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 $m_1 = 50.7^{+10.4}_{-8.9}\,M_{\odot}$ and $m_2 = 24.4^{+20.2}_{-9.3}\,M_{\odot}$ at the luminosity distance of $d_L = 564^{+812}_{-338}\,\mathrm{Mpc}$.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