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The breakthrough discovery of Gravitational Waves (GWs) on September 14, 2015, was possible through the synergy of techniques drawing from expertise in physics, mathematics, information science and computing. The community nurtured by the CA17137 G2net COST Action is exploring, building on past work, the tremendous opportunity of the systematic application of Machine Learning (ML), Artificial Intelligence (AI) and Robotics to GW detection and Geophysics.
In this workshop we will show the results obtained so far by the working groups of the COST Action CA17137. Furthermore, we will discuss the state-of-the-art, as well as future challenges, of Machine Learning applied to gravitational wave research. The program will also include contributions from machine learning experts in different research areas.
There is no fee for participation. The workshop will be held in hybrid format, with participation in presence at the European Gravitational Observatory and remote connection with Zoom.
Link for remote connection: Zoom link.
The field of AI research finds itself at an interesting decision point: to exploit promising solutions that rose to prominence over the past few years and scale them up to more and more powerful generalist models, or to explore new unknown areas? Do we already have all the basic ingredients we need? This talk will describe some of the currently most popular architectures (Transformers, Perceivers, Graph Neural Nets, Diffusion, etc.), discuss the multimodal frontier, and highlight some success stories in technical application domains such as code generation, algorithmic reasoning, and the physical science, and explore results and open questions in the fundamental understanding of these models (e.g. scaling laws, interpretability, compression). This overview aims to provide an introduction to exciting topics in AI research that may be relevant to this community, under the lens of a data-driven scaling approach.
The state-of-the-art of gravitational wave (GW) search techniques for transient signals have been extremely successful, but their sensitivity continues to be hindered by the presence of transient noise artifacts in the detectors, known as glitches. Glitches happen at a rate of 1 per min reducing the amount of scientific data available, as well as masking or mimicking GW signals. Therefore, there is a need for better modeling and inclusion of glitches, as well as improving the robustness of future GW searches. In this presentation we tackle two different challenges employing Machine Learning techniques: firstly we further analyze glitches populations with Generative Adversarial Networks, and secondly we ameliorate glitches in GW searches by analysing pipelines triggers with Gaussian Process classifier.