1,721,055 research outputs found
Application of dictionary learning to denoise LIGO's blip noise transients
No full textData streams of gravitational-wave detectors are polluted by transient noise features, or “glitches,” of
instrumental and environmental origin. In this work we investigate the use of total variation methods
and learned dictionaries to mitigate the effect of those transients in the data. We focus on a specific type of
transient, “blip" glitches, as this is the most common type of glitch present in the LIGO detectors and their
waveforms are easy to identify. We randomly select 100 blip glitches scattered in the data from advanced
LIGO’s O1 run, as provided by the citizen-science project Gravity Spy. Our results show that dictionarylearning methods are a valid approach to model and subtract most of the glitch contribution in all cases
analyzed, particularly at frequencies below ∼1 kHz. The high-frequency component of the glitch is best
removed when a combination of dictionaries with different atom length is employed. As a further example
we apply our approach to the glitch visible in the LIGO-Livingston data around the time of merger of
binary neutron star signal GW170817, finding satisfactory results. This paper is the first step in our ongoing
program to automatically classify and subtract all families of gravitational-wave glitches employing
variational method
Total-variation methods for gravitational-wave denoising: Performance tests on Advanced LIGO data
No full textWe assess total-variation methods to denoise gravitational-wave signals in real noise conditions by injecting numerical-relativity waveforms from core-collapse supernovae and binary black hole mergers in data from the first observing run of Advanced LIGO. This work is an extension of our previous investigation in which only Gaussian noise was used. Since the quality of the results depends on the regularization parameter of the model, we perform a heuristic search for the value that produces the best results. We discuss various approaches for the selection of this parameter, based on the optimal, mean, or multiple values, and compare the results of the denoising upon these choices. Moreover, we also present a machine-learning-informed approach to obtain the Lagrange multiplier of the method through an automatic search. Our results provide further evidence that total-variation methods can be useful in the field of gravitational-wave astronomy as a tool to remove noise
Classification methods for noise transients in advanced gravitational-wave detectors II: performance tests on Advanced LIGO data
No full textThe data taken by the advanced LIGO and Virgo gravitational-wave detectors contains short duration noise transients that limit the significance of astrophysical detections and reduce the duty cycle of the instruments. As the advanced detectors are reaching sensitivity levels that allow for multiple detections of astrophysical gravitational-wave sources it is crucial to achieve a fast and accurate characterization of non-astrophysical transient noise shortly after it occurs in the detectors. Previously we presented three methods for the classification of transient noise sources. They are Principal Component Analysis for Transients (PCAT), Principal Component LALInference Burst (PC-LIB) and Wavelet Detection Filter with Machine Learning (WDF-ML). In this study we carry out the first performance tests of these algorithms on gravitational-wave data from the Advanced LIGO detectors. We use the data taken between the 3rd of June 2015 and the 14th of June 2015 during the 7th engineering run (ER7), and outline the improvements made to increase the performance and lower the latency of the algorithms on real data. This work provides an important test for understanding the performance of these methods on real, non stationary data in preparation for the second advanced gravitational-wave detector observation run, planned for later this year. We show that all methods can classify transients in non stationary data with a high level of accuracy and show the benefits of using multiple classifiers
Strategy for signal classification to improve data quality for Advanced Detectors gravitational-wave searches
No full textNoise of non-astrophysical origin contaminates science data taken by
the Advanced Laser Interferometer Gravitational-wave Observatory and Advanced
Virgo gravitational-wave detectors. Characterization of instrumental and environmental
noise transients has proven critical in identifying false positives in the first
aLIGO observing run O1. In this talk, we present three algorithms designed for
the automatic classification of non-astrophysical transients in advanced detectors.
Principal Component Analysis for Transients (PCAT) and an adaptation of LALInference
Burst (PC-LIB) are based on Principal Component Analysis. The third
algorithm is a combination of a glitch finder called Wavelet Detection Filter (WDF)
and unsupervised machine learning techniques for classification
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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