Search

Your search keyword '"Tsaris, A."' showing total 291 results
Start Over Author "Tsaris, A."
291 results on '"Tsaris, A."'

51. MLPerf™ HPC: A Holistic Benchmark Suite for Scientific Machine Learning on HPC Systems.

Author

Steven Farrell, Murali Emani, Jacob Balma, Lukas Drescher, Aleksandr Drozd, Andreas Fink, Geoffrey C. Fox, David Kanter, Thorsten Kurth, Peter Mattson, Dawei Mu, Amit Ruhela, Kento Sato, Koichi Shirahata, Tsuguchika Tabaru, Aristeidis Tsaris, Jan Balewski, Ben Cumming, Takumi Danjo, Jens Domke, Takaaki Fukai, Naoto Fukumoto, Tatsuya Fukushi, Balazs Gerofi, Takumi Honda, Toshiyuki Imamura, Akihiko Kasagi, Kentaro Kawakami, Shuhei Kudo, Akiyoshi Kuroda, Maxime Martinasso, Satoshi Matsuoka, Henrique Mendonça, Kazuki Minami, Prabhat Ram, Takashi Sawada, Mallikarjun Shankar, Tom St. John, Akihiro Tabuchi, Venkatram Vishwanath, Mohamed Wahib, Masafumi Yamazaki, and Junqi Yin

Published
2021
Full Text
View/download PDF

55. The HEP.TrkX Project: Deep neural networks for HL-LHC online and offline tracking

Author

Farrell, S, Anderson, D, Calafiura, P, Cerati, G, Gray, L, Kowalkowski, J, Mudigonda, M, Prabhat, Spentzouris, P, Spiropoulou, M, Tsaris, A, Vlimant, JR, and Zheng, S

Subjects
Bioengineering
Abstract

Particle track reconstruction in dense environments such as the detectors of the High Luminosity Large Hadron Collider (HL-LHC) is a challenging pattern recognition problem. Traditional tracking algorithms such as the combinatorial Kalman Filter have been used with great success in LHC experiments for years. However, these state-of-the-art techniques are inherently sequential and scale poorly with the expected increases in detector occupancy in the HL-LHC conditions. The HEP.TrkX project is a pilot project with the aim to identify and develop cross-experiment solutions based on machine learning algorithms for track reconstruction. Machine learning algorithms bring a lot of potential to this problem thanks to their capability to model complex non-linear data dependencies, to learn effective representations of high-dimensional data through training, and to parallelize easily on high-throughput architectures such as GPUs. This contribution will describe our initial explorations into this relatively unexplored idea space. We will discuss the use of recurrent (LSTM) and convolutional neural networks to find and fit tracks in toy detector data.

Published
2017

56. The HEP.TrkX Project: deep neural networks for HL-LHC online and offline tracking

Author

Farrell, Steven, Anderson, Dustin, Calafiura, Paolo, Cerati, Giuseppe, Gray, Lindsey, Kowalkowski, Jim, Mudigonda, Mayur, Prabhat, Spentzouris, Panagiotis, Spiropoulou, Maria, Tsaris, Aristeidis, Vlimant, Jean-Roch, and Zheng, Stephan

Subjects
Bioengineering
Abstract

Particle track reconstruction in dense environments such as the detectors of the High Luminosity Large Hadron Collider (HL-LHC) is a challenging pattern recognition problem. Traditional tracking algorithms such as the combinatorial Kalman Filter have been used with great success in LHC experiments for years. However, these state-of-the-art techniques are inherently sequential and scale poorly with the expected increases in detector occupancy in the HL-LHC conditions. The HEP.TrkX project is a pilot project with the aim to identify and develop cross-experiment solutions based on machine learning algorithms for track reconstruction. Machine learning algorithms bring a lot of potential to this problem thanks to their capability to model complex non-linear data dependencies, to learn effective representations of high-dimensional data through training, and to parallelize easily on high-throughput architectures such as GPUs. This contribution will describe our initial explorations into this relatively unexplored idea space. We will discuss the use of recurrent (LSTM) and convolutional neural networks to find and fit tracks in toy detector data.

Published
2017

57. The GlueX beamline and detector

Author

Adhikari, S., Akondi, C.S., Al Ghoul, H., Ali, A., Amaryan, M., Anassontzis, E.G., Austregesilo, A., Barbosa, F., Barlow, J., Barnes, A., Barriga, E., Barsotti, R., Beattie, T.D., Benesch, J., Berdnikov, V.V., Biallas, G., Black, T., Boeglin, W., Brindza, P., Briscoe, W.J., Britton, T., Brock, J., Brooks, W.K., Cannon, B.E., Carlin, C., Carman, D.S., Carstens, T., Cao, N., Chernyshov, O., Chudakov, E., Cole, S., Cortes, O., Crahen, W.D., Crede, V., Dalton, M.M., Daniels, T., Deur, A., Dickover, C., Dobbs, S., Dolgolenko, A., Dotel, R., Dugger, M., Dzhygadlo, R., Dzierba, A., Egiyan, H., Erbora, T., Ernst, A., Eugenio, P., Fanelli, C., Fegan, S., Foda, A.M., Foote, J., Frye, J., Furletov, S., Gan, L., Gasparian, A., Gerasimov, A., Gevorgyan, N., Gleason, C., Goetzen, K., Goncalves, A., Goryachev, V.S., Guo, L., Hakobyan, H., Hamdi, A., Hardin, J., Henschel, C.L., Huber, G.M., Hutton, C., Hurley, A., Ioannou, P., Ireland, D.G., Ito, M.M., Jarvis, N.S., Jones, R.T., Kakoyan, V., Katsaganis, S., Kalicy, G., Kamel, M., Keith, C.D., Klein, F.J., Kliemt, R., Kolybaba, D., Kourkoumelis, C., Krueger, S.T., Kuleshov, S., Larin, I., Lawrence, D., Leckey, J.P., Lersch, D.I., Leverington, B.D., Levine, W.I., Li, W., Liu, B., Livingston, K., Lolos, G.J., Lyubovitskij, V., Mack, D., Marukyan, H., Mattione, P.T., Matveev, V., McCaughan, M., McCracken, M., McGinley, W., McIntyre, J., Meekins, D., Mendez, R., Meyer, C.A., Miskimen, R., Mitchell, R.E., Mokaya, F., Moriya, K., Nerling, F., Ng, L., Ni, H., Ostrovidov, A.I., Papandreou, Z., Patsyuk, M., Paudel, C., Pauli, P., Pedroni, R., Pentchev, L., Peters, K.J., Phelps, W., Pierce, J., Pooser, E., Popov, V., Pratt, B., Qiang, Y., Qin, N., Razmyslovich, V., Reinhold, J., Ritchie, B.G., Ritman, J., Robison, L., Romanov, D., Romero, C., Salgado, C., Sandoval, N., Satogata, T., Schertz, A.M., Schadmand, S., Schick, A., Schumacher, R.A., Schwarz, C., Schwiening, J., Semenov, A.Yu., Semenova, I.A., Seth, K.K., Shen, X., Shepherd, M.R., Smith, E.S., Sober, D.I., Somov, A., Somov, S., Soto, O., Sparks, N., Staib, M.J., Stanislav, C., Stevens, J.R., Stewart, J., Strakovsky, I.I., Sumner, B.C. L., Suresh, K., Tarasov, V.V., Taylor, S., Teigrob, L.A., Teymurazyan, A., Thiel, A., Tolstukhin, I., Tomaradze, A., Toro, A., Tsaris, A., Van Haarlem, Y., Vasileiadis, G., Vega, I., Visser, G., Voulgaris, G., Walford, N.K., Werthmüller, D., Whitlatch, T., Wickramaarachchi, N., Williams, M., Wolin, E., Xiao, T., Yang, Y., Zarling, J., Zhang, Z., Zhou, Q., Zhou, X., and Zihlmann, B.

