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1.
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Scalable ATLAS pMSSM computational workflows using containerised REANA reusable analysis platform
/ Donadoni, Marco (CERN) ; Feickert, Matthew (U. Wisconsin, Madison (main)) ; Heinrich, Lukas (Munich, Max Planck Inst.) ; Liu, Yang (SYSU, Guangzhou) ; Mečionis, Audrius (CERN) ; Moisieienkov, Vladyslav (CERN) ; Šimko, Tibor (CERN) ; Stark, Giordon (UC, Santa Cruz, Inst. Part. Phys.) ; García, Marco Vidal (CERN)
In this paper we describe the development of a streamlined framework for large-scale ATLAS pMSSM reinterpretations of LHC Run-2 analyses using containerised computational workflows. The project is looking to assess the global coverage of BSM physics and requires running O(5k) computational workflows representing pMSSM model points. [...]
arXiv:2403.03494.-
2024 - 8 p.
- Published in : EPJ Web Conf. 295 (2024) 04035
Fulltext: document - PDF; 2403.03494 - PDF;
In : 26th International Conference on Computing in High Energy & Nuclear Physics, Norfolk, Virginia, Us, 8 - 12 May 2023, pp.04035
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2.
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Second Analysis Ecosystem Workshop Report
/ Aly, Mohamed (Manchester U.) ; Burzynski, Jackson (Simon Fraser U.) ; Cardwell, Bryan (Virginia U.) ; Craik, Daniel C. (Zurich U.) ; van Daalen, Tal (Washington U., Seattle) ; Dado, Tomas (Dortmund U.) ; Das, Ayanabha (Prague, Tech. U.) ; Delgado Peris, Antonio (Madrid, CIEMAT) ; Doglioni, Caterina (Manchester U.) ; Elmer, Peter (Princeton U.) et al.
The second workshop on the HEP Analysis Ecosystem took place 23-25 May 2022 at IJCLab in Orsay, to look at progress and continuing challenges in scaling up HEP analysis to meet the needs of HL-LHC and DUNE, as well as the very pressing needs of LHC Run 3 analysis. [...]
arXiv:2212.04889 ; HSF-DOC-2022-02 ; FERMILAB-CONF-22-955-PPD.
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2022. - 27 p.
Fermilab Library Server - Fulltext - Fulltext
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3.
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Scalable Declarative HEP Analysis Workflows for Containerised Compute Clouds
/ Šimko, Tibor (CERN) ; Heinrich, Lukas Alexander (CERN) ; Lange, Clemens (CERN) ; Lintuluoto, Adelina Eleonora (CERN ; Helsinki U.) ; MacDonell, Danika Marina (Victoria U.) ; Mečionis, Audrius (CERN) ; Rodríguez Rodríguez, Diego (CERN) ; Shandilya, Parth (CERN ; LNM Inst. Info. Tech.) ; Vidal García, Marco (CERN)
We describe a novel approach for experimental High-Energy Physics (HEP) data analyses that is centred around the declarative rather than imperative paradigm when describing analysis computational tasks. The analysis process can be structured in the form of a Directed Acyclic Graph (DAG), where each graph vertex represents a unit of computation with its inputs and outputs, and the graph edges describe the interconnection of various computational steps. [...]
2021 - 12 p.
- Published in : 10.3389/fdata.2021.661501
Fulltext: PDF;
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4.
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Large-scale HPC deployment of Scalable CyberInfrastructure for Artificial Intelligence and Likelihood Free Inference (SCAILFIN)
/ Hildreth, Michael (Notre Dame U.) ; Hurtado Anampa, Kenyi Paolo (Notre Dame U.) ; Kankel, Cody (Notre Dame U.) ; Hampton, Scott (Notre Dame U.) ; Brenner, Paul (Notre Dame U.) ; Johnson, Irena (Notre Dame U.) ; Simko, Tibor (CERN)
The NSF-funded Scalable CyberInfrastructure for Artificial Intelligence and Likelihood Free Inference (SCAILFIN) project aims to develop and deploy artificial intelligence (AI) and likelihood-free inference (LFI) techniques and software using scalable cyberinfrastructure (CI) built on top of existing CI elements. Specifically, the project has extended the CERN-based REANA framework, a cloud-based data analysis platform deployed on top of Kubernetes clusters that was originally designed to enable analysis reusability and reproducibility. [...]
2020 - 6 p.
- Published in : EPJ Web Conf. 245 (2020) 09011
Fulltext by publisher: PDF;
In : 24th International Conference on Computing in High Energy and Nuclear Physics, Adelaide, Australia, 4 - 8 Nov 2019, pp.09011
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5.
