Category: Conferences

The Banff International Research Station will host the “Detection and Analysis of Gravitational Waves in the era of Multi-Messenger Astronomy: From Mathematical Modelling to Machine Learning” workshop in Banff from November 17 - 22, 2024.

Gravitational waves are a new way to explore the sky and uncover the Universe’s deepest mysteries. In the last few years, tens of gravitational-wave detections have allowed scientists to harness the potential of gravitational waves in testing Einstein’s General Relativity theory under extreme conditions, helping to understand the origin of dense matter, measuring the Hubble constant, and estimating the population of black holes in the Universe.

Researchers from all around the world are gathering in Banff to discuss new, recent results from the LIGO, Virgo and KAGRA detectors and the future of gravitational-wave science. The workshop “Detection and analysis of gravitational waves in the era of multi-messenger astronomy: From mathematical modelling to machine learning”, a second in its series, provides a forum, unique in its genre, for discussing new mathematical methods in modelling, detecting, and analyzing gravitational waves, as well as their integration with machine learning and artificial intelligence.

The Banff International Research Station for Mathematical Innovation and Discovery (BIRS) is a collaborative Canada-US venture that provides an environment for creative interaction as well as the exchange of ideas, knowledge, and methods within the Mathematical Sciences, with related disciplines and with industry. The research station is located at The Banff Centre in Alberta and is supported by Canada’s Natural Science and Engineering Research Council (NSERC), the U.S. National Science Foundation (NSF), and the Government of Alberta.

BIRS sits on the traditional territory of the Stoney Nakoda Nations of Wesley, Chiniki, and Bearspaw; three Blackfoot Confederacy nations: the Pikani, Kainai, and Siksika; and the Tsuut’ina First Nations, and is shared with the Métis Nation of Alberta. Before provincial boundaries were established, the Ktunaxa and Maskwacis people lived in this territory as well. For decades, these peoples have contributed to preserve this land, honoring and cherishing it as a place of knowledge and healing. We invite you to follow in their footsteps and join us in celebrating human creativity, cooperation, and learning.

Organizers

Marco Cavaglia (Missouri University of Science and Technology)
Jade Powell (Swinburne University of Technology)
Elena Cuoco (European Gravitational Observatory)
Shaon Ghosh (Montclair State University)

Workshop Website

The first three days will be workshop-style with invited and contributed talks. The last day will be dedicated to technical demonstrations and satellite meetings. The event will be hybrid with an in-person, on-site venue and the possibility to join virtually.  For those attending in person, there will be a social reception during the evening of Tuesday, October 15, and a dinner on Thursday, 17th.

As advances in experimental methods create growing datasets and higher resolution and more complex measurements, machine learning (ML) is rapidly becoming the major tool to analyze complex datasets over many different disciplines. Following the rapid rise of ML through deep learning algorithms, the investigation of processing technologies and strategies to accelerate deep learning and inference is well underway. We envision this will enable a revolution in experimental design and data processing as a part of the scientific method to accelerate discovery greatly. This workshop is aimed at current and emerging methods and scientific applications for deep learning and inference acceleration, including novel methods of efficient ML algorithm design, ultrafast on-detector inference and real-time systems, acceleration as-a-service, hardware platforms, coprocessor technologies, distributed learning, and hyper-parameter optimization.

Abstract submission deadline: September 16th, 2024
Registration deadline: October 1st,2024

Workshop Website

The LISA Consortium’s Simulation and LDC Groups are happy to invite you to the LISA Data Generation and Analysis Workshop, which will take place online October 7-10 2024. It is aimed at beginners to LISA data analysis (with hands-on getting-started sessions), as well as more expert individuals who want to learn more (with in-depth sessions on simulation and analysis of sources of various types).

Hands-on tutorial sessions on how to generate simulated datasets (similar to LDC datasets) and run parameter estimation on them. It will include 101-level sessions, as well as more in-depth sessions. The end-goal is to work towards including multiple types of sources in the mini-global fit pipeline.

The workshop will cover 4 sessions (European afternoons, North-American mornings), including

A 4-hour “getting started” hands-on tutorial, where we will generate of a simple LDC-like dataset with realistic instrumental noise and one MBHB signal, then run an MCMC on it to recover the source parameters. A 3-hour “in-depth simulation” hands-on tutorial, where the parametrization of the instrumental noise and various effects (such as nonstationary noise and data artifcats) will be demonstrated, as well as realistic time-domain EMRI, Galactic binary ensemble, and SGWB signals. Two 3-hour “in-depth analysis” hands-on tutorials, where a mini-global fit will be constructed, including inference for a Galactic binary population, MHBHs, and instrument noise.

This workshop is open to all. However, some tools might have restricted access to LISA Consortium associated or full members.

The workshop is organized by the junior chairs of the LISA Consortium Simulation Expert Group and the LISA Data Challenge Working Group. Many thanks to the speakers, who agreed to help with the organization of the tutorials, and in particular:

• Jean-Baptiste Bayle (University of Glasgow)
• Quentin Baghi (APC)
• Eleonora Castelli (NASA GSFC)
• Natalia Korsakova (APC)
• Christian Chapman-Bird (University of Glasgow)
• Henri Inchauspé (Universität Heidelberg)
• Martin Staab (Observatoire de Paris)
• Maude Le Jeune (APC)
• Michael Katz (NASA Marshall)
• Nikolaos Karnesis (AUTh)
• Olaf Hartwig (AEI Hannover)
• Senwen Deng (APC)
• Sylvain Marsat (L2I Toulouse)

Workshop Website