Published
2021
Full Text
View/download PDF

58. First Results from The GlueX Experiment

Author

The GlueX Collaboration, Ghoul, H. Al, Anassontzis, E. G., Barbosa, F., Barnes, A., Beattie, T. D., Bennett, D. W., Berdnikov, V. V., Black, T., Boeglin, W., Brooks, W. K., Cannon, B., Chernyshov, O., Chudakov, E., Crede, V., Dalton, M. M., Deur, A., Dobbs, S., Dolgolenko, A., Dugger, M., Egiyan, H., Eugenio, P., Foda, A. M., Frye, J., Furletov, S., Gan, L., Gasparian, A., Gerasimov, A., Gevorgyan, N., Goryachev, V. S., Guegan, B., Guo, L., Hakobyan, H., Hakobyan2, H., Hardin, J., Huber, G. M., Ireland, D., Ito, M. M., Jarvis, N. S., Jones, R. T., Kakoyan, V., Kamel, M., Klein, F. J., Kourkoumeli, C., Kuleshov, S., Lara, M., Larin, I., Lawrence, D., Leckey, J., Levine, W. I., Livingston, K., Lolos, G. J., Mack, D., Mattione, P. T., Matveev, V., McCaughan, M., McGinley, W., McIntyre, J., Mendez, R., Meyer, C. A., Miskimen, R., Mitchell, R. E., Mokaya, F., Moriya, K., Nigmatkulov, G., Ochoa, N., Ostrovidov, A. I., Papandreou, Z., Pedroni, R., Pennington, M., Pentchev, L., Ponosov, A., Pooser, E., Pratt, B., Qiang, Y., Reinhold, J., Ritchie, B. G., Robison, L., Romanov, D., Salgado, C., Schumacher, R. A., Semenov, A. Yu., Semenova, I. A., Senderovich, I., Seth, K. K., Shepherd, M. R., Smith, E. S., Sober, D. I., Somov, A., Somov, S., Soto, O., Sparks, N., Staib, M. J., Stevens, J. R., Subedi, A., Tarasov, V., Taylor, S., Tolstukhin, I., Tomaradze, A., Toro, A., Tsaris, A., Vasileiadis, G., Vega, I., Voulgaris, G., Walford, N. K., Whitlatch, T., Williams, M., Wolin, E., Xiao, T., Zarling, J., and Zihlmann, B.

Subjects
Nuclear Experiment, High Energy Physics - Experiment, Physics - Instrumentation and Detectors
Abstract

The GlueX experiment at Jefferson Lab ran with its first commissioning beam in late 2014 and the spring of 2015. Data were collected on both plastic and liquid hydrogen targets, and much of the detector has been commissioned. All of the detector systems are now performing at or near design specifications and events are being fully reconstructed, including exclusive production of $\pi^{0}$, $\eta$ and $\omega$ mesons. Linearly-polarized photons were successfully produced through coherent bremsstrahlung and polarization transfer to the $\rho$ has been observed., Comment: 8 pages, 6 figures, Invited contribution to the Hadron 2015 Conference, Newport News VA, September 2015

Published
2015
Full Text
View/download PDF

59. Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19.

Author

Atanu Acharya, Rupesh Agarwal, Matthew B. Baker, Jérôme Baudry, Debsindhu Bhowmik, Swen Böhm, Kendall G. Byler, Sam Yen-Chi Chen, Leighton Coates, Connor J. Cooper, Omar Demerdash, Isabella Daidone, John D. Eblen, Sally R. Ellingson, Stefano Forli, Jens Glaser, James C. Gumbart, John Gunnels, Oscar R. Hernandez, Stephan Irle, Daniel W. Kneller, Andrey Kovalevsky, Jeffrey M. Larkin, Travis J. Lawrence, Scott LeGrand, Shih-Hsien Liu, Julie C. Mitchell, Gilchan Park, Jerry M. Parks, Anna Pavlova, Loukas Petridis, Duncan Poole, Line Pouchard, Arvind Ramanathan, David M. Rogers 0001, Diogo Santos-Martins, Aaron Scheinberg, Ada Sedova, Yue Shen, Jeremy C. Smith, Micholas Dean Smith, Carlos Soto 0003, Aristides Tsaris, Mathialakan Thavappiragasam, Andreas F. Tillack, Josh Vincent Vermaas, Van Quan Vuong, Junqi Yin, Shinjae Yoo, Mai Zahran, and Laura Zanetti Polzi

Published
2020
Full Text
View/download PDF

60. A study of decays to strange final states with GlueX in Hall D using components of the BaBar DIRC

Author

The GlueX Collaboration, Dugger, M., Ritchie, B., Senderovich, I., Anassontzis, E., Ioannou, P., Kourkoumeli, C., Vasileiadis, G., Voulgaris, G., Jarvis, N., Levine, W., Mattione, P., McGinley, W., Meyer, C. A., Schumacher, R., Staib, M., Klein, F., Sober, D., Sparks, N., Walford, N., Doughty, D., Barnes, A., Jones, R., McIntyre, J., Mokaya, F., Pratt, B., Boeglin, W., Guo, L., Pooser, E., Reinhold, J., Ghoul, H. Al, Crede, V., Eugenio, P., Ostrovidov, A., Tsaris, A., Ireland, D., Livingston, K., Bennett, D., Bennett, J., Frye, J., Lara, M., Leckey, J., Mitchell, R., Moriya, K., Shepherd, M. R., Chernyshov, O., Dolgolenko, A., Gerasimov, A., Goryachev, V., Larin, I., Matveev, V., Tarasov, V., Barbosa, F., Chudakov, E., Dalton, M., Deur, A., Dudek, J., Egiyan, H., Furletov, S., Ito, M., Mack, D., Lawrence, D., McCaughan, M., Pennington, M., Pentchev, L., Qiang, Y., Smith, E., Somov, A., Taylor, S., Whitlatch, T., Zihlmann, B., Miskimen, R., Guegan, B., Hardin, J., Stevens, J., Williams, M., Berdnikov, V., Nigmatkulov, G., Ponosov, A., Romanov, D., Somov, S., Tolstukhin, I., Salgado, C., Ambrozewicz, P., Gasparian, A., Pedroni, R., Black, T., Gan, L., Dobbs, S., Seth, K., Ting, X., Tomaradze, A., Beattie, T., Huber, G., Lolos, G., Papandreou, Z., Semenov, A., Semenova, I., Brooks, W., Hakobyan, H., Kuleshov, S., Soto, O., Toro, A., Vega, I., Gevorgyan, N., and Kakoyan, V.

Subjects
Physics - Instrumentation and Detectors, High Energy Physics - Experiment
Abstract

We propose to enhance the kaon identification capabilities of the GlueX detector by constructing an FDIRC (Focusing Detection of Internally Reflected Cherenkov) detector utilizing the decommissioned BaBar DIRC components. The GlueX FDIRC would significantly enhance the GlueX physics program by allowing one to search for and study hybrid mesons decaying into kaon final states. Such systematic studies of kaon final states are essential for inferring the quark flavor content of hybrid and conventional mesons. The GlueX FDIRC would reuse one-third of the synthetic fused silica bars that were utilized in the BaBar DIRC. A new focussing photon camera, read out with large area photodetectors, would be developed. We propose operating the enhanced GlueX detector in Hall D for a total of 220 days at an average intensity of 5x10^7 {\gamma}/s, a program that was conditionally approved by PAC39, Comment: 25 pages, 29 figures

