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High-dimensional statistics with neural simulation-based inference: A story told through Higgs width studies / Ghosh, Aishik (University of California Irvine (US)) /ATLAS Collaboration
Keynote talk on neural simulation-based inference in HEP at the Inter-Experimental ML workshop 2025
ATL-SOFT-SLIDE-2025-262.- Geneva : CERN, 2025 Fulltext: PDF; External link: Original Communication (restricted to ATLAS)

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Overcoming challenges of quantum interference at LHC with neural simulation-based inference and a full implementation in ATLAS / Ghosh, Aishik (University of California Irvine (US)) /ATLAS Collaboration
Quantum interference between signal and background Feynman diagrams produce non-linear effects that challenge core assumptions going into the statistical analysis methodology in particle physics. I show that for such cases, no single observable can capture all the relevant information needed to perform optimal inference of theory parameters from data collected in our experiments. [...]
ATL-SOFT-SLIDE-2024-613.- Geneva : CERN, 2024 Fulltext: PDF; External link: Original Communication (restricted to ATLAS)

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Parameter Estimation in ATLAS with Neural Simulation-Based Inference / Ghosh, Aishik (University of California Irvine (US)) /ATLAS Collaboration
Neural Simulation-Based Inference (NSBI) is a powerful class of machine learning (ML)-based methods for statistical inference that naturally handle high dimensional parameter estimation without the need to bin data into low-dimensional summary histograms. Such methods are promising for a range of measurements at the Large Hadron Collider, where no single observable may be optimal to scan over the entire theoretical phase space under consideration, or where binning data into histograms could result in a loss of sensitivity. [...]
ATL-SOFT-SLIDE-2024-508.- Geneva : CERN, 2024 Fulltext: PDF; External link: Original Communication (restricted to ATLAS)
In : 27th International Conference on Computing in High Energy & Nuclear Physics, Kraków, Pl, 19 - 25 Oct 2024

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An implementation of Neural Simulation-Based Inference for Parameter Estimation in ATLAS
Neural Simulation-Based Inference (NSBI) is a powerful class of machine learning (ML)-based methods for statistical inference that naturally handles high-dimensional parameter estimation without the need to bin data into low-dimensional summary histograms. [...]
ATLAS-CONF-2024-015. - 2024 - mult..
Original Communication (restricted to ATLAS) - Full text

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