2,711 research outputs found
Deep Multi-view Models for Glitch Classification
Non-cosmic, non-Gaussian disturbances known as "glitches", show up in
gravitational-wave data of the Advanced Laser Interferometer Gravitational-wave
Observatory, or aLIGO. In this paper, we propose a deep multi-view
convolutional neural network to classify glitches automatically. The primary
purpose of classifying glitches is to understand their characteristics and
origin, which facilitates their removal from the data or from the detector
entirely. We visualize glitches as spectrograms and leverage the
state-of-the-art image classification techniques in our model. The suggested
classifier is a multi-view deep neural network that exploits four different
views for classification. The experimental results demonstrate that the
proposed model improves the overall accuracy of the classification compared to
traditional single view algorithms.Comment: Accepted to the 42nd IEEE International Conference on Acoustics,
Speech and Signal Processing (ICASSP'17
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
Image-based deep learning for classification of noise transients in gravitational wave detectors
The detection of gravitational waves has inaugurated the era of gravitational
astronomy and opened new avenues for the multimessenger study of cosmic
sources. Thanks to their sensitivity, the Advanced LIGO and Advanced Virgo
interferometers will probe a much larger volume of space and expand the
capability of discovering new gravitational wave emitters. The characterization
of these detectors is a primary task in order to recognize the main sources of
noise and optimize the sensitivity of interferometers. Glitches are transient
noise events that can impact the data quality of the interferometers and their
classification is an important task for detector characterization. Deep
learning techniques are a promising tool for the recognition and classification
of glitches. We present a classification pipeline that exploits convolutional
neural networks to classify glitches starting from their time-frequency
evolution represented as images. We evaluated the classification accuracy on
simulated glitches, showing that the proposed algorithm can automatically
classify glitches on very fast timescales and with high accuracy, thus
providing a promising tool for online detector characterization.Comment: 25 pages, 8 figures, accepted for publication in Classical and
Quantum Gravit
Explaining the GWSkyNet-Multi machine learning classifier predictions for gravitational-wave events
GWSkyNet-Multi is a machine learning model developed for classification of
candidate gravitational-wave events detected by the LIGO and Virgo
observatories. The model uses limited information released in the low-latency
Open Public Alerts to produce prediction scores indicating whether an event is
a merger of two black holes, a merger involving a neutron star, or a
non-astrophysical glitch. This facilitates time sensitive decisions about
whether to perform electromagnetic follow-up of candidate events during
LIGO-Virgo-KAGRA (LVK) observing runs. However, it is not well understood how
the model is leveraging the limited information available to make its
predictions. As a deep learning neural network, the inner workings of the model
can be difficult to interpret, impacting our trust in its validity and
robustness. We tackle this issue by systematically perturbing the model and its
inputs to explain what underlying features and correlations it has learned for
distinguishing the sources. We show that the localization area of the 2D sky
maps and the computed coherence versus incoherence Bayes factors are used as
strong predictors for distinguishing between real events and glitches. The
estimated distance to the source is further used to discriminate between binary
black hole mergers and mergers involving neutron stars. We leverage these
findings to show that events misclassified by GWSkyNet-Multi in LVK's third
observing run have distinct sky area, coherence factor, and distance values
that influence the predictions and explain these misclassifications. The
results help identify the model's limitations and inform potential avenues for
further optimization.Comment: 22 pages, 11 figures, submitted to Ap
Deep learning para a classificação de ruídos transitórios e sinais nos detetores LIGO
In this work, data from the aLIGO detectores collected during the first two aLIGO
and AdV observing runs (O1 and O2), in the form of spectrograms, were classified
using Deep Learning models based on Convolutional Neural Networks. As well as
training models from scratch, pre-trained models were also employed, and their
performance compared.
Initially, a brief theoretical introduction on gravitational wave detection was performed,
focusing on the LIGO detectors. In addition, the foundations of Deep
Learning and current best practices for the training of image classification models
were also presented.
The computational experiments showed that encoding information from different
time windows in the different colour channels enhanced the performance of the
models and that small architectures were capable of separating the 22 classes
present in the Gravity Spy dataset. Moreover, transfer learning was able to accelerate
the training process and achieve classifiers with competitive performance.