Published
2014

61. An initial study of mesons and baryons containing strange quarks with GlueX

Author

The GlueX Collaboration, AlekSejevs, A., Barkanova, S., Dugger, M., Ritchie, B., Senderovich, I., Anassontzis, E., Ioannou, P., Kourkoumeli, C., Voulgaris, G., Jarvis, N., Levine, W., Mattione, P., McGinley, W., Meyer, C. A., Schumacher, R., Staib, M., Collins, P., Klein, F., Sober, D., Doughty, D., Barnes, A., Jones, R., McIntyre, J., Mokaya, F., Pratt, B., Boeglin, W., Guo, L., Khetarpal, P., Pooser, E., Reinhold, J., Ghoul, H. Al, Capstick, S., Crede, V., Eugenio, P., Ostrovidov, A., Sparks, N., Tsaris, A., Ireland, D., Livingston, K., Bennett, D., Bennett, J., Frye, J., Lara, M., Leckey, J., Mitchell, R., Moriya, K., Shepherd, M. R., Szczepaniak, A., Miskimen, R., Mushkarenkov, A., Guegan, B., Hardin, J., Stevens, J., Williams, M., Ponosov, A., Somov, S., Salgado, C., Ambrozewicz, P., Gasparian, A., Pedroni, R., Black, T., Gan, L., Dobbs, S., Seth, K. K., Tomaradze, A., Dudek, J., Close, F., Swanson, E., Denisov, S., Huber, G., Kolybaba, D., Krueger, S., Lolos, G., Papandreou, Z., Semenov, A., Semenova, I., Tahani, M., Brooks, W., Hakobyan, H., Kuleshov, S., Soto, O., Toro, A., Vega, I., White, R., Barbosa, F., Chudakov, E., Egiyan, H., Ito, M., Lawrence, D., McCaughan, M., Pennington, M., Pentchev, L., Qiang, Y., Smith, E. S., Somov, A., Taylor, S., Whitlatch, T., Wolin, E., and Zihlmann, B.

Subjects
Nuclear Experiment
Abstract

The primary motivation of the GlueX experiment is to search for and ultimately study the pattern of gluonic excitations in the meson spectrum produced in $\gamma p$ collisions. Recent lattice QCD calculations predict a rich spectrum of hybrid mesons that have both exotic and non-exotic $J^{PC}$, corresponding to $q\bar{q}$ states ($q=u,$ $d,$ or $s$) coupled with a gluonic field. A thorough study of the hybrid spectrum, including the identification of the isovector triplet, with charges 0 and $\pm1$, and both isoscalar members, $|s\bar{s}\ >$ and $|u\bar{u}\ > + |d\bar{d}\ >$, for each predicted hybrid combination of $J^{PC}$, may only be achieved by conducting a systematic amplitude analysis of many different hadronic final states. Detailed studies of the performance of the \gx detector have indicated that identification of particular final states with kaons is possible using the baseline detector configuration. The efficiency of kaon detection coupled with the relatively lower production cross section for particles containing hidden strangeness will require a high intensity run in order for analyses of such states to be feasible. We propose to collect a total of 200 days of physics analysis data at an average intensity of $5\times 10^7$ tagged photons on target per second. This data sample will provide an order of magnitude statistical improvement over the initial GlueX running, which will allow us to begin a program of studying mesons and baryons containing strange quarks. In addition, the increased intensity will permit us to study reactions that may have been statistically limited in the initial phases of GlueX. Overall, this will lead to a significant increase in the potential for \gx to make key experimental advances in our knowledge of hybrid mesons and excited $\Xi$ baryons., Comment: 22 pages, 8 figures

Published
2013

62. Comparative evaluation of deep learning workloads for leadership-class systems

Author

Junqi Yin, Aristeidis Tsaris, Sajal Dash, Ross Miller, Feiyi Wang, and Mallikarjun (Arjun) Shankar

Subjects
CORAL benchmark, Deep learning stack, ROCm, Science, Engineering (General). Civil engineering (General), TA1-2040
Abstract

Deep learning (DL) workloads and their performance at scale are becoming important factors to consider as we design, develop and deploy next-generation high-performance computing systems. Since DL applications rely heavily on DL frameworks and underlying compute (CPU/GPU) stacks, it is essential to gain a holistic understanding from compute kernels, models, and frameworks of popular DL stacks, and to assess their impact on science-driven, mission-critical applications. At Oak Ridge Leadership Computing Facility (OLCF), we employ a set of micro and macro DL benchmarks established through the Collaboration of Oak Ridge, Argonne, and Livermore (CORAL) to evaluate the AI readiness of our next-generation supercomputers. In this paper, we present our early observations and performance benchmark comparisons between the Nvidia V100 based Summit system with its CUDA stack and an AMD MI100 based testbed system with its ROCm stack. We take a layered perspective on DL benchmarking and point to opportunities for future optimizations in the technologies that we consider.

Published
2021
Full Text
View/download PDF

63. A study of meson and baryon decays to strange final states with GlueX in Hall D (A proposal to the 39th Jefferson Lab Program Advisory Committee)

Author

The GlueX Collaboration, Dugger, M., Ritchie, B., Anassontzis, E., Ioannou, P., Kourkoumeli, C., Voulgaris, G., Jarvis, N., Levine, W., Mattione, P., Meyer, C. A., Schumacher, R., Collins, P., Klein, F., Sober, D., Doughty, D., Barnes, A., Jones, R., McIntyre, J., Mokaya, F., Pratt, B., Senderovich, I., Boeglin, W., Guo, L., Khetarpal, P., Pooser, E., Reinhold, J., Ghoul, H. Al, Capstick, S., Crede, V., Eugenio, P., Ostrovidov, A., Sparks, N., Tsaris, A., Ireland, D., Livingston, K., Bennett, D., Bennett, J., Frye, J., Leckey, J., Mitchell, R., Moriya, K., Shepherd, M. R., Szczepaniak, A., Miskimen, R., Williams, M., Ambrozewicz, P., Gasparian, A., Pedroni, R., Black, T., Gan, L., Dudek, J., Close, F., Swanson, E., Denisov, S., Huber, G., Katsaganis, S., Kolybaba, D., Lolos, G., Papandreou, Z., Semenov, A., Semenova, I., Tahani, M., Brooks, W., Kuleshov, S., Toro, A., Barbosa, F., Chudakov, E., Egiyan, H., Ito, M., Lawrence, D., Pentchev, L., Qiang, Y., Smith, E. S., Somov, A., Taylor, S., Whitlatch, T., Wolin, E., and Zihlmann, B.

Subjects
High Energy Physics - Experiment, Nuclear Experiment
Abstract

The primary motivation of the GlueX experiment is to search for and ultimately study the pattern of gluonic excitations in the meson spectrum produced in gamma p collisions. Recent lattice QCD calculations predict a rich spectrum of hybrid mesons that have both exotic and non-exotic JPC, corresponding to q q-bar (q=u, d, or s) states coupled with a gluonic field. A thorough study of the hybrid spectrum, including the identification of the isovector triplet, with charges 0 and +-1, and both isoscalar members, |s s-bar> and |u u-bar> + |d d-bar>, for each predicted hybrid combination of JPC, may only be achieved by conducting a systematic amplitude analysis of many different hadronic final states. We propose the development of a kaon identification system, supplementing the existing GlueX forward time-of-flight detector, in order to cleanly select meson and baryon decay channels that include kaons. Once this detector has been installed and commissioned, we plan to collect a total of 200 days of physics analysis data at an average intensity of 5 x 10^7 tagged photons on target per second. This data sample will provide an order of magnitude statistical improvement over the initial GlueX data set and, with the developed kaon identification system, a significant increase in the potential for GlueX to make key experimental advances in our knowledge of hybrid mesons and Cascade baryons., Comment: 20 pages, 9 figures (submitted to the 39th Jefferson Lab PAC)

Published
2012

64. 2021 Operational Assessment - OLCF

Author

Abraham, Subil, primary, Abston, Paul, additional, Adamson, Ryan, additional, Anantharaj, Valentine, additional, Barlow, Aaron, additional, Barker, Ashley, additional, Beck, Tom, additional, Bethea, Katie, additional, Carlyle, Adam, additional, Dietz, Dan, additional, Fuson, Christopher, additional, Hill, Jason, additional, Landfield, Ryan, additional, Maxwell, Don, additional, McDowell, Rachel, additional, Miller, Bronson, additional, Moore, Sheila, additional, Oral, Sarp, additional, Papatheodore, Thomas, additional, Parete-Koon, Suzanne, additional, Prout, Ryan, additional, Ray, Sherry, additional, Renaud, William, additional, Rogers II, Jim, additional, Shankar, Mallikarjun (Arjun), additional, Shin, Woong, additional, Simmerman, Scott, additional, Sonewald, Betsy, additional, Thach, Kevin, additional, Tsaris, Aristeidis (aris), additional, Tourassi, Georgia, additional, Whitt, Justin, additional, Yin, Junqi, additional, Hernandez Arreguin, Benjamin, additional, and Melesse Vergara, Veronica, additional