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Open data provenance and reproducibility: a case study from publishing CMS open data
/ Šimko, Tibor (CERN) ; de Bittencourt, Heitor Pascoal (Helsinki Inst. of Phys.) ; Carrera, Edgar (San Francisco de Quito U.) ; Lopez, Diyaselis Delgado (Unlisted, PR) ; Lange, Clemens (CERN) ; Lassila-Perini, Kati (Helsinki Inst. of Phys.) ; Lintuluoto, Adelina (CERN ; Helsinki Inst. of Phys.) ; Iglesias, Lara Lloret (Cantabria Inst. of Phys.) ; McCauley, Thomas (Notre Dame U.) ; Okraska, Jan (CERN) et al.
In this paper we present the latest CMS open data release published on the CERN Oopen Data portal. Samples of collision and simulated datasets were released together with detailed information about the data provenance. [...]
2020 - 8 p.
- Published in : EPJ Web Conf. 245 (2020) 08014
Fulltext from publisher: PDF;
In : 24th International Conference on Computing in High Energy and Nuclear Physics, Adelaide, Australia, 4 - 8 Nov 2019, pp.08014
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6.
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Dataset of tau neutrino interactions recorded by the OPERA experiment
/ De Lellis, Giovanni (Naples U. ; INFN, Naples ; CERN) ; Dmitrievsky, Sergey (Dubna, JINR) ; Galati, Giuliana (INFN, Naples) ; Lavasa, Artemis (CERN) ; Šimko, Tibor (CERN) ; Tsanaktsidis, Ioannis (CERN) ; Ustyuzhanin, Andrey (Higher Sch. of Economics, Moscow ; Natl. U. Sci. Tech., Moscow ; INFN, Naples)
We describe the dataset of very rare events recorded by the OPERA experiment. The events represent tracks of particles associated with tau neutrino interactions coming from the transformation of muon neutrinos due to a process known as neutrino oscillations. [...]
2020 - 7 p.
- Published in : EPJ Web Conf. 245 (2020) 08013
Fulltext from publisher: PDF;
In : 24th International Conference on Computing in High Energy and Nuclear Physics, Adelaide, Australia, 4 - 8 Nov 2019, pp.08013
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7.
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CERN Analysis Preservation and Reuse Framework: FAIR research data services for LHC experiments
/ Fokianos, Pamfilos (CERN) ; Feger, Sebastian (CERN) ; Koutsakis, Ilias (CERN) ; Lavasa, Artemis (CERN) ; Maciulaitis, Rokas (CERN) ; Naim, Kamran (CERN) ; Okraska, Jan (CERN) ; Papadopoulos, Antonios (CERN) ; Rodríguez, Diego (CERN) ; Šimko, Tibor (CERN) et al.
In this paper we present the CERN Analysis Preservation service as a FAIR (Findable, Accessible, Interoperable and Reusable) research data preservation repository platform for LHC experiments. The CERN Analysis Preservation repository allows LHC collaborations to deposit and share the structured information about analyses as well as to capture the individual data assets associated to the analysis. [...]
2020 - 9 p.
- Published in : EPJ Web Conf. 245 (2020) 06011
Fulltext: PDF;
In : 24th International Conference on Computing in High Energy and Nuclear Physics, Adelaide, Australia, 4 - 8 Nov 2019, pp.06011
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8.
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Abstracting container technologies and transfer mechanisms in the Scalable CyberInfrastructure for Artificial Intelligence and Likelihood Free Inference (SCAILFIN) project
/ Hurtado Anampa, Kenyi (Notre Dame U.) ; Kankel, Cody (Notre Dame U.) ; Hildreth, Mike (Notre Dame U.) ; Brenner, Paul (Notre Dame U.) ; Johnson, Irena (Notre Dame U.) ; Hampton, Scott (Notre Dame U.) ; Simko, Tibor (CERN)
High Performance Computing (HPC) facilities provide vast computational power and storage, but generally work on fixed environments designed to address the most common software needs locally, making it challenging for users to bring their own software. To overcome this issue, most HPC facilities have added support for HPC friendly container technologies such as Shifter, Singularity, or Charliecloud. [...]
2020 - 6 p.
- Published in : EPJ Web Conf. 245 (2020) 07023
Fulltext: PDF;
In : 24th International Conference on Computing in High Energy and Nuclear Physics, Adelaide, Australia, 4 - 8 Nov 2019, pp.07023
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9.
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10.
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Overview of the CERN Open Data portal
Reference: Poster-2019-982
Created: 2019. -1 p
Creator(s): Wunsch, Stefan; Simko, Tibor; Serkin, Leonid
The poster presents an overview of the CERN Open Data portal originally created for the public event at CHEP 2019. The content covers the different aspects of the portal including preservation, reproducibility, open science and education and outreach.
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