9th -13 MARCH 2020, UNIVERSITY OF MALTA VALETTA CAMPUS, MALTA
CA17137 action description
The breakthrough discovery of gravitational waves on September 14, 2015 was made possible through synergy of techniques drawing from expertise in physics, mathematics, information science and computing. At present, there is a rapidly growing interest in Machine Learning (ML), Deep Learning (DL), classification problems, data mining and visualization and, in general, in the development of new techniques and algorithms for efficiently handling the complex and massive data sets found in what has been coined “Big Data”, across a broad range of disciplines, ranging from Social Sciences to Natural Sciences. The rapid increase in computing power at our disposal and the development of innovative techniques for the rapid analysis of data will be vital to the exciting new field of Gravitational Wave (GW) Astronomy, on specific topics such as control and feedback systems for next-generation detectors, noise removal, data analysis and data-conditioning tools.The discovery of GW signals from colliding binary black holes (BBH) and the likely existence of a newly observable population of massive, stellar-origin black holes, has made the analysis of low-frequency GW data a crucial mission of GW science. The low-frequency performance of Earth-based GW detectors is largely influenced by the capability of handling ambient seismic noise suppression. This Cost Action aims at creating a broad network of scientists from four different areas of expertise, namely GW physics, Geophysics, Computing Science and Robotics, with a common goal of tackling challenges in data analysis and noise characterization for GW detectors.
Training School Objective
This training school is targeted towards scientists with expertise in Gravitational Wave Physics, Geophysics and Computing Science.
Start: March 9. 2020 – 08:00
Finish: March 13, 2020 – 18:00
Comments from participants
It was a great pleasure to participate in this training school. Impressive topics, outstanding lecturers, and amazing networking. It was impactful and useful for me and I hope for everyone being there. Thanks to organizers for the great opportunity and making this school such a fruitful meeting. Also thanks for gathering us together. Special thanks to the COST program for its invaluable contribution to researchers’ training and networking. I appreciate the courage of organizers for continuing the school remotely after partial cancellation due to COVID2019 and the move of the last two days and the hackathon online.
The Malta training school was organised in a fantastic location, with a remarkable perseverance of all involved, especially the local organizer Christopher Zerafa. Halfway through the school however we had to switch to online working, as Malta closed the university to prevent the virus spread and flights to a number of countries were cancelled. I remain with a regret that we could not interact longer. I am sure though that the contacts we established with each other in early March in Malta will carry on, consolidated perhaps also by the fact that we did all we could to reach our school goals – training and and hackaton.
A positive experience that I’d recommend to anyone who is interested in using machine learning to enhance their research.
I have had a great personal and academic experience. I want to congratulate the coordinators for their task and also for the help we received.
José Joaquín Moll Crespo
Despite the very stressful circumstances, the organizers have flexibly reorganized the school as well as possible. The lectures were a bit more elementary than I expected. A very interesting part was the hackathon, which provided a challenge as well as a great learning opportunity. The overall experience was both enjoyable and educational.
The school was organized professionally, well planned and thought-out. If it wasn’t the unfortunate coronavirus situation, it would be much better, but this was out of anyone’s hands.