we identify three marginal candidate events not listed in GWTC-3. For simulated events distributed uniformly in a fiducial volume, we improve the detection efficiency with respect to the standard cWB search by approximately $20\%$ for both stellar-mass and intermediate mass black hole binary mergers, detected with a false-alarm rate less than $1\,\mathrm{yr}^{-1}$. We show the robustness of the ML-enhanced search for detection of generic BBH signals by reporting increased sensitivity to the spin-precessing and eccentric BBH events as compared to the standard cWB search. Furthermore, we compare the improvement of the ML-enhanced cWB search for different detector networks. Comment: 11 pages, 8 figures. arXiv admin note: text overlap with arXiv:2105.04739
Keywords	General Relativity and Quantum Cosmology
Subject code	303
Publishing date	2022-01-05
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Detection prospects for GeV neutrinos from collisionally heated gamma-ray bursts with IceCube/DeepCore.

Bartos, I / Beloborodov, A M / Hurley, K / Márka, S

Physical review letters

2013 Volume 110, Issue 24, Page(s) 241101

Abstract: Jet reheating via nuclear collisions has recently been proposed as the main mechanism for gamma-ray burst (GRB) emission. In addition to producing the observed gamma rays, collisional heating must generate 10-100 GeV neutrinos, implying a close relation ... ...

Abstract	Jet reheating via nuclear collisions has recently been proposed as the main mechanism for gamma-ray burst (GRB) emission. In addition to producing the observed gamma rays, collisional heating must generate 10-100 GeV neutrinos, implying a close relation between the neutrino and gamma-ray luminosities. We exploit this theoretical relation to make predictions for possible GRB detections by IceCube + DeepCore. To estimate the expected neutrino signal, we use the largest sample of bursts observed by the Burst and Transient Source Experiment in 1991-2000. GRB neutrinos could have been detected if IceCube + DeepCore operated at that time. Detection of 10-100 GeV neutrinos would have significant implications, shedding light on the composition of GRB jets and their Lorentz factors. This could be an important target in designing future upgrades of the IceCube + DeepCore observatory.
Language	English
Publishing date	2013-06-14
Publishing country	United States
Document type	Journal Article
ZDB-ID	208853-8
ISSN	1079-7114 ; 0031-9007
ISSN (online)	1079-7114
ISSN	0031-9007
DOI	10.1103/PhysRevLett.110.241101
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