Gravity Spy: Integrating Advanced LIGO Detector Characterization, Machine Learning, and Citizen Science (1611.04596v2)

Abstract: (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.

• The paper introduces an interdisciplinary approach that combines machine learning with citizen science to efficiently classify noise glitches in Advanced LIGO data.
• It employs a deep CNN trained on 7,718 curated glitches, achieving an impressive 97.1% accuracy in distinguishing glitch patterns.
• The integration of human insights and algorithmic processing significantly improves data quality, bolstering the reliability of gravitational wave detections.

Overview of "Gravity Spy: Integrating Advanced LIGO Detector Characterization, Machine Learning, and Citizen Science"

The paper "Gravity Spy: Integrating Advanced LIGO Detector Characterization, Machine Learning, and Citizen Science" addresses a fundamental challenge in gravitational wave astronomy: the efficient and accurate classification of transient noise artifacts, known as glitches, in data from the Advanced LIGO (aLIGO) detectors. Glitches, originating from various instrumental and environmental noise sources, can significantly obscure or mimic gravitational wave signals. As LIGO's sensitivity continues to improve, alleviating the impact of these glitches becomes crucial for maximizing detection rates and ensuring data quality.

Core Contributions

This research introduces the Gravity Spy project, an interdisciplinary effort combining crowdsourcing through citizen science, machine learning techniques, and gravitational physics for enhanced glitch classification and elimination. The project leverages the Zooniverse platform, engaging volunteers to classify time-frequency graphs of glitches into predefined morphological categories, while also discovering new categories as aLIGO evolves. The machine learning component employs a deep Convolutional Neural Network (CNN) architecture to classify glitches, trained on large datasets labeled by human volunteers. This symbiotic relationship between human classification and machine learning allows the system to efficiently scale, adapting to new forms of glitches that emerge as the detectors are further refined.

Methodology

Data preparation tailored the input selection, focusing on glitches occurring during observing runs, above specific signal-to-noise ratios (SNR), and within the detectors' sensitive frequency bands. Glitches are visualized using Omega Scans representing time-frequency-energy space, ideal for the human eye and the machine learning algorithms. A training set of approximately 7,718 curated glitches was used to develop initial machine learning models, achieving a promising accuracy of 97.1% with precision and recall nearing unity across most glitch classes.

Volunteers on the Zooniverse platform participate in the classification through a dynamic interface that evolves with their skill level, starting with simple tasks and progressing to more complex classifications. The interface incorporates feedback mechanisms and tracks performance to optimize the volunteer experience and the reliability of the results. Importantly, this dual-model system (human and machine classification) enhances the accuracy and efficiency of glitch characterization.

Results and Implications

Preliminary results indicate that the system performs well with existing glitch morphologies and has already identified new glitch classes during beta testing, such as "Paired Doves" and "Helix," which are relevant to LIGO's data quality campaigns. Citizen scientists demonstrated the ability to recognize complex patterns, which aided in expanding the catalog of glitch classes.

The methodological novelty lies in the integration of human pattern recognition prowess with the scalability and speed of machine learning algorithms. This integration is expected to provide a lasting impact on LIGO's quest to detect and analyze gravitational waves more effectively.

Future Prospects

Looking ahead, the Gravity Spy project promises ongoing contributions to LIGO's data analysis by continually updating glitch characterizations and enhancing the accuracy of gravitational wave observations. The innovative convergence of citizen science with modern machine learning techniques represents a template for future projects dealing with large-scale data. The collaborative aspects of the project also provide insights into the socio-computational dynamics of leveraging human expertise alongside automated processing.

In conclusion, this paper presents a comprehensive strategy for improving the detection capabilities of aLIGO, prioritizing the minimization of noise through a robust framework integrating community-driven science and advanced computational methods. This approach holds significant potential for application across various domains needing large-scale data interpretation and analysis.