Présidents de session
Tuesday afternoon
- Yann Coadou (CPPM, Aix-Marseille Université, CNRS/IN2P3)
- Viatcheslav Sharyy (IRFU, CEA, Saclay, France)
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Jalal Fadili (CNRS/INS2I)27/09/2022 14:00
https://www.cnrs.fr/fr/le-centre-artificial-intelligence-science-science-artificial-intelligence-aissai
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Yann Coadou (CPPM, Aix-Marseille Université, CNRS/IN2P3)27/09/2022 14:206 ML training, courses, tutorial, open datasets and challenges
The 2022 edition of the School of Statistics SOS2022 was held in Carry-le-Rouet (13) from 16 to 20 May 2022. The school targets PhD students, post-docs and junior/senior scientists (researchers, engineers) wishing to strengthen their knowledge or discover new methods in statistical analysis applied to particle and astroparticle physics, cosmology and nuclear physics.
The programme covers...
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Sylvain Caillou (L2I Toulouse, CNRS/IN2P3)27/09/2022 14:401 ML for object identification and reconstruction
Graph Neural Network (GNN)-based algorithms have been shown to produce competitive physics performance for the reconstruction of tracks from charged particles (« tracking ») during the future high-luminosity phase of the LHC (HL-LHC). Initial studies [1,2] of these algorithms were based on the dataset from the TrackML challenge [3], i.e. a simulated dataset created with a number of simplifying...
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Polina Simkina (CEA-Saclay)27/09/2022 15:001 ML for object identification and reconstruction
Machine Learning (ML) algorithms are currently a leading choice for Data Analysis applications in various fields: from industry to science and medicine. Following the general trend, different ML methods (Boosted Decision Trees, Neural Networks) have already been successfully used for data reconstruction and analysis in the CMS experiment. More sophisticated algorithms are becoming available,...
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Andrii Lobasenko (CEA-Saclay/IRFU/DPhN)27/09/2022 15:201 ML for object identification and reconstruction
The PandaX-III experiment, developed to search for the Neutrinoless Double-beta decay (NLDBD), is based on a Time Projection Chamber (TPC) detector of cylindrical shape with a height of 120.0 cm, a diameter of 160.0 cm. It is filled with 10 bar gaseous Xe-136, and the readout plane is made out of 52 Micromegas modules 20 by 20 cm in size. Each Micromegas module is constructed with 64 by 64...
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Ana Paula Pereira Peixoto (Laboratoire de Physique Subatomique et de Cosmologie de Grenoble (LPSC/CNRS))27/09/2022 16:101 ML for object identification and reconstruction
Hadronic jets are essential components of analysis at the LHC. Not only their Energy and mass needs to be precisely measured, their internal structure is also essential in order to distinguish signal jets from the common QCD initiated background jets. However jet constituents representing the energy flow insside jets do not have 1-to-1 correspondence with hadrons generated in simulations. In...
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Etienne Russeil (Université Clermont Auvergne, LPC, Clermont Ferrand, France)27/09/2022 16:307 ML for phenomenology and theory (only if does not fit in Tracks above)
Symbolic Regression is a data-driven method that searches the space of mathematical equations with the goal of finding the best analytical representation of a given dataset. It is a very powerful tool, which enables the emergence of underlying behavior governing the data generation process. Furthermore, in the case of physical equations, obtaining an analytical form adds a layer of...
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Alexandre Hakimi (LLR, école polytechnique/CNRS)27/09/2022 16:504 Fast ML : Application of Machine Learning to DAQ/Trigger/Real Time Analysis
The CMS collaboration has chosen a novel High-Granularity Calorimeter (HGCAL) for the endcap regions as part of its planned upgrade for the high luminosity LHC. The high granularity of the detector is crucial for disentangling showers overlapped with high levels of pileup events (140 or more per bunch crossing at HL-LHC). But the reconstruction of the complex events and rejection of background...
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Volker Beckmann (MESR / DGRI / SSRI / A7)27/09/2022 17:10
L'European Open Science Cloud (EOSC) vise à fournir aux chercheurs européens un accès transparent aux données, services et e-infrastructures FAIR. L'objectif est d'améliorer la productivité de la recherche en général. En tant que tel, l'EOSC est également un pilier de la transition numérique en France, qui comprend des efforts pour mutualiser les services et les e-infrastructures au profit de...
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