RootInteractive tool for multidimensional statistical analysis, machine learning and analytical model validation
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866911835368718336 |
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| author | Ivanov, Marian Ivanov jr, Marian Eulise, Giulio |
| author_facet | Ivanov, Marian Ivanov jr, Marian Eulise, Giulio |
| contents | The ALICE experiment at CERN LHC is specifically designed for investigating heavy ion collisions. The upgraded ALICE accommodates a tenfold increase in PbPb luminosity and a two-order of magnitude surge in minimum bias events. To address the challenges of high detector occupancy and event pile-ups, advanced multidimensional data analysis techniques, including machine learning (ML), are indispensable. Despite ML popularity, the complexity of its models presents interpretation challenges, and oversimplification in analysis often leads to inaccuracies.
Our objective was to develop RootInteractive, a tool for multidimensional statistical analysis. This tool simplifies data analysis across dimensions, visualizes functions with uncertainties, and validates assumptions and approximations. In RootInteractive, it is crucial to easily define the functional composition of analytical parametric and non-parametric functions, exploit symmetries, and define multidimensional invariant functions and corresponding alarms.
RootInteractive adopts a declarative programming paradigm, ensuring userfriendliness for experts, students, and educators. It facilitates interactive visualization, n-dimensional histogramming/projection, and information extraction on both Python,C++ server and client. Data compression, datasets with O(10 to 7) entries and O(25) attributes can be interactively analyzed in a browser with O(0.500-1 GB) size. Representative downsampling and reweighting/pre-aggregation enable the effective analysis of one year of ALICE data for various purposes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_19330 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | RootInteractive tool for multidimensional statistical analysis, machine learning and analytical model validation Ivanov, Marian Ivanov jr, Marian Eulise, Giulio High Energy Physics - Experiment Data Analysis, Statistics and Probability The ALICE experiment at CERN LHC is specifically designed for investigating heavy ion collisions. The upgraded ALICE accommodates a tenfold increase in PbPb luminosity and a two-order of magnitude surge in minimum bias events. To address the challenges of high detector occupancy and event pile-ups, advanced multidimensional data analysis techniques, including machine learning (ML), are indispensable. Despite ML popularity, the complexity of its models presents interpretation challenges, and oversimplification in analysis often leads to inaccuracies. Our objective was to develop RootInteractive, a tool for multidimensional statistical analysis. This tool simplifies data analysis across dimensions, visualizes functions with uncertainties, and validates assumptions and approximations. In RootInteractive, it is crucial to easily define the functional composition of analytical parametric and non-parametric functions, exploit symmetries, and define multidimensional invariant functions and corresponding alarms. RootInteractive adopts a declarative programming paradigm, ensuring userfriendliness for experts, students, and educators. It facilitates interactive visualization, n-dimensional histogramming/projection, and information extraction on both Python,C++ server and client. Data compression, datasets with O(10 to 7) entries and O(25) attributes can be interactively analyzed in a browser with O(0.500-1 GB) size. Representative downsampling and reweighting/pre-aggregation enable the effective analysis of one year of ALICE data for various purposes. |
| title | RootInteractive tool for multidimensional statistical analysis, machine learning and analytical model validation |
| topic | High Energy Physics - Experiment Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2403.19330 |