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The ELFIN Mission
/ Angelopoulos, V. (UCLA, Los Angeles (main)) ; Tsai, E. (UCLA, Los Angeles (main)) ; Bingley, L. (UCLA, Los Angeles (main)) ; Shaffer, C. (UCLA, Los Angeles (main) ; Tektronix, Irvine) ; Turner, D.L. (UCLA, Los Angeles (main) ; Johns Hopkins U.) ; Runov, A. (UCLA, Los Angeles (main)) ; Li, W. (UCLA, Los Angeles (main) ; Boston U.) ; Liu, J. (UCLA, Los Angeles (main)) ; Artemyev, A.V. (UCLA, Los Angeles (main)) ; Zhang, X.-J. (UCLA, Los Angeles (main)) et al.
The Electron Loss and Fields Investigation with a Spatio-Temporal Ambiguity-Resolving option (ELFIN-STAR, or simply: ELFIN) mission comprises two identical 3-Unit (3U) CubeSats on a polar (~93deg inclination), nearly circular, low-Earth (~450 km altitude) orbit. Launched on September 15, 2018, ELFIN is expected to have a >2.5 year lifetime. [...]
arXiv:2006.07747.-
2020-07-30 - 51 p.
- Published in : Space Sci. Rev.
Fulltext: PDF; Fulltext from Publisher: PDF;
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2.
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Quantum-centric Supercomputing for Materials Science: A Perspective on Challenges and Future Directions
/ Alexeev, Yuri (Argonne, PHY) ; Amsler, Maximilian (Unlisted, DE) ; Barroca, Marco Antonio (Rio de Janeiro, IMPA ; Rio de Janeiro, CBPF) ; Bassini, Sanzio (CINECA) ; Battelle, Torey (Arizona State U.) ; Camps, Daan (LBL, Berkeley) ; Casanova, David (Donostia Intl. Phys. Ctr., San Sebastian ; IKERBASQUE, Bilbao ; Basque U., Bilbao) ; Choi, Young Jai (Yonsei U.) ; Chong, Frederic T. (Chicago U.) ; Chung, Charles (IBM Watson Res. Ctr.) et al.
Computational models are an essential tool for the design, characterization, and discovery of novel materials. Hard computational tasks in materials science stretch the limits of existing high-performance supercomputing centers, consuming much of their simulation, analysis, and data resources. [...]
arXiv:2312.09733; FERMILAB-PUB-24-0001-SQMS; LA-UR-24-20107.-
2024-05-31 - 45 p.
Fulltext: 2312.09733 - PDF; FERMILAB-PUB-24-0001-SQMS - PDF; 990a7c5cfb7293c88d2918a117658c8c - PDF; External link: Fermilab Accepted Manuscript
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3.
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Pandemic drugs at pandemic speed: infrastructure for accelerating COVID-19 drug discovery with hybrid machine learning- and physics-based simulations on high-performance computers
/ Bhati, Agastya P (University Coll. London) ; Wan, Shunzhou (U. Coll. London) ; Alfè, Dario (U. Coll. London) ; Clyde, Austin R (Chicago U.) ; Bode, Mathis (RWTH Aachen U.) ; Tan, Li (Brookhaven Natl. Lab.) ; Titov, Mikhail (Rutgers U., Piscataway) ; Merzky, Andre (Rutgers U., Piscataway) ; Turilli, Matteo (Rutgers U., Piscataway) ; Jha, Shantenu (Brookhaven Natl. Lab. ; Rutgers U., Piscataway) et al.
The race to meet the challenges of the global pandemic has served
as a reminder that the existing drug discovery process is expensive, inefficient
and slow. There is a major bottleneck screening the vast number of potential
small molecules to shortlist lead compounds for antiviral drug development.
New opportunities to accelerate drug discovery lie at the interface between
machine learning methods, in this case, developed for linear accelerators,
and physics-based methods. [...]
2021 - 12 p.
- Published in : Interface Focus 11 (2021) 20210018
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5.
