19 research outputs found
Gravity Spy: Integrating Advanced LIGO Detector Characterization, Machine Learning, and Citizen Science
(abridged for arXiv) With the first direct detection of gravitational waves,
the Advanced Laser Interferometer Gravitational-wave Observatory (LIGO) has
initiated a new field of astronomy by providing an alternate means of sensing
the universe. The extreme sensitivity required to make such detections is
achieved through exquisite isolation of all sensitive components of LIGO from
non-gravitational-wave disturbances. Nonetheless, LIGO is still susceptible to
a variety of instrumental and environmental sources of noise that contaminate
the data. Of particular concern are noise features known as glitches, which are
transient and non-Gaussian in their nature, and occur at a high enough rate so
that accidental coincidence between the two LIGO detectors is non-negligible.
In this paper we describe an innovative project that combines crowdsourcing
with machine learning to aid in the challenging task of categorizing all of the
glitches recorded by the LIGO detectors. Through the Zooniverse platform, we
engage and recruit volunteers from the public to categorize images of glitches
into pre-identified morphological classes and to discover new classes that
appear as the detectors evolve. In addition, machine learning algorithms are
used to categorize images after being trained on human-classified examples of
the morphological classes. Leveraging the strengths of both classification
methods, we create a combined method with the aim of improving the efficiency
and accuracy of each individual classifier. The resulting classification and
characterization should help LIGO scientists to identify causes of glitches and
subsequently eliminate them from the data or the detector entirely, thereby
improving the rate and accuracy of gravitational-wave observations. We
demonstrate these methods using a small subset of data from LIGO's first
observing run.Comment: 27 pages, 8 figures, 1 tabl
Classifying the unknown: discovering novel gravitational-wave detector glitches using similarity learning
The observation of gravitational waves from compact binary coalescences by
LIGO and Virgo has begun a new era in astronomy. A critical challenge in making
detections is determining whether loud transient features in the data are
caused by gravitational waves or by instrumental or environmental sources. The
citizen-science project \emph{Gravity Spy} has been demonstrated as an
efficient infrastructure for classifying known types of noise transients
(glitches) through a combination of data analysis performed by both citizen
volunteers and machine learning. We present the next iteration of this project,
using similarity indices to empower citizen scientists to create large data
sets of unknown transients, which can then be used to facilitate supervised
machine-learning characterization. This new evolution aims to alleviate a
persistent challenge that plagues both citizen-science and instrumental
detector work: the ability to build large samples of relatively rare events.
Using two families of transient noise that appeared unexpectedly during LIGO's
second observing run (O2), we demonstrate the impact that the similarity
indices could have had on finding these new glitch types in the Gravity Spy
program
Classifying the unknown: Discovering novel gravitational-wave detector glitches using similarity learning
The observation of gravitational waves from compact binary coalescences by LIGO and Virgo has begun a new era in astronomy. A critical challenge in making detections is determining whether loud transient features in the data are caused by gravitational waves or by instrumental or environmental sources. The citizen-science project Gravity Spy has been demonstrated as an efficient infrastructure for classifying known types of noise transients (glitches) through a combination of data analysis performed by both citizen volunteers and machine learning. We present the next iteration of this project, using similarity indices to empower citizen scientists to create large data sets of unknown transients, which can then be used to facilitate supervised machine-learning characterization. This new evolution aims to alleviate a persistent challenge that plagues both citizen-science and instrumental detector work: the ability to build large samples of relatively rare events. Using two families of transient noise that appeared unexpectedly during LIGO’s second observing run, we demonstrate the impact that the similarity indices could have had on finding these new glitch types in the Gravity Spy program
An overview of burst, buckling, durability and corrosion analysis of lightweight FRP composite pipes and their applicability
© 2019 Elsevier Ltd. All rights reserved.The main aim of this review article was to address the performance of filament wound fibre reinforced polymer (FRP) composite pipes and their critical properties, such as burst, buckling, durability and corrosion. The importance of process parameters concerning merits and demerits of the manufacturing methods was discussed for the better-quality performance. Burst analysis revealed that the winding angle of ±55° was observed to be optimum with minimum failure mechanisms, such as matrix cracking, whitening, leakage and fracture. The reduction of buckling effect was reported in case of lower hoop stress value in the hoop to axial stress ratio against axial, compression and torsion. A significant improvement in energy absorption was observed in the hybrid composite pipes with the effect of thermal treatment. However, the varying winding angle in FRP pipe fabrication was reported as an influencing factor affecting all the aforementioned properties. Almost 90% of the reviewed studies was done using E-glass/epoxy materials for the composite pipe production. By overcoming associated limitations, such as replacing synthetic materials, designing new material combinations and cost-benefit analysis, the production cost of the lightweight FRP composite pipes can be decreased for the real-time applications.Peer reviewe