Pub Date : 2021-01-03DOI: 10.1007/s41781-020-00048-6
G. Amadio, A. Ananya, J. Apostolakis, M. Bandieramonte, S. Banerjee, A. Bhattacharyya, Calebe P. Bianchini, G. Bitzes, P. Canal, F. Carminati, O. Chaparro-Amaro, G. Cosmo, J. D. F. Licht, V. Drogan, L. Duhem, D. Elvira, J. Fuentes, A. Gheata, M. Gheata, M. Gravey, I. Goulas, F. Hariri, S. Jun, D. Konstantinov, H. Kumawat, J. Lima, A. Maldonado-Romo, J. Martínez-Castro, P. Mato, T. Nikitina, S. Novaes, M. Novak, K. Pedro, W. Pokorski, A. Ribon, R. Schmitz, R. Seghal, O. Shadura, E. Tcherniaev, S. Vallecorsa, S. Wenzel, Y. Zhang
{"title":"GeantV","authors":"G. Amadio, A. Ananya, J. Apostolakis, M. Bandieramonte, S. Banerjee, A. Bhattacharyya, Calebe P. Bianchini, G. Bitzes, P. Canal, F. Carminati, O. Chaparro-Amaro, G. Cosmo, J. D. F. Licht, V. Drogan, L. Duhem, D. Elvira, J. Fuentes, A. Gheata, M. Gheata, M. Gravey, I. Goulas, F. Hariri, S. Jun, D. Konstantinov, H. Kumawat, J. Lima, A. Maldonado-Romo, J. Martínez-Castro, P. Mato, T. Nikitina, S. Novaes, M. Novak, K. Pedro, W. Pokorski, A. Ribon, R. Schmitz, R. Seghal, O. Shadura, E. Tcherniaev, S. Vallecorsa, S. Wenzel, Y. Zhang","doi":"10.1007/s41781-020-00048-6","DOIUrl":"https://doi.org/10.1007/s41781-020-00048-6","url":null,"abstract":"","PeriodicalId":36026,"journal":{"name":"Computing and Software for Big Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s41781-020-00048-6","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44500136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01Epub Date: 2021-10-08DOI: 10.1007/s41781-021-00069-9
Sudhir Malik, Samuel Meehan, Kilian Lieret, Meirin Oan Evans, Michel H Villanueva, Daniel S Katz, Graeme A Stewart, Peter Elmer, Sizar Aziz, Matthew Bellis, Riccardo Maria Bianchi, Gianluca Bianco, Johan Sebastian Bonilla, Angela Burger, Jackson Burzynski, David Chamont, Matthew Feickert, Philipp Gadow, Bernhard Manfred Gruber, Daniel Guest, Stephan Hageboeck, Lukas Heinrich, Maximilian M Horzela, Marc Huwiler, Clemens Lange, Konstantin Lehmann, Ke Li, Devdatta Majumder, Judita Mamužić, Kevin Nelson, Robin Newhouse, Emery Nibigira, Scarlet Norberg, Arturo Sánchez Pineda, Mason Proffitt, Brendan Regnery, Amber Roepe, Stefan Roiser, Henry Schreiner, Oksana Shadura, Giordon Stark, Stephen Nicholas Swatman, Savannah Thais, Andrea Valassi, Stefan Wunsch, David Yakobovitch, Siqi Yuan
The long-term sustainability of the high-energy physics (HEP) research software ecosystem is essential to the field. With new facilities and upgrades coming online throughout the 2020s, this will only become increasingly important. Meeting the sustainability challenge requires a workforce with a combination of HEP domain knowledge and advanced software skills. The required software skills fall into three broad groups. The first is fundamental and generic software engineering (e.g., Unix, version control, C++, and continuous integration). The second is knowledge of domain-specific HEP packages and practices (e.g., the ROOT data format and analysis framework). The third is more advanced knowledge involving specialized techniques, including parallel programming, machine learning and data science tools, and techniques to maintain software projects at all scales. This paper discusses the collective software training program in HEP led by the HEP Software Foundation (HSF) and the Institute for Research and Innovation in Software in HEP (IRIS-HEP). The program equips participants with an array of software skills that serve as ingredients for the solution of HEP computing challenges. Beyond serving the community by ensuring that members are able to pursue research goals, the program serves individuals by providing intellectual capital and transferable skills important to careers in the realm of software and computing, inside or outside HEP.