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Integration Of PanDA Workload Management System With Supercomputers for ATLAS and Data Intensive Science
/ Klimentov, A (Brookhaven Natl. Lab.) ; De, K (Texas U., Arlington) ; Jha, S (Rutgers U., Piscataway) ; Maeno, T (Brookhaven) ; Nilsson, P (Brookhaven Natl. Lab.) ; Oleynik, D (Texas U., Arlington ; Dubna, JINR) ; Panitkin, S (Brookhaven Natl. Lab.) ; Wells, J (Oak Ridge) ; Wenaus, T (Brookhaven Natl. Lab.)
The.LHC, operating at CERN, is leading Big Data driven scientific explorations. Experiments at the LHC explore the fundamental nature of matter and the basic forces that shape our universe. [...]
2016 - 5 p.
- Published in : J. Phys.: Conf. Ser. 762 (2016) 012021
In : 17th International Workshop on Advanced Computing and Analysis Techniques in Physics Research, Valparaíso, Valparaíso, Chile, 18 - 22 Jan 2016, pp.012021
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6.
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Next Generation Workload Management System For Big Data on Heterogeneous Distributed Computing
/ Klimentov, A (Brookhaven) ; Buncic, P (CERN) ; De, K (Texas U., Arlington) ; Jha, S (Rutgers U., Piscataway) ; Maeno, T (Brookhaven) ; Mount, R (SLAC) ; Nilsson, P (Brookhaven) ; Oleynik, D (Texas U., Arlington) ; Panitkin, S (Brookhaven) ; Petrosyan, A (Texas U., Arlington) et al.
The Large Hadron Collider (LHC), operating at the international CERN Laboratory in Geneva, Switzerland, is leading Big Data driven scientific explorations. Experiments at the LHC explore the fundamental nature of matter and the basic forces that shape our universe, and were recently credited for the discovery of a Higgs boson. [...]
2015 - 8 p.
- Published in : J. Phys.: Conf. Ser. 608 (2015) 012040
Fulltext: PDF;
In : 16th International workshop on Advanced Computing and Analysis Techniques in physics (ACAT), Prague, Czech Republic, 1 - 5 Sep 2014, pp.012040
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7.
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PanDA Beyond ATLAS : A Scalable Workload Management System For Data Intensive Science
/ Borodin, M (MEPhI) ; De, K (UTA) ; Jha, S (Rutgers University) ; Golubkov, D (IHEP-Protvino) ; Klimentov, A (BNL) ; Maeno, T (BNL) ; Nilsson, P (UTA) ; Oleynik, D (UTA) ; Panitkin, S (BNL) ; Petrosyan, A (UTA) et al.
The LHC experiments are today at the leading edge of large scale distributed data-intensive computational science. The LHC's ATLAS experiment processes data volumes which are particularly extreme, over 140 PB to date, distributed worldwide at over of 120 sites. [...]
ATL-SOFT-SLIDE-2014-117.-
Geneva : CERN, 2014
Fulltext: ISGC_Mar28_2014 - PDF; ATL-SOFT-SLIDE-2014-117 - PDF; External link: Original Communication (restricted to ATLAS)
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8.
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The Cluster Lensing and Supernova Survey with Hubble (CLASH): Strong Lensing Analysis of Abell 383 from 16-Band HST WFC3/ACS Imaging
/ Zitrin, A. ; Broadhurst, T. ; Coe, D. ; Umetsu, K. ; Postman, M. ; Benitez, N. ; Meneghetti, M. ; Medezinski, E. ; Jouvel, S. ; Bradley, L. et al.
We examine the inner mass distribution of the relaxed galaxy cluster Abell 383 in deep 16-band HST/ACS+WFC3 imaging taken as part of the CLASH multi-cycle treasury program. Our program is designed to study the dark matter distribution in 25 massive clusters, and balances depth with a wide wavelength coverage to better identify lensed systems and generate precise photometric redshifts [...]
arXiv:1103.5618.-
2011 - 13 p.
- Published in : Astrophys. J. Lett. 742 (2011) 117
External link: Preprint
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