Published
2022
Full Text
View/download PDF

65. IMPECCABLE: Integrated Modeling PipelinE for COVID Cure by Assessing Better LEads.

Author

Aymen Al Saadi, Dario Alfè, Yadu N. Babuji, Agastya Bhati, Ben Blaiszik, Thomas S. Brettin, Kyle Chard, Ryan Chard, Peter V. Coveney, Anda Trifan, Alex Brace, Austin Clyde, Ian T. Foster, Tom Gibbs, Shantenu Jha, Kristopher Keipert, Thorsten Kurth, Dieter Kranzlmüller, Hyungro Lee, Zhuozhao Li, Heng Ma, André Merzky, Gerald Mathias, Alexander Partin, Junqi Yin, Arvind Ramanathan, Ashka Shah, Abraham C. Stern, Rick Stevens, Li Tan, Mikhail Titov, Aristeidis Tsaris, Matteo Turilli, Huub J. J. Van Dam, Shunzhou Wan, and David Wifling

Published
2020

66. Track Seeding and Labelling with Embedded-space Graph Neural Networks.

Author

Nicholas Choma, Daniel Murnane, Xiangyang Ju, Paolo Calafiura, Sean Conlon, Steven Farrell, Prabhat, Giuseppe Cerati, Lindsey Gray, Thomas Klijnsma, Jim Kowalkowski, Panagiotis Spentzouris, Jean-Roch Vlimant, Maria Spiropulu, Adam Aurisano, Jeremy Hewes, Aristeidis Tsaris, Kazuhiro Terao, and Tracy L. Usher

Published
2020

74. HEP ML/Optimization Go Quantum – QuantISED Pilot

Author

Adachi, Steven, primary, Caldeira, João, additional, Delgado, Andrea, additional, Hamilton, Kathleen, additional, Humble, Travis, additional, Job, Joshua, additional, Kowalkowski, James, additional, Leichenauer, Stefan, additional, McCaskey, Alex, additional, Mrenna, Stephen, additional, Nord, Brian, additional, Perdue, Gabriel, additional, Peters, Evan, additional, and Tsaris, Aristeidis, additional

Published
2020
Full Text
View/download PDF

75. Measurement of νμ charged-current inclusive π0 production in the NOvA near detector

Author

Acero, M. A., Adamson, P., Agam, G., Aliaga, L., Alion, T., Allakhverdian, V., Altakarli, S., Anfimov, N., Antoshkin, A., Asquith, L., Arrieta-Diaz, E., Aurisano, A., Back, A., Baird, M., Balashov, N., Baldi, P., Bambah, B. A., Bashar, S., Bays, K., Bending, S., Bernstein, R., Bhatnagar, V., Bhuyan, B., Bian, J., Blair, J., Booth, A. C., Bour, P., Bromberg, C., Buchanan, N., Butkevich, A., Calvez, S., Campbell, M., Carroll, T. J., Catano-Mur, E., Childress, S., Choudhary, B. C., Chowdhury, B., Coan, T. E., Colo, M., Corwin, L., Cremonesi, L., Davies, G. S., Derwent, P. F., Dharmapalan, R., Ding, P., Djurcic, Z., Dolce, M., Doyle, D., Dukes, E. C., Dueñas Tonguino, D., Dung, P., Duyang, H., Edayath, S., Ehrlich, R., Feldman, G. J., Filip, P., Flanagan, W., Frank, M. J., Gallagher, H. R., Gandrajula, R., Gao, F., Germani, S., Giri, A., Gomes, R. A., Goodman, M. C., Grichine, V., Groh, M., Group, R., Guo, B., Habig, A., Hakl, F., Hall, A., Hartnell, J., Hatcher, R., Hatzikoutelis, A., Heller, K., Hewes, V., Himmel, A., Holin, A., Howard, B., Huang, J., Hylen, J., Jediny, F., Johnson, C., Judah, M., Kakorin, I., Kalra, D., Kaplan, D. M., Keloth, R., Klimov, O., Koerner, L. W., Kolupaeva, L., Kotelnikov, S., Kreymer, A., Kubu, M., Kullenberg, Ch., Kumar, A., Kuruppu, C. D., Kus, V., Lackey, T., Lang, K., Li, L., Lin, S., Lister, A., Lokajicek, M., Luchuk, S., Maan, K., Magill, S., Mann, W. A., Marshak, M. L., Martinez-Casales, M., Matveev, V., Mayes, B., Méndez, D. P., Messier, M. D., Meyer, H., Miao, T., Miller, W. H., Mishra, S. R., Mislivec, A., Mohanta, R., Moren, A., Mualem, L., Muether, M., Mufson, S., Mulder, K., Murphy, R., Musser, J., Naples, D., Nayak, N., Nelson, J. K., Nichol, R., Nikseresht, G., Niner, E., Norman, A., Norrick, A., Nosek, T., Olshevskiy, A., Olson, T., Paley, J., Patterson, R. B., Pawloski, G., Pershey, D., Petrova, O., Petti, R., Phan, D. D., Phan-Budd, S., Plunkett, R. K., Potukuchi, B., Principato, C., Psihas, F., Radovic, A., Rafique, A., Raj, V., Rameika, R. A., Rebel, B., Rojas, P., Ryabov, V., Samoylov, O., Sanchez, M. C., Sánchez Falero, S., Seong, I. S., Shanahan, P., Sheshukov, A., Singh, P., Singh, V., Smith, E., Smolik, J., Snopok, P., Solomey, N., Song, E., Sousa, A., Soustruznik, K., Strait, M., Suter, L., Sutton, A., Talaga, R. L., Tapia Oregui, B., Tas, P., Thayyullathil, R. B., Thomas, J., Tiras, E., Torbunov, D., Tripathi, J., Tsaris, A., Torun, Y., Urheim, J., Vahle, P., Vasel, J., Vokac, P., Vrba, T., Wallbank, M., Warburton, T. K., Wetstein, M., While, M., Whittington, D., Wickremasinghe, D. A., Wojcicki, S. G., Wolcott, J., Yallappa Dombara, A., Yonehara, K., Yu, S., Yu, Y., Zadorozhnyy, S., Zalesak, J., Zhang, Y., and Zwaska, R.

Abstract

Cross sections for the interaction νμA→μ−π0X with neutrino energies between 1 and 5 GeV are measured using a sample of 165,000 selected events collected in the NOvA experiment’s near detector, a hydrocarbon-based detector exposed to the Neutrinos from the Main Injector beam at the Fermi National Accelerator Laboratory. Results are presented as a flux-averaged total cross section and as differential cross sections in the momenta and angles of the outgoing muon and π0, the total four-momentum transfer, and the invariant mass of the hadronic system. Comparisons are made with predictions from a reference version of the genie neutrino interaction generator. The measured total cross section of (3.57±0.44)×10−39 cm2 is 7.5% higher than the genie prediction, but is consistent within experimental errors.