{"title":"Software Training in HEP.","authors":"Sudhir Malik, Samuel Meehan, Kilian Lieret, Meirin Oan Evans, Michel H Villanueva, Daniel S Katz, Graeme A Stewart, Peter Elmer, Sizar Aziz, Matthew Bellis, Riccardo Maria Bianchi, Gianluca Bianco, Johan Sebastian Bonilla, Angela Burger, Jackson Burzynski, David Chamont, Matthew Feickert, Philipp Gadow, Bernhard Manfred Gruber, Daniel Guest, Stephan Hageboeck, Lukas Heinrich, Maximilian M Horzela, Marc Huwiler, Clemens Lange, Konstantin Lehmann, Ke Li, Devdatta Majumder, Judita Mamužić, Kevin Nelson, Robin Newhouse, Emery Nibigira, Scarlet Norberg, Arturo Sánchez Pineda, Mason Proffitt, Brendan Regnery, Amber Roepe, Stefan Roiser, Henry Schreiner, Oksana Shadura, Giordon Stark, Stephen Nicholas Swatman, Savannah Thais, Andrea Valassi, Stefan Wunsch, David Yakobovitch, Siqi Yuan","doi":"10.1007/s41781-021-00069-9","DOIUrl":"https://doi.org/10.1007/s41781-021-00069-9","url":null,"abstract":"<p><p>The long-term sustainability of the high-energy physics (HEP) research software ecosystem is essential to the field. With new facilities and upgrades coming online throughout the 2020s, this will only become increasingly important. Meeting the sustainability challenge requires a workforce with a combination of HEP domain knowledge and advanced software skills. The required software skills fall into three broad groups. The first is fundamental and generic software engineering (e.g., Unix, version control, C++, and continuous integration). The second is knowledge of domain-specific HEP packages and practices (e.g., the ROOT data format and analysis framework). The third is more advanced knowledge involving specialized techniques, including parallel programming, machine learning and data science tools, and techniques to maintain software projects at all scales. This paper discusses the collective software training program in HEP led by the HEP Software Foundation (HSF) and the Institute for Research and Innovation in Software in HEP (IRIS-HEP). The program equips participants with an array of software skills that serve as ingredients for the solution of HEP computing challenges. Beyond serving the community by ensuring that members are able to pursue research goals, the program serves individuals by providing intellectual capital and transferable skills important to careers in the realm of software and computing, inside or outside HEP.</p>","PeriodicalId":36026,"journal":{"name":"Computing and Software for Big Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8497185/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39512945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01Epub Date: 2021-06-09DOI: 10.1007/s41781-021-00060-4
C Chen, O Cerri, T Q Nguyen, J R Vlimant, M Pierini
We present a fast-simulation application based on a deep neural network, designed to create large analysis-specific datasets. Taking as an example the generation of W + jet events produced in 13 TeV proton-proton collisions, we train a neural network to model detector resolution effects as a transfer function acting on an analysis-specific set of relevant features, computed at generation level, i.e., in absence of detector effects. Based on this model, we propose a novel fast-simulation workflow that starts from a large amount of generator-level events to deliver large analysis-specific samples. The adoption of this approach would result in about an order-of-magnitude reduction in computing and storage requirements for the collision simulation workflow. This strategy could help the high energy physics community to face the computing challenges of the future High-Luminosity LHC.
