2025-07-03 18:22 |
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2025-07-03 18:19 |
An Implementation of Neural Simulation-Based Inference for Parameter Estimation at the LHC
/ Sandesara, Jay Ajitbhai (University of Wisconsin Madison (US))
/ATLAS Collaboration
Neural simulation-based inference is a powerful class of machine-learning-based methods for statistical inference that naturally handles high-dimensional parameter estimation without the need to bin data into low-dimensional summary histograms. Such methods are promising for a range of measurements, including 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-PHYS-SLIDE-2025-290.-
Geneva : CERN, 2025
Fulltext: PDF; External link: Original Communication (restricted to ATLAS)
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2025-07-01 14:48 |
Recent Heavy Flavor Results in ATLAS
/ Gentry, Andrew Donald (University of New Mexico (US))
/ATLAS Collaboration
Studying heavy-flavour hadron properties provides a extensive tests for various QCD predictions as well as a means to probe the Standard Model validity. ATLAS experiment, being a general-purpose detector at LHC, is particularly successful in such measurements with final states involving muons, thanks to large collected integrated luminosity and precise muon reconstruction and triggering. [...]
ATL-PHYS>-SLIDE-2025-289.-
Geneva : CERN, 2025 - 26 p.
Fulltext: CIPANP2025_PresFinal - PDF; ATL-PHYS>-SLIDE-2025-289 - PDF; External link: Original Communication (restricted to ATLAS)
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2025-07-01 13:31 |
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2025-06-27 12:51 |
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2025-06-26 10:37 |
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2025-06-25 15:32 |
Quark-Gluon Constituent-Based Jet Taggers for the HL-LHC
/ Castillo, Florencia Luciana (Centre National de la Recherche Scientifique (FR))
/ATLAS Collaboration
Jet constituents provide a more detailed description of the radiation pattern within a jet compared to observables summarizing global jet properties. In Run 2 analyses at the LHC using the ATLAS detector, transformer-based taggers leveraging low-level variables outperformed traditional approaches based on high-level variables and conventional neural networks in distinguishing quark- and gluon-initiated jets. [...]
ATL-PHYS-SLIDE-2025-285.-
Geneva : CERN, 2025 - 1 p.
Fulltext: PDF; External link: Original Communication (restricted to ATLAS)
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2025-06-25 15:11 |
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2025-06-24 23:43 |
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2025-06-24 15:34 |
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