The best models obtained a macro-averaged F1 score of 96.84% (fine-tuned model)
and 97.18% (baseline trained from scratch), which are in line with the best results
in the literature for the same dataset. In addition, these models were evaluated on
real gravitational wave signals from Compact Binary Coalescences from the first
two aLIGO and AdV observing runs, and they achieved recalls of 75% and 25%,
respectively, while only having been trained with a small number of signals from
gravitational wave simulations.Neste trabalho, dados dos detetores aLIGO recolhidos nos dois primeiros períodos
de observação de LIGO e Virgo (O1 e O2), na forma de espectrogramas, foram
classificados usando modelos de Deep Learning baseados em redes neuronais convolucionais.
Além de serem usados modelos treinados do zero, também se testaram
modelos pré-treinados, e os resultados foram comparados.
Para isso, começou por se fazer uma breve introdução às ondas gravitacionais e
sua deteção nos detetores de LIGO. Foram também introduzidos os fundamentos
relacionados com algoritmos de Deep Learning e das boas práticas para o treino
de modelos para a classificação de imagens.
Verificou-se que usar os diferentes canais de cor das imagens para apresentar informação com diferentes janelas temporais melhora os resultados dos modelos e
que, além disso, arquiteturas pequenas são capazes de separar eficazmente as 22
classes presentes no dataset Gravity Spy. Adicionalmente, a técnica de transfer learning
permite acelerar a fase de treino e obter classificadores com um desempenho
competitivo.
Os melhores modelos obtiveram um F1-score médio (macro) de 96.84% para o
modelo pré-treinado e de 97.18% para o modelo base treinado do zero. Estes
resultados estão em linha com os melhores resultados encontrados na literatura
para o mesmo dataset. Adicionalmente, os modelos foram testados em sinais
reais de ondas gravitacionais de Coalescências Binárias Compactas detetadas por
LIGO, obtendo sensibilidades de, respetivamente, 25% e 75%, apesar de terem sido
treinados com um número reduzido de sinais provenientes de simulações de ondas
gravitacionais.Mestrado em Engenharia Físic
Neural network time-series classifiers for gravitational-wave searches in single-detector periods
The search for gravitational-wave signals is limited by non-Gaussian
transient noises that mimic astrophysical signals. Temporal coincidence between
two or more detectors is used to mitigate contamination by these instrumental
glitches. However, when a single detector is in operation, coincidence is
impossible, and other strategies have to be used. We explore the possibility of
using neural network classifiers and present the results obtained with three
types of architectures: convolutional neural network, temporal convolutional
network, and inception time. The last two architectures are specifically
designed to process time-series data. The classifiers are trained on a month of
data from the LIGO Livingston detector during the first observing run (O1) to
identify data segments that include the signature of a binary black hole
merger. Their performances are assessed and compared. We then apply trained
classifiers to the remaining three months of O1 data, focusing specifically on
single-detector times. The most promising candidate from our search is
2016-01-04 12:24:17 UTC. Although we are not able to constrain the significance
of this event to the level conventionally followed in gravitational-wave
searches, we show that the signal is compatible with the merger of two black
holes with masses and at the luminosity distance of .Comment: 29 pages, 11 figures, submitted to CQ
Giant star seismology
The internal properties of stars in the red-giant phase undergo significant
changes on relatively short timescales. Long near-uninterrupted high-precision
photometric timeseries observations from dedicated space missions such as CoRoT
and Kepler have provided seismic inferences of the global and internal
properties of a large number of evolved stars, including red giants. These
inferences are confronted with predictions from theoretical models to improve
our understanding of stellar structure and evolution. Our knowledge and
understanding of red giants have indeed increased tremendously using these
seismic inferences, and we anticipate that more information is still hidden in
the data. Unraveling this will further improve our understanding of stellar
evolution. This will also have significant impact on our knowledge of the Milky
Way Galaxy as well as on exo-planet host stars. The latter is important for our
understanding of the formation and structure of planetary systems.Comment: Invited review for The Astronomy and Astrophysics Review, accepted
for publicatio
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