arXiv subjects

John Towns

Publications and source records attributed to John Towns.

5 recordsLinked to original sources

arXiv2025

Quantitative analysis of the value of investment in research facilities, with examples from cyberinfrastructure

Purpose: How much to invest in research facilities has long been a question in higher education and research policy. We present established and recently developed techniques for assessing the quantitative value created or received as a result of investments in research facilities. This discussion is timely. Financial challenges in higher education may soon force difficult decisions regarding investment in research facilities at some institutions. Clear quantitative analysis will be necessary for such strategic decision-making. Further, institutions of higher education in the USA are currently being called on to justify their value to society. The analyses presented here are extendable to research enterprises as a whole. Results: We present methods developed primarily for analyses of cyberinfrastructure. Most analyses comparing investment in university-based cyberinfrastructure facilities with purchasing services from commercial sources demonstrate positive results for economic and scientific research. A recent assessment, based on a comprehensive accounting approach, has shown that for one large publicly funded cyberinfrastructure project the value delivered to the USA economy and society exceeded the cost to USA taxpayers. Conclusions: Quantitative analyses of the benefits of investment in research and research facilities create a fact-based foundation for discussing the value of research and higher education. These methods enable a quantitative assessment of the relationship between investment in specific research facilities or research projects and economic, societal, and educational outcomes. These methods are of value in quantifying the economic benefit of higher education and in managing investments within such institutions.

econ.GN↗

arXiv2019

Enabling real-time multi-messenger astrophysics discoveries with deep learning

Multi-messenger astrophysics is a fast-growing, interdisciplinary field that combines data, which vary in volume and speed of data processing, from many different instruments that probe the Universe using different cosmic messengers: electromagnetic waves, cosmic rays, gravitational waves and neutrinos. In this Expert Recommendation, we review the key challenges of real-time observations of gravitational wave sources and their electromagnetic and astroparticle counterparts, and make a number of recommendations to maximize their potential for scientific discovery. These recommendations refer to the design of scalable and computationally efficient machine learning algorithms; the cyber-infrastructure to numerically simulate astrophysical sources, and to process and interpret multi-messenger astrophysics data; the management of gravitational wave detections to trigger real-time alerts for electromagnetic and astroparticle follow-ups; a vision to harness future developments of machine learning and cyber-infrastructure resources to cope with the big-data requirements; and the need to build a community of experts to realize the goals of multi-messenger astrophysics.

gr-qc↗

arXiv2019

Deep Learning for Multi-Messenger Astrophysics: A Gateway for Discovery in the Big Data Era

This report provides an overview of recent work that harnesses the Big Data Revolution and Large Scale Computing to address grand computational challenges in Multi-Messenger Astrophysics, with a particular emphasis on real-time discovery campaigns. Acknowledging the transdisciplinary nature of Multi-Messenger Astrophysics, this document has been prepared by members of the physics, astronomy, computer science, data science, software and cyberinfrastructure communities who attended the NSF-, DOE- and NVIDIA-funded "Deep Learning for Multi-Messenger Astrophysics: Real-time Discovery at Scale" workshop, hosted at the National Center for Supercomputing Applications, October 17-19, 2018. Highlights of this report include unanimous agreement that it is critical to accelerate the development and deployment of novel, signal-processing algorithms that use the synergy between artificial intelligence (AI) and high performance computing to maximize the potential for scientific discovery with Multi-Messenger Astrophysics. We discuss key aspects to realize this endeavor, namely (i) the design and exploitation of scalable and computationally efficient AI algorithms for Multi-Messenger Astrophysics; (ii) cyberinfrastructure requirements to numerically simulate astrophysical sources, and to process and interpret Multi-Messenger Astrophysics data; (iii) management of gravitational wave detections and triggers to enable electromagnetic and astro-particle follow-ups; (iv) a vision to harness future developments of machine and deep learning and cyberinfrastructure resources to cope with the scale of discovery in the Big Data Era; (v) and the need to build a community that brings domain experts together with data scientists on equal footing to maximize and accelerate discovery in the nascent field of Multi-Messenger Astrophysics.

astro-ph.IM↗

arXiv1995

Three dimensional numerical relativity: the evolution of black holes

We report on a new 3D numerical code designed to solve the Einstein equations for general vacuum spacetimes. This code is based on the standard 3+1 approach using cartesian coordinates. We discuss the numerical techniques used in developing this code, and its performance on massively parallel and vector supercomputers. As a test case, we present evolutions for the first 3D black hole spacetimes. We identify a number of difficulties in evolving 3D black holes and suggest approaches to overcome them. We show how special treatment of the conformal factor can lead to more accurate evolution, and discuss techniques we developed to handle black hole spacetimes in the absence of symmetries. Many different slicing conditions are tested, including geodesic, maximal, and various algebraic conditions on the lapse. With current resolutions, limited by computer memory sizes, we show that with certain lapse conditions we can evolve the black hole to about $t=50M$, where $M$ is the black hole mass. Comparisons are made with results obtained by evolving spherical initial black hole data sets with a 1D spherically symmetric code. We also demonstrate that an ``apparent horizon locking shift'' can be used to prevent the development of large gradients in the metric functions that result from singularity avoiding time slicings. We compute the mass of the apparent horizon in these spacetimes, and find that in many cases it can be conserved to within about 5\% throughout the evolution with our techniques and current resolution.

gr-qc↗

arXiv1994

3D Numerical Relativity at NCSA

Numerical relativity is finally coming of age with the development of massively parallel computers. 3D problems, which were completely intractable several years ago due to limited computer power, can now be performed with grid sizes of about $200^3$. We report on several new codes developed for solving the full 3D Einstein equations, and present results of using them to evolve black holes and gravitational waves.

gr-qc↗