AI for Gravitational Waves Workshop @ CERN

Session

AI for detector operations

May 7, 2026, 3:20 PM

40/S2-A01 - Salle Anderson (CERN)

Conveners

AI for detector operations

• Jan Harms (INFN - National Institute for Nuclear Physics)

AI for detector operations

• There are no conveners in this block

17. From Post Hoc Subtraction to Source Suppression: ML Noise Mitigation in Advanced LIGO

Christina Reissel (Massachusetts Inst. of Technology (US))

5/7/26, 3:20 PM

AI for Detector Operations

Talk

We present our work on machine learning for noise mitigation in Advanced LIGO that moves from software denoising of the strain channel to suppression of disturbances at their source within the detector control system.
Using Coherence DeepClean, we perform coherence-based witness-channel selection followed by machine-learning regression to subtract linear and nonlinear noise couplings from...

29. Non-linear noise regression in the Virgo detector

R. Weizmann Kiendrebeogo (IRFU, CEA, Universit´e Paris-Saclay, F-91191 Gif-sur-Yvette, France)

5/7/26, 3:40 PM

AI for Detector Operations

Talk

Since the first detection of gravitational waves from a binary black hole merger, hundreds of such events have been observed. However, many compact binary coalescence signals remain buried below the detector noise threshold and could be recovered through improved noise mitigation. Detector noise arises from multiple sources, including fundamental, technical, and environmental contributions...

27. Tools for real-time inferences in GW detectors

Sebastien Viret (Centre National de la Recherche Scientifique (FR))

5/7/26, 4:00 PM

AI for Detector Operations

Talk

Machine learning will play a key role in the next generation of interferometers data acquisition architectures, particularly through hardware-based solutions. However, methods deployed will have to meet very specific simplicity and robustness requirements. We will present those constraints and the tools we are currently developing to fulfill them at different hardware stages:

• TolmNet:...

22. ★ Computational Discovery of Interferometric Gravitational Wave Detectors ★

Jonathan Klimesch (University of Tübingen)

5/7/26, 4:20 PM

AI for Detector Operations

Talk

Current and next-generation gravitational wave detectors are designed by human experts who must balance coupled physical effects across many domains. The vast space of all possible experiment designs suggests that many high-sensitivity, unconventional detectors may lie beyond the reach of human intuition alone. AI-based methods are increasingly capable of discovering powerful measurement...

23. Deep Loop Shaping for Angular Sensing and Control in Virgo

Tomislav Andric (Gran Sasso Science Institute & INFN-LNGS)

5/7/26, 4:40 PM

AI for Detector Operations

Talk

Gravitational-wave interferometers rely on hundreds of feedback control loops to stabilize mirror alignment and maintain detector sensitivity. These control systems can inject noise into the observation band, limiting low-frequency performance. Recently, the Deep Loop Shaping (DLS) approach demonstrated that reinforcement learning (RL) can substantially reduce injected control noise in the...

32. GlitchFlow: generative neural networks for de-noising gravitaional waves data in present and future detectors.

Francesco Sarandrea

5/8/26, 11:30 AM

AI for Detector Operations

Talk

Third-generation (3G) gravitational waves (GW) detectors such as Einstein Telescope and Cosmic Explorer are expected to increase annual GW detections by a factor of 1000, enabling the detection of every stellar-mass black hole merger in the Universe. The growing volume and complexity of data generated by these observatories necessitate the application of data science techniques,...

62. Operations of GW alert validation in O4

Sarah ANTIER (IJCLAB)

5/8/26, 11:50 AM

AI for Detector Operations

Talk

In my talk, I would like to discuss the role of large language models (LLMs) in supporting GW candidate validation during O5 observing run, in complement to shifts rota that were in place to provide reliability to the follow-up and mitigate the risk to consume telescope ressources for no reason. While human-in-the-loop validation has remained critical over the last campaigns, we are raising...

66. Deployable Reinforcement Learning for Interferometric Control

Nikhil Mukund (MIT)

5/8/26, 12:10 PM

AI for Detector Operations

Talk

Gravitational-wave detectors present a difficult control problem. They are strongly coupled, nonstationary, and safety-critical, and there is very little room to learn directly on real hardware. In this talk, I will describe a practical approach to reinforcement learning for interferometric sensing and control, drawing on lessons from both kilometer-scale interferometers and tabletop optical...