Published
2023

79. Language models for the prediction of SARS-CoV-2 inhibitors

Author

Blanchard, Andrew E, primary, Gounley, John, additional, Bhowmik, Debsindhu, additional, Chandra Shekar, Mayanka, additional, Lyngaas, Isaac, additional, Gao, Shang, additional, Yin, Junqi, additional, Tsaris, Aristeidis, additional, Wang, Feiyi, additional, and Glaser, Jens, additional

Published
2022
Full Text
View/download PDF

80. Accelerated Machine Learning as a Service for Particle Physics Computing

Author

Duarte, Javier, primary, Holzman, Burt, additional, Jindariani, Sergo, additional, Klijnsma, Thomas, additional, Kreis, Benjamin, additional, Liu, Mia, additional, Pedro, Kevin, additional, Tran, Nhan, additional, Tsaris, Aristeidis, additional, Harris, Phil, additional, Rankin, Dylan, additional, Loncar, Vladimir, additional, Ngadiuba, Jennifer, additional, Pierini, Maurizio, additional, Khan, Suffian, additional, Lee, Brian, additional, Perez, Brandon, additional, Way, Ted, additional, Versteeg, Colin, additional, Hauck, Scott, additional, Hsu, Shih-Chieh, additional, Trahms, Matthew, additional, Werran, Dustin, additional, and Wu, Zhenbin, additional

Published
2019
Full Text
View/download PDF

81. Improved measurement of neutrino oscillation parameters by the NOvA experiment

Author

Acero, M. A., Adamson, P., Aliaga, L., Anfimov, N., Antoshkin, A., Arrieta-Diaz, E., Asquith, L., Aurisano, A., Back, A., Backhouse, C., Baird, M., Balashov, N., Baldi, P., Bambah, B. A., Bashar, S., Bays, K., Bernstein, R., Bhatnagar, V., Bhattarai, D., Bhuyan, B., Bian, J., Blair, J., Booth, A. C., Bowles, R., Bromberg, C., Buchanan, N., Butkevich, A., Calvez, S., Carroll, T. J., Catano-Mur, E., Choudhary, B. C., Christensen, A., Coan, T. E., Colo, M., Cremonesi, L., Davies, G. S., Derwent, P. F., Ding, P., Djurcic, Z., Dolce, M., Doyle, D., Tonguino, D. Dueñas, Dukes, E. C., Duyang, H., Ehrlich, R., Elkins, M., Ewart, E., Feldman, G. J., Filip, P., Franc, J., Frank, M. J., Gallagher, H. R., Gandrajula, R., Gao, F., Giri, A., Gomes, R. A., Goodman, M. C., Grichine, V., Groh, M., Guo, B., Habig, A., Hakl, F., Hall, A., Hartnell, J., Hatcher, R., Hausner, H., He, M., Heller, K., Hewes, J., Himmel, A., Holin, A., Huang, J., Jargowsky, B., Jarosz, J., Jediny, F., Johnson, C., Judah, M., Kakorin, I., Kaplan, D. M., Kalitkina, A., Keloth, R., Klimov, O., Koerner, L. W., Kolupaeva, L., Kotelnikov, S., Kralik, R., Kullenberg, Ch, Kubu, M., Kumar, A., Kuruppu, C. D., Kus, V., Lackey, T., Lang, K., Lasorak, P., Lesmeister, J., Lin, S., Lister, A., Liu, J., Lokajicek, M., Magill, S., Plata, M. Manrique, Mann, W. A., Marshak, M. L., Martinez-Casales, M., Matveev, V., Mayes, B., Méndez, D. P., Messier, M. D., Meyer, H., Miao, T., Miller, W. H., Mishra, S. R., Mislivec, A., Mohanta, R., Moren, A., Morozova, A., Mu, W., Mualem, L., Muether, M., Mufson, S., Mulder, K., Naples, D., Nayak, N., Nelson, J. K., Nichol, R., Niner, E., Norman, A., Norrick, A., Nosek, T., Oh, H., Olshevskiy, A., Olson, T., Ott, J., Paley, J., Patterson, R. B., Pawloski, G., Olga Petrova, Petti, R., Phan, D. D., Plunkett, R. K., Porter, J. C. C., Rafique, A., Psihas, F., Raj, V., Rajaoalisoa, M., Ramson, B., Rebel, B., Rojas, P., Roy, P., Ryabov, V., Samoylov, O., Sanchez, M. C., Falero, S. Sánchez, Shanahan, P., Sheshukov, A., Singh, P., Singh, V., Smith, E., Smolik, J., Snopok, P., Solomey, N., Sousa, A., Soustruznik, K., Strait, M., Suter, L., Sutton, A., Swain, S., Sweeney, C., Sztuc, A., Talaga, R. L., Oregui, B. Tapia, Tas, P., Thakore, T., Thayyullathil, R. B., Thomas, J., Tiras, E., Tripathi, J., Trokan-Tenorio, J., Tsaris, A., Torun, Y., Urheim, J., Vahle, P., Vallari, Z., Vasel, J., Vokac, P., Vrba, T., Wallbank, M., Warburton, T. K., Wetstein, M., Whittington, D., Wickremasinghe, D. A., Wojcicki, S. G., Wolcott, J., Wu, W., Xiao, Y., Dombara, A. Yallappa, Yankelevich, A., Yonehara, K., Yu, S., Yu, Y., Zadorozhnyy, S., Zalesak, J., Zhang, Y., Zwaska, R., and Group, R. C.

Subjects
High Energy Physics - Experiment (hep-ex), High Energy Physics::Phenomenology, FOS: Physical sciences, High Energy Physics::Experiment, High Energy Physics - Experiment
Abstract

We present new $\nu_\mu\rightarrow\nu_e$, $\nu_\mu\rightarrow\nu_\mu$, $\overline{\nu}_\mu\rightarrow\overline{\nu}_e$, and $\overline{\nu}_\mu\rightarrow\overline{\nu}_\mu$ oscillation measurements by the NOvA experiment, with a 50% increase in neutrino-mode beam exposure over the previously reported results. The additional data, combined with previously published neutrino and antineutrino data, are all analyzed using improved techniques and simulations. A joint fit to the $\nu_e$, $\nu_\mu$, $\overline{\nu}_e$, and $\overline{\nu}_\mu$ candidate samples within the 3-flavor neutrino oscillation framework continues to yield a best-fit point in the normal mass ordering and the upper octant of the $\theta_{23}$ mixing angle, with $\Delta m^{2}_{32} = (2.41\pm0.07)\times 10^{-3}$ eV$^2$ and $\sin^2\theta_{23} = 0.57^{+0.03}_{-0.04}$. The data disfavor combinations of oscillation parameters that give rise to a large asymmetry in the rates of $\nu_e$ and $\overline{\nu}_e$ appearance. This includes values of the CP-violating phase in the vicinity of $\delta_\text{CP} = \pi/2$ which are excluded by $>3\sigma$ for the inverted mass ordering, and values around $\delta_\text{CP} = 3\pi/2$ in the normal ordering which are disfavored at 2$\sigma$ confidence., Comment: 11 pages, 6 figures. Supplementary material attached (7 figures)

Published
2022

82. Intelligent resolution: Integrating Cryo-EM with AI-driven multi-resolution simulations to observe the severe acute respiratory syndrome coronavirus-2 replication-transcription machinery in action

Author

Trifan, Anda, primary, Gorgun, Defne, additional, Salim, Michael, additional, Li, Zongyi, additional, Brace, Alexander, additional, Zvyagin, Maxim, additional, Ma, Heng, additional, Clyde, Austin, additional, Clark, David, additional, Hardy, David J, additional, Burnley, Tom, additional, Huang, Lei, additional, McCalpin, John, additional, Emani, Murali, additional, Yoo, Hyenseung, additional, Yin, Junqi, additional, Tsaris, Aristeidis, additional, Subbiah, Vishal, additional, Raza, Tanveer, additional, Liu, Jessica, additional, Trebesch, Noah, additional, Wells, Geoffrey, additional, Mysore, Venkatesh, additional, Gibbs, Thomas, additional, Phillips, James, additional, Chennubhotla, S Chakra, additional, Foster, Ian, additional, Stevens, Rick, additional, Anandkumar, Anima, additional, Vishwanath, Venkatram, additional, Stone, John E, additional, Tajkhorshid, Emad, additional, Harris, Sarah A, additional, and Ramanathan, Arvind, additional

Published
2022
Full Text
View/download PDF

84. Image Gradient Decomposition for Parallel and Memory-Efficient Ptychographic Reconstruction

Author

Xiao Wang, Aristeidis Tsaris, Debangshu Mukherjee, Mohamed Wahib, Peng Chen, Mark Oxley, Olga Ovchinnikova, and Jacob Hinkle

