Exploring the CERN Open Data using Open Software
Room B | Tue 21 Jan 10:45 a.m.–11:30 a.m.
Presented by
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Aman Desai is a PhD student at the University of Adelaide and works on the ATLAS experiment as well the Future Circular Collider (FCC-ee).
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Albert is a PhD student at the University of Adelaide in the field of high energy particle physics, where Linux and free open source software is used daily to analyse the wealth of data available within the field from contemporary experiments.
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Particle physicist.
Abstract
The Large Hadron Collider (LHC) at the European Organization for Nuclear Research (CERN) is the world’s largest particle accelerator that collides protons and ions with the aim of understanding physics at the most fundamental level. The ATLAS experiment located at the LHC is the one of the four detectors that detects the outcome of these collisions and reconstructs the underlying physics. In 2012, the ATLAS and CMS experiment discovered the Higgs Boson which completed the main predictions of the Standard Model of Particle Physics. However, it is well-known that the Standard Model of Particle Physics fails to explain certain aspects of nature and thus there have been theories and models (lumped into Beyond the Standard Model) that try to overcome the shortcomings of the Standard Model. Dedicated searches are being carried out by experiments to find evidence for some of the predictions of Beyond the Standard Model Physics like the candidate(s) for the Dark Matter and Supersymmetry among other things. Over the years, CERN has released over five petabytes of data for public access. In July 2024, the ATLAS experiment released about 65 terabytes of proton-proton collision data that corresponds to over seven billion LHC collision events. These data were collected during 2015 and 2016 as part of LHC Run 2. These data are accompanied by documentation that illustrates its usage. Open data allows scientists and computer experts to explore collider datasets that may be otherwise available only to scientists affiliated with CERN.
The purpose of this talk is to give a walkthrough of how one may access open data and various open-software tools to explore ATLAS open data as well as to give a glimpse of physics that may be carried out using these tools. We further explore the possibility of using these open data sets to develop machine learning models that may be trained on these dataset - thereby exploring how open-source Machine Learning software plays an important role at the collider experiments.
The Large Hadron Collider (LHC) at the European Organization for Nuclear Research (CERN) is the world’s largest particle accelerator that collides protons and ions with the aim of understanding physics at the most fundamental level. The ATLAS experiment located at the LHC is the one of the four detectors that detects the outcome of these collisions and reconstructs the underlying physics. In 2012, the ATLAS and CMS experiment discovered the Higgs Boson which completed the main predictions of the Standard Model of Particle Physics. However, it is well-known that the Standard Model of Particle Physics fails to explain certain aspects of nature and thus there have been theories and models (lumped into Beyond the Standard Model) that try to overcome the shortcomings of the Standard Model. Dedicated searches are being carried out by experiments to find evidence for some of the predictions of Beyond the Standard Model Physics like the candidate(s) for the Dark Matter and Supersymmetry among other things. Over the years, CERN has released over five petabytes of data for public access. In July 2024, the ATLAS experiment released about 65 terabytes of proton-proton collision data that corresponds to over seven billion LHC collision events. These data were collected during 2015 and 2016 as part of LHC Run 2. These data are accompanied by documentation that illustrates its usage. Open data allows scientists and computer experts to explore collider datasets that may be otherwise available only to scientists affiliated with CERN. The purpose of this talk is to give a walkthrough of how one may access open data and various open-software tools to explore ATLAS open data as well as to give a glimpse of physics that may be carried out using these tools. We further explore the possibility of using these open data sets to develop machine learning models that may be trained on these dataset - thereby exploring how open-source Machine Learning software plays an important role at the collider experiments.