Engineering topics

Coughlin, Michael

Publications and source records attributed to Coughlin, Michael.

3 records

DOE OSTI2022

Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learning

The rapidly-developing intersection of machine learning (ML) with high-energy physics (HEP) presents both opportunities and challenges to our community. Far beyond applications of standard ML tools to HEP problems, genuinely new and potentially revolutionary approaches are being developed by a generation of talent literate in both fields. There is an urgent need to support the needs of the interdisciplinary community driving these developments, including funding dedicated research at the intersection of the two fields, investing in high-performance computing at universities and tailoring allocation policies to support this work, developing of community tools and standards, and providing education and career paths for young researchers attracted by the intellectual vitality of machine learning for high energy physics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2022

Hardware-accelerated inference for real-time gravitational-wave astronomy

The field of transient astronomy has seen a revolution with the first gravitational-wave detections and the arrival of multi-messenger observations they enabled. Transformed by the first detection of binary black hole and binary neutron star mergers, computational demands in gravitational-wave astronomy are expected to grow by at least a factor of two over the next five years as the global network of kilometer-scale interferometers are brought to design sensitivity. With the increase in detector sensitivity, real-time delivery of gravitational-wave alerts will become increasingly important as an enabler of multi-messenger followup. In this work, we report a novel implementation and deployment of deep learning inference for real-time gravitational-wave data denoising and astrophysical source identification. This is accomplished using a generic Inference-as-a-Service model that is capable of adapting to the future needs of gravitational-wave data analysis. Overall, our implementation allows seamless incorporation of hardware accelerators and also enables the use of commercial or private (dedicated) as-a-service computing. Based on our results, we propose a paradigm shift in low-latency and offline computing in gravitational-wave astronomy. Such a shift can address key challenges in peak-usage, scalability and reliability, and provide a data analysis platform particularly optimized for deep learning applications. The achieved sub-millisecond scale latency will also be relevant for any machine learning-based real-time control systems that may be invoked in the operation of near-future and next generation ground-based laser interferometers, as well as the front-end collection, distribution and processing of data from such instruments.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRS2016

Going the Distance: Mapping Host Galaxies of LIGO and VIRGO Sources in Three Dimensions using Local Cosmography and Targeted Follow-Up

The Advanced Laser Interferometer Gravitational-wave Observatory (LIGO) discovered gravitational waves (GWs) from a binary black hole merger in 2015 September and may soon observe signals from neutron star mergers. There is considerable interest in searching for their faint and rapidly fading electromagnetic (EM) counterparts, though GW position uncertainties are as coarse as hundreds of square degrees. Because LIGO's sensitivity to binary neutron stars is limited to the local universe, the area on the sky that must be searched could be reduced by weighting positions by mass, luminosity, or star formation in nearby galaxies. Since GW observations provide information about luminosity distance, combining the reconstructed volume with positions and redshifts of galaxies could reduce the area even more dramatically. A key missing ingredient has been a rapid GW parameter estimation algorithm that reconstructs the full distribution of sky location and distance. We demonstrate the first such algorithm, which takes under a minute, fast enough to enable immediate EM follow-up. By combining the three-dimensional posterior with a galaxy catalog, we can reduce the number of galaxies that could conceivably host the event by a factor of 1.4, the total exposure time for the Swift X-ray Telescope by a factor of 2, the total exposure time for a synoptic optical survey by a factor of 2, and the total exposure time for a narrow-field optical telescope by a factor of 3. This encourages us to suggest a new role for small field of view optical instruments in performing targeted searches of the most massive galaxies within the reconstructed volumes.

catalogs â€“ galaxies: distances and redshifts â€“↗