Subjects
FOS: Computer and information sciences, Computer Science - Distributed, Parallel, and Cluster Computing, Image and Video Processing (eess.IV), FOS: Electrical engineering, electronic engineering, information engineering, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Distributed, Parallel, and Cluster Computing (cs.DC), Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Ptychography is a popular microscopic imaging modality for many scientific discoveries and sets the record for highest image resolution. Unfortunately, the high image resolution for ptychographic reconstruction requires significant amount of memory and computations, forcing many applications to compromise their image resolution in exchange for a smaller memory footprint and a shorter reconstruction time. In this paper, we propose a novel image gradient decomposition method that significantly reduces the memory footprint for ptychographic reconstruction by tessellating image gradients and diffraction measurements into tiles. In addition, we propose a parallel image gradient decomposition method that enables asynchronous point-to-point communications and parallel pipelining with minimal overhead on a large number of GPUs. Our experiments on a Titanate material dataset (PbTiO3) with 16632 probe locations show that our Gradient Decomposition algorithm reduces memory footprint by 51 times. In addition, it achieves time-to-solution within 2.2 minutes by scaling to 4158 GPUs with a super-linear strong scaling efficiency at 364% compared to runtimes at 6 GPUs. This performance is 2.7 times more memory efficient, 9 times more scalable and 86 times faster than the state-of-the-art algorithm.

Published
2022

85. 2021 Operational Assessment - OLCF

Author

Subil Abraham, Paul Abston, Ryan Adamson, Valentine Anantharaj, Aaron Barlow, Ashley Barker, Tom Beck, Katie Bethea, Adam Carlyle, Dan Dietz, Christopher Fuson, Jason Hill, Ryan Landfield, Don Maxwell, Rachel McDowell, Bronson Miller, Sheila Moore, Sarp Oral, Thomas Papatheodore, Suzanne Parete-Koon, Ryan Prout, Sherry Ray, William Renaud, Jim Rogers II, Mallikarjun (Arjun) Shankar, Woong Shin, Scott Simmerman, Betsy Sonewald, Kevin Thach, Aristeidis (aris) Tsaris, Georgia Tourassi, Justin Whitt, Junqi Yin, Benjamin Hernandez Arreguin, and Veronica Melesse Vergara

Published
2022
Full Text
View/download PDF

86. Intelligent resolution: Integrating Cryo-EM with AI-driven multi-resolution simulations to observe the severe acute respiratory syndrome coronavirus-2 replication-transcription machinery in action

Author

Trifan, Anda, Gorgun, Defne, Salim, Michael, Li, Zongyi, Brace, Alexander, Zvyagin, Maxim, Ma, Heng, Clyde, Austin, Clark, David, Hardy, David J., Burnley, Tom, Huang, Lei, McCalpin, John, Emani, Murali, Yoo, Hyenseung, Yin, Junqi, Tsaris, Aristeidis, Subbiah, Vishal, Raza, Tanveer, Liu, Jessica, Trebesch, Noah, Wells, Geoffrey, Mysore, Venkatesh, Gibbs, Thomas, Phillips, James, Chennubhotla, S. Chakra, Foster, Ian, Stevens, Rick, Anandkumar, Anima, Vishwanath, Venkatram, Stone, John E., Tajkhorshid, Emad, Harris, Sarah A., Ramanathan, Arvind, Trifan, Anda, Gorgun, Defne, Salim, Michael, Li, Zongyi, Brace, Alexander, Zvyagin, Maxim, Ma, Heng, Clyde, Austin, Clark, David, Hardy, David J., Burnley, Tom, Huang, Lei, McCalpin, John, Emani, Murali, Yoo, Hyenseung, Yin, Junqi, Tsaris, Aristeidis, Subbiah, Vishal, Raza, Tanveer, Liu, Jessica, Trebesch, Noah, Wells, Geoffrey, Mysore, Venkatesh, Gibbs, Thomas, Phillips, James, Chennubhotla, S. Chakra, Foster, Ian, Stevens, Rick, Anandkumar, Anima, Vishwanath, Venkatram, Stone, John E., Tajkhorshid, Emad, Harris, Sarah A., and Ramanathan, Arvind

Abstract

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) replication transcription complex (RTC) is a multi-domain protein responsible for replicating and transcribing the viral mRNA inside a human cell. Attacking RTC function with pharmaceutical compounds is a pathway to treating COVID-19. Conventional tools, e.g. cryo-electron microscopy and all-atom molecular dynamics (AAMD), do not provide sufficiently high resolution or timescale to capture important dynamics of this molecular machine. Consequently, we develop an innovative workflow that bridges the gap between these resolutions, using mesoscale fluctuating finite element analysis (FFEA) continuum simulations and a hierarchy of AI-methods that continually learn and infer features for maintaining consistency between AAMD and FFEA simulations. We leverage a multi-site distributed workflow manager to orchestrate AI, FFEA, and AAMD jobs, providing optimal resource utilization across HPC centers. Our study provides unprecedented access to study the SARS-CoV-2 RTC machinery, while providing general capability for AI-enabled multi-resolution simulations at scale.

Published
2022

90. Intelligent Resolution: Integrating Cryo-EM with AI-driven Multi-resolution Simulations to Observe the SARS-CoV-2 Replication-Transcription Machinery in Action

Author

Junqi Yin, Tom Burnley, Tom Gibbs, Venkatram Vishwanath, Emad Tajkhorshid, Sarah A. Harris, Murali Emani, Vishal Subbiah, Lei Huang, John McCalpin, David A Clark, Jessica Liu, Arvind Ramanathan, Austin Clyde, Zongyi Li, Anda Trifan, James Phillips, Hyunseung Yoo, Rick Stevens, Ian Foster, A. Tsaris, Maxim Zvyagin, Geoffrey Wells, Michael A. Salim, Alexander Brace, John E. Stone, Animashree Anandkumar, Chakra Chennubhotla, David J. Hardy, Heng Ma, Venkatesh Mysore, Noah Trebesch, and Defne Gorgun

Subjects
Consistency (database systems), Workflow, Computer science, Cryo-electron microscopy, Distributed computing, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Leverage (statistics), Resolution (logic), Replication (computing), Molecular machine
Abstract

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) replication transcription complex (RTC) is a multi-domain protein responsible for replicating and transcribing the viral mRNA inside a human cell. Attacking RTC function with pharmaceutical compounds is a pathway to treating COVID-19. Conventional tools, e.g., cryo-electron microscopy and all-atom molecular dynamics (AAMD), do not provide sufficiently high resolution or timescale to capture important dynamics of this molecular machine. Consequently, we develop an innovative workflow that bridges the gap between these resolutions, using mesoscale fluctuating finite element analysis (FFEA) continuum simulations and a hierarchy of AI-methods that continually learn and infer features for maintaining consistency between AAMD and FFEA simulations. We leverage a multi-site distributed workflow manager to orchestrate AI, FFEA, and AAMD jobs, providing optimal resource utilization across HPC centers. Our study provides unprecedented access to study the SARS-CoV-2 RTC machinery, while providing general capability for AI-enabled multi-resolution simulations at scale.