{"title":"Analysis-Specific Fast Simulation at the LHC with Deep Learning.","authors":"C Chen, O Cerri, T Q Nguyen, J R Vlimant, M Pierini","doi":"10.1007/s41781-021-00060-4","DOIUrl":"https://doi.org/10.1007/s41781-021-00060-4","url":null,"abstract":"<p><p>We present a fast-simulation application based on a deep neural network, designed to create large analysis-specific datasets. Taking as an example the generation of <i>W</i> + jet events produced in <math> <mrow><msqrt><mi>s</mi></msqrt> <mo>=</mo></mrow> </math> 13 TeV proton-proton collisions, we train a neural network to model detector resolution effects as a transfer function acting on an analysis-specific set of relevant features, computed at generation level, i.e., in absence of detector effects. Based on this model, we propose a novel fast-simulation workflow that starts from a large amount of generator-level events to deliver large analysis-specific samples. The adoption of this approach would result in about an order-of-magnitude reduction in computing and storage requirements for the collision simulation workflow. This strategy could help the high energy physics community to face the computing challenges of the future High-Luminosity LHC.</p>","PeriodicalId":36026,"journal":{"name":"Computing and Software for Big Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s41781-021-00060-4","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39580513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-07DOI: 10.1007/s41781-020-00051-x
C. Ariza-Porras, V. Kuznetsov, F. Legger
{"title":"The CMS monitoring infrastructure and applications","authors":"C. Ariza-Porras, V. Kuznetsov, F. Legger","doi":"10.1007/s41781-020-00051-x","DOIUrl":"https://doi.org/10.1007/s41781-020-00051-x","url":null,"abstract":"","PeriodicalId":36026,"journal":{"name":"Computing and Software for Big Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s41781-020-00051-x","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48128516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-26DOI: 10.1007/s41781-020-00042-y
L. Arrabito, K. Bernlöhr, J. Bregeon, M. Carrère, A. Khattabi, P. Langlois, David Parello, G. Revy
{"title":"Optimizing Cherenkov Photons Generation and Propagation in CORSIKA for CTA Monte–Carlo Simulations","authors":"L. Arrabito, K. Bernlöhr, J. Bregeon, M. Carrère, A. Khattabi, P. Langlois, David Parello, G. Revy","doi":"10.1007/s41781-020-00042-y","DOIUrl":"https://doi.org/10.1007/s41781-020-00042-y","url":null,"abstract":"","PeriodicalId":36026,"journal":{"name":"Computing and Software for Big Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s41781-020-00042-y","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"53240674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-05-11DOI: 10.1007/s41781-021-00056-0
E. Buhmann, S. Diefenbacher, E. Eren, F. Gaede, G. Kasieczka, A. Korol, K. Krüger
{"title":"Getting High: High Fidelity Simulation of High Granularity Calorimeters with High Speed","authors":"E. Buhmann, S. Diefenbacher, E. Eren, F. Gaede, G. Kasieczka, A. Korol, K. Krüger","doi":"10.1007/s41781-021-00056-0","DOIUrl":"https://doi.org/10.1007/s41781-021-00056-0","url":null,"abstract":"","PeriodicalId":36026,"journal":{"name":"Computing and Software for Big Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141205783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-06DOI: 10.1007/s41781-021-00059-x
C. Badiali, F. Bello, G. Frattari, E. Gross, V. Ippolito, M. Kado, Jonathan Shlomi
{"title":"Efficiency Parameterization with Neural Networks","authors":"C. Badiali, F. Bello, G. Frattari, E. Gross, V. Ippolito, M. Kado, Jonathan Shlomi","doi":"10.1007/s41781-021-00059-x","DOIUrl":"https://doi.org/10.1007/s41781-021-00059-x","url":null,"abstract":"","PeriodicalId":36026,"journal":{"name":"Computing and Software for Big Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s41781-021-00059-x","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49653511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-03-25DOI: 10.1007/s41781-021-00054-2
Y. Iiyama, B. Maier, D. Abercrombie, M. Goncharov, C. Paus
{"title":"Dynamo: Handling Scientific Data Across Sites and Storage Media","authors":"Y. Iiyama, B. Maier, D. Abercrombie, M. Goncharov, C. Paus","doi":"10.1007/s41781-021-00054-2","DOIUrl":"https://doi.org/10.1007/s41781-021-00054-2","url":null,"abstract":"","PeriodicalId":36026,"journal":{"name":"Computing and Software for Big Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141220836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-03-24DOI: 10.1007/s41781-021-00053-3
M. Stanitzki, J. Strube
{"title":"Performance of Julia for High Energy Physics Analyses","authors":"M. Stanitzki, J. Strube","doi":"10.1007/s41781-021-00053-3","DOIUrl":"https://doi.org/10.1007/s41781-021-00053-3","url":null,"abstract":"","PeriodicalId":36026,"journal":{"name":"Computing and Software for Big Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s41781-021-00053-3","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41546428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-03-16DOI: 10.1007/s41781-020-00049-5
Stefan Wunsch, Simon Jörger, R. Wolf, G. Quast
{"title":"Optimal Statistical Inference in the Presence of Systematic Uncertainties Using Neural Network Optimization Based on Binned Poisson Likelihoods with Nuisance Parameters","authors":"Stefan Wunsch, Simon Jörger, R. Wolf, G. Quast","doi":"10.1007/s41781-020-00049-5","DOIUrl":"https://doi.org/10.1007/s41781-020-00049-5","url":null,"abstract":"","PeriodicalId":36026,"journal":{"name":"Computing and Software for Big Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s41781-020-00049-5","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42848064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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