Speaker
Description
The detection of gravitational waves by the Laser Interferometer Gravitational-Wave Observatory (LIGO) has opened a new window onto the universe, but the sensitivity of these detectors is fundamentally limited by a complex and evolving landscape of instrumental and environmental noise. In recent years, machine learning has emerged as a powerful tool for understanding, characterizing, and mitigating these noise sources.
In this talk, I will present current applications of machine learning in LIGO detector characterization, with a focus on transient noise identification and its impact on search analyses. By leveraging techniques from modern computer vision, including object detection and segmentation models such as YOLO, we are able to automatically identify and localize noise artifacts in time–frequency representations of detector data. These approaches enable scalable classification of noise transients, improved data quality vetting, and more robust separation of astrophysical signals from noise, directly enhancing the sensitivity and reliability of gravitational-wave searches.
Looking ahead, I will discuss emerging directions where machine learning can play a transformative role beyond data analysis. These include integrating ML into low-latency pipelines, developing physics-informed models for noise prediction and subtraction, and exploring the use of ML-driven optimization in the design of future interferometers. In particular, data-driven approaches may help guide the selection of design parameters for next-generation cavities and instruments, bridging the gap between detector characterization and instrument development.
Together, these efforts highlight how machine learning is becoming an integral component of both current LIGO operations and the future of gravitational-wave instrumentation.