Published
2021
Full Text
View/download PDF

91. Extended search for supernovalike neutrinos in NOvA coincident with LIGO/Virgo detections

Author

Acero, M. A., Adamson, P., Aliaga, L., Anfimov, N., Antoshkin, A., Arrieta-Diaz, E., Asquith, L., Aurisano, A., Back, A., Backhouse, C., Baird, M., Balashov, N., Baldi, P., Bambah, B. A., Bashar, S., Bays, K., Bernstein, R., Bhatnagar, V., Bhuyan, B., Bian, J., Blair, J., Booth, A. C., Bowles, R., Bromberg, C., Buchanan, N., Butkevich, A., Calvez, S., Carroll, T. J., Catano-Mur, E., Choudhary, B. C., Christensen, A., Coan, T. E., Colo, M., Corwin, L., Cremonesi, L., Davies, G. S., Derwent, P. F., Ding, P., Djurcic, Z., Dolce, M., Doyle, D., Dueñas Tonguino, D., Dukes, E. C., Duyang, H., Edayath, S., Ehrlich, R., Elkins, M., Ewart, E., Feldman, G. J., Filip, P., Franc, J., Frank, M. J., Gallagher, H. R., Gandrajula, R., Gao, F., Giri, A., Gomes, R. A., Goodman, M. C., Grichine, V., Groh, M., Group, R., Guo, B., Habig, A., Hakl, F., Hall, A., Hartnell, J., Hatcher, R., Hatzikoutelis, A., Hausner, H., Heller, K., Hewes, J., Himmel, A., Holin, A., Huang, J., Jargowsky, B., Jarosz, J., Jediny, F., Johnson, C., Judah, M., Kakorin, I., Kalra, D., Kaplan, D. M., Kalitkina, A., Keloth, R., Klimov, O., Koerner, L. W., Kolupaeva, L., Kotelnikov, S., Kralik, R., Kullenberg, Ch., Kubu, M., Kumar, A., Kuruppu, C. D., Kus, V., Lackey, T., Lang, K., Lasorak, P., Lesmeister, J., Lin, S., Lister, A., Liu, J., Lokajicek, M., Magill, S., Manrique Plata, M., Mann, W. A., Marshak, M. L., Martinez-Casales, M., Matveev, V., Mayes, B., Méndez, D. P., Messier, M. D., Meyer, H., Miao, T., Miller, W. H., Mishra, S. R., Mislivec, A., Mohanta, R., Moren, A., Morozova, A., Mu, W., Mualem, L., Muether, M., Mufson, S., Mulder, K., Naples, D., Nayak, N., Nelson, J. K., Nichol, R., Niner, E., Norman, A., Norrick, A., Nosek, T., Oh, H., Olshevskiy, A., Olson, T., Ott, J., Paley, J., Patterson, R. B., Pawloski, G., Petrova, O., Petti, R., Phan, D. D., Plunkett, R. K., Porter, J. C. C., Rafique, A., Psihas, F., Raj, V., Rajaoalisoa, M., Ramson, B., Rebel, B., Rojas, P., Ryabov, V., Samoylov, O., Sanchez, M. C., Sánchez Falero, S., Shanahan, P., Sheshukov, A., Singh, P., Singh, V., Smith, E., Smolik, J., Snopok, P., Solomey, N., Sousa, A., Soustruznik, K., Strait, M., Suter, L., Sutton, A., Swain, S., Sweeney, C., Tapia Oregui, B., Tas, P., Thakore, T., Thayyullathil, R. B., Thomas, J., Tiras, E., Tripathi, J., Trokan-Tenorio, J., Tsaris, A., Torun, Y., Urheim, J., Vahle, P., Vallari, Z., Vasel, J., Vokac, P., Vrba, T., Wallbank, M., Warburton, T. K., Wetstein, M., Whittington, D., Wickremasinghe, D. A., Wojcicki, S. G., Wolcott, J., Wu, W., Xiao, Y., Yallappa Dombara, A., Yonehara, K., Yu, S., Yu, Y., Zadorozhnyy, S., Zalesak, J., Zhang, Y., and Zwaska, R.

Subjects
Physics, High Energy Astrophysical Phenomena (astro-ph.HE), Gravitational wave, Research areas, Physics::Instrumentation and Detectors, Astrophysics::High Energy Astrophysical Phenomena, Detector, Astronomy, FOS: Physical sciences, Nova (laser), Astrophysics::Cosmology and Extragalactic Astrophysics, LIGO, High Energy Physics - Experiment, General Relativity and Quantum Cosmology, High Energy Physics - Experiment (hep-ex), Neutrino detector, Coincident, High Energy Physics::Experiment, Neutrino, Astrophysics - High Energy Astrophysical Phenomena, Astrophysics::Galaxy Astrophysics
Abstract

A search is performed for supernova-like neutrino interactions coincident with 76 gravitational wave events detected by the LIGO/Virgo Collaboration. For 40 of these events, full readout of the time around the gravitational wave is available from the NOvA Far Detector. For these events, we set limits on the fluence of the sum of all neutrino flavors of $F < 7(4)\times 10^{10}\mathrm{cm}^{-2}$ at 90% C.L. assuming energy and time distributions corresponding to the Garching supernova models with masses 9.6(27)$\mathrm{M}_\odot$. Under the hypothesis that any given gravitational wave event was caused by a supernova, this corresponds to a distance of $r > 29(50)$kpc at 90% C.L. Weaker limits are set for other gravitational wave events with partial Far Detector data and/or Near Detector data., 10 pages, 2 figures

Published
2021

93. Language Models for the Prediction of SARS-CoV-2 Inhibitors

Author

Blanchard, Andrew E, primary, Gounley, John, additional, Bhowmik, Debsindhu, additional, Shekar, Mayanka Chandra, additional, Lyngaas, Isaac, additional, Gao, Shang, additional, Yin, Junqi, additional, Tsaris, Aristeidis, additional, Wang, Feiyi, additional, and Glaser, Jens, additional

Published
2021
Full Text
View/download PDF

95. MLPerf™ HPC: A Holistic Benchmark Suite for Scientific Machine Learning on HPC Systems

Author

Farrell, Steven, primary, Emani, Murali, additional, Balma, Jacob, additional, Drescher, Lukas, additional, Drozd, Aleksandr, additional, Fink, Andreas, additional, Fox, Geoffrey, additional, Kanter, David, additional, Kurth, Thorsten, additional, Mattson, Peter, additional, Mu, Dawei, additional, Ruhela, Amit, additional, Sato, Kento, additional, Shirahata, Koichi, additional, Tabaru, Tsuguchika, additional, Tsaris, Aristeidis, additional, Balewski, Jan, additional, Cumming, Ben, additional, Danjo, Takumi, additional, Domke, Jens, additional, Fukai, Takaaki, additional, Fukumoto, Naoto, additional, Fukushi, Tatsuya, additional, Gerofi, Balazs, additional, Honda, Takumi, additional, Imamura, Toshiyuki, additional, Kasagi, Akihiko, additional, Kawakami, Kentaro, additional, Kudo, Shuhei, additional, Kuroda, Akiyoshi, additional, Martinasso, Maxime, additional, Matsuoka, Satoshi, additional, Mendonca, Henrique, additional, Minami, Kazuki, additional, Ram, Prabhat, additional, Sawada, Takashi, additional, Shankar, Mallikarjun, additional, John, Tom St., additional, Tabuchi, Akihiro, additional, Vishwanath, Venkatram, additional, Wahib, Mohamed, additional, Yamazaki, Masafumi, additional, and Yin, Junqi, additional

Published
2021
Full Text
View/download PDF

96. Intelligent Resolution: Integrating Cryo-EM with AI-driven Multi-resolution Simulations to Observe the SARS-CoV-2 Replication-Transcription Machinery in Action

Author

Trifan, Anda, primary, Gorgun, Defne, additional, Li, Zongyi, additional, Brace, Alexander, additional, Zvyagin, Maxim, additional, Ma, Heng, additional, Clyde, Austin, additional, Clark, David, additional, Salim, Michael, additional, Hardy, David J., additional, Burnley, Tom, additional, Huang, Lei, additional, McCalpin, John, additional, Emani, Murali, additional, Yoo, Hyenseung, additional, Yin, Junqi, additional, Tsaris, Aristeidis, additional, Subbiah, Vishal, additional, Raza, Tanveer, additional, Liu, Jessica, additional, Trebesch, Noah, additional, Wells, Geoffrey, additional, Mysore, Venkatesh, additional, Gibbs, Thomas, additional, Phillips, James, additional, Chennubhotla, S. Chakra, additional, Foster, Ian, additional, Stevens, Rick, additional, Anandkumar, Anima, additional, Vishwanath, Venkatram, additional, Stone, John E., additional, Tajkhorshid, Emad, additional, Harris, Sarah A., additional, and Ramanathan, Arvind, additional

