You only thermoelastically deform once: Point Absorber Detection in LIGO Test Masses with YOLO

TL;DR

This paper introduces a machine learning-based method using YOLO for real-time detection of point absorbers on LIGO test masses, improving monitoring and maintenance of gravitational-wave detectors.

Contribution

The paper presents the first application of YOLO for in situ detection of point absorbers in gravitational-wave observatories, enabling automated and accurate monitoring during operation.

Findings

The algorithm matches human detection of point absorbers with high confidence.

It identifies additional absorbers not previously detected by humans, confirmed by follow-up.

Machine learning enhances the commissioning process of gravitational-wave detectors.

Abstract

Current and future gravitational-wave observatories rely on large-scale, precision interferometers to detect the gravitational-wave signals. However, microscopic imperfections on the test masses, known as point absorbers, cause problematic heating of the optic via absorption of the high-power laser beam, which results in diminished sensitivity, lock loss, or even permanent damage. Consistent monitoring of the test masses is crucial for detecting, characterizing, and ultimately removing point absorbers. We present a machine-learning algorithm for detecting point absorbers based on the object-detection algorithm You Only Look Once (YOLO). The algorithm can perform this task in situ while the detector is in operation. We validate our algorithm by comparing it with past reports of point absorbers identified by humans at LIGO. The algorithm confidently identifies the same point absorbers as humans with minimal false positives. It also identifies some point absorbers previously not identified by humans, which we confirm with human follow-up. We highlight the potential of machine learning in commissioning efforts.