Published
2021
Full Text
View/download PDF

98. The HEP.TrkX Project: deep neural networks for HL-LHC online and offline tracking

Author

Farrell Steven, Anderson Dustin, Calafiura Paolo, Cerati Giuseppe, Gray Lindsey, Kowalkowski Jim, Mudigonda Mayur, Prabhat, Spentzouris Panagiotis, Spiropoulou Maria, Tsaris Aristeidis, Vlimant Jean-Roch, and Zheng Stephan

Subjects
Physics, QC1-999
Abstract

Particle track reconstruction in dense environments such as the detectors of the High Luminosity Large Hadron Collider (HL-LHC) is a challenging pattern recognition problem. Traditional tracking algorithms such as the combinatorial Kalman Filter have been used with great success in LHC experiments for years. However, these state-of-the-art techniques are inherently sequential and scale poorly with the expected increases in detector occupancy in the HL-LHC conditions. The HEP.TrkX project is a pilot project with the aim to identify and develop cross-experiment solutions based on machine learning algorithms for track reconstruction. Machine learning algorithms bring a lot of potential to this problem thanks to their capability to model complex non-linear data dependencies, to learn effective representations of high-dimensional data through training, and to parallelize easily on high-throughput architectures such as GPUs. This contribution will describe our initial explorations into this relatively unexplored idea space. We will discuss the use of recurrent (LSTM) and convolutional neural networks to find and fit tracks in toy detector data.

Published
2017
Full Text
View/download PDF

99. Search for Active-Sterile Antineutrino Mixing Using Neutral-Current Interactions with the NOvA Experiment

Author

M A, Acero, P, Adamson, L, Aliaga, N, Anfimov, A, Antoshkin, E, Arrieta-Diaz, L, Asquith, A, Aurisano, A, Back, C, Backhouse, M, Baird, N, Balashov, P, Baldi, B A, Bambah, S, Bashar, K, Bays, R, Bernstein, V, Bhatnagar, B, Bhuyan, J, Bian, J, Blair, A C, Booth, R, Bowles, C, Bromberg, N, Buchanan, A, Butkevich, S, Calvez, T J, Carroll, E, Catano-Mur, B C, Choudhary, A, Christensen, T E, Coan, M, Colo, L, Cremonesi, G S, Davies, P F, Derwent, P, Ding, Z, Djurcic, M, Dolce, D, Doyle, D, Dueñas Tonguino, E C, Dukes, H, Duyang, S, Edayath, R, Ehrlich, M, Elkins, E, Ewart, G J, Feldman, P, Filip, J, Franc, M J, Frank, H R, Gallagher, R, Gandrajula, F, Gao, A, Giri, R A, Gomes, M C, Goodman, V, Grichine, M, Groh, R, Group, B, Guo, A, Habig, F, Hakl, A, Hall, J, Hartnell, R, Hatcher, H, Hausner, K, Heller, J, Hewes, A, Himmel, A, Holin, J, Huang, B, Jargowsky, J, Jarosz, F, Jediny, C, Johnson, M, Judah, I, Kakorin, D, Kalra, A, Kalitkina, D M, Kaplan, R, Keloth, O, Klimov, L W, Koerner, L, Kolupaeva, S, Kotelnikov, R, Kralik, Ch, Kullenberg, M, Kubu, A, Kumar, C D, Kuruppu, V, Kus, T, Lackey, P, Lasorak, K, Lang, J, Lesmeister, S, Lin, A, Lister, J, Liu, M, Lokajicek, S, Magill, M, Manrique Plata, W A, Mann, M L, Marshak, M, Martinez-Casales, V, Matveev, B, Mayes, D P, Méndez, M D, Messier, H, Meyer, T, Miao, W H, Miller, S R, Mishra, A, Mislivec, R, Mohanta, A, Moren, A, Morozova, W, Mu, L, Mualem, M, Muether, K, Mulder, D, Naples, N, Nayak, J K, Nelson, R, Nichol, E, Niner, A, Norman, A, Norrick, T, Nosek, H, Oh, A, Olshevskiy, T, Olson, J, Ott, J, Paley, R B, Patterson, G, Pawloski, O, Petrova, R, Petti, D D, Phan, R K, Plunkett, J C C, Porter, A, Rafique, V, Raj, M, Rajaoalisoa, B, Ramson, B, Rebel, P, Rojas, V, Ryabov, O, Samoylov, M C, Sanchez, S, Sánchez Falero, P, Shanahan, A, Sheshukov, P, Singh, V, Singh, E, Smith, J, Smolik, P, Snopok, N, Solomey, A, Sousa, K, Soustruznik, M, Strait, L, Suter, A, Sutton, S, Swain, C, Sweeney, B, Tapia Oregui, P, Tas, T, Thakore, R B, Thayyullathil, J, Thomas, E, Tiras, J, Tripathi, J, Trokan-Tenorio, A, Tsaris, Y, Torun, J, Urheim, P, Vahle, Z, Vallari, J, Vasel, P, Vokac, T, Vrba, M, Wallbank, T K, Warburton, M, Wetstein, D, Whittington, D A, Wickremasinghe, S G, Wojcicki, J, Wolcott, W, Wu, Y, Xiao, A, Yallappa Dombara, K, Yonehara, S, Yu, Y, Yu, S, Zadorozhnyy, J, Zalesak, Y, Zhang, and R, Zwaska

Subjects
Physics, Particle physics, Neutral current, Oscillation, FOS: Physical sciences, General Physics and Astronomy, Nova (laser), NuMI, High Energy Physics - Experiment, High Energy Physics - Experiment (hep-ex), High Energy Physics::Experiment, Fermilab, Beam (structure), Mixing (physics), Bar (unit)
Abstract

This Letter reports results from the first long-baseline search for sterile antineutrinos mixing in an accelerator-based antineutrino-dominated beam. The rate of neutral-current interactions in the two NOvA detectors, at distances of 1 km and 810 km from the beam source, is analyzed using an exposure of $12.51\times10^{20}$ protons-on-target from the NuMI beam at Fermilab running in antineutrino mode. A total of $121$ of neutral-current candidates are observed at the Far Detector, compared to a prediction of $122\pm11$(stat.)$\pm15$(syst.) assuming mixing between three active flavors. No evidence for $\bar{\nu}_{\mu}\rightarrow\bar{\nu}_{s}$ oscillation is observed. Interpreting this result within a 3+1 model, constraints are placed on the mixing angles ${\theta}_{24} < 25^{\circ}$ and ${\theta}_{34} < 32^{\circ}$ at the 90% C.L. for $0.05$eV$^{2} \leq \Delta m^{2}_{41} \leq 0.5$eV$^{2}$, the range of mass splittings that produces no significant oscillations at the Near Detector. These are the first 3+1 confidence limits set using long-baseline accelerator antineutrinos., Comment: 8 pages, 4 figures

Published
2021

100. IMPECCABLE: Integrated Modeling PipelinE for COVID Cure by Assessing Better LEads

Author

Saadi, Aymen Al, primary, Alfe, Dario, additional, Babuji, Yadu, additional, Bhati, Agastya, additional, Blaiszik, Ben, additional, Brace, Alexander, additional, Brettin, Thomas, additional, Chard, Kyle, additional, Chard, Ryan, additional, Clyde, Austin, additional, Coveney, Peter, additional, Foster, Ian, additional, Gibbs, Tom, additional, Jha, Shantenu, additional, Keipert, Kristopher, additional, Kranzlmüller, Dieter, additional, Kurth, Thorsten, additional, Lee, Hyungro, additional, Li, Zhuozhao, additional, Ma, Heng, additional, Mathias, Gerald, additional, Merzky, Andre, additional, Partin, Alexander, additional, Ramanathan, Arvind, additional, Shah, Ashka, additional, Stern, Abraham, additional, Stevens, Rick, additional, Tan, Li, additional, Titov, Mikhail, additional, Trifan, Anda, additional, Tsaris, Aristeidis, additional, Turilli, Matteo, additional, Van Dam, Huub, additional, Wan, Shunzhou, additional, Wifling, David, additional, and Yin, Junqi, additional

Published
2021
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results