IN2P3/IRFU Machine Learning workshop

Liste des Contributions

93. Introduction

Alexandre Boucaud (APC / IN2P3), David Rousseau (IJCLab, CNRS/IN2P3, Université Paris-Saclay), Valérie Gautard (CEA-Irfu)

26/09/2022 11:00

Monday morning

94. Round table

26/09/2022 11:20

Monday morning

95. Boosting event generation and inference with new machine learning methods

Anja Butter (ITP U Heidelberg/LPNHE)

26/09/2022 11:40

Monday morning

First-principle simulations are at the heart of the high-energy physics research program. They link the vast data output of multi-purpose detectors with fundamental theory predictions and interpretation. In this talk I will discuss a range of applications of modern machine learning to event generation and simulation-based inference, including conceptional developments driven by the specific...

80. Maximum-A-posteriori estimate with Deep generative NEtworks for Source Separation (MADNESS)

Biswajit Biswas (APC)

26/09/2022 14:00

1 ML for object identification and reconstruction

Monday afternoon

The next generation of astronomical surveys will collect massive amounts of data. It will present challenges not only because of the sheer volume of data but also because of its complexity. In the Legacy Survey of Space and Time (LSST) at the Vera Rubin Observatory, more than 60% of objects along the line of sight are expected to overlap in the images. Classical methods for solving the...

92. Classification of astrophysical image time series using convolutional attention

Anass BAIROUK, Dominique Fouchez (CPPM)

26/09/2022 14:20

1 ML for object identification and reconstruction

Monday afternoon

We will present the collaborative work between astrophysicists and computer scientists to explore classification of optical transients. The optical transient time serie, captured with its scene (usualy a galaxy in the background) is used as raw input into a convolutional attention network to performe supervised learning on several class of astrophysical transient objects. Results and...

63. Convolutional neural networks demultiplexing in large Micromegas detectors for muography purposes

Baptiste Lefevre (CEA/DRF/IRFU/DPhP)

26/09/2022 14:40

1 ML for object identification and reconstruction

Monday afternoon

The muography project at CEA/IRFU uses high precision large gaseous detectors known as multiplexed Micromegas [1] which aim to reduce the cost of the electronic part by accumulating signal amplitudes together from 1037 strips into only 61 channels. Thanks to the properties of this genetic multiplexing [2], conventional algorithms may be and have been developed to recover estimated signals...

64. Machine Learning Algorithms for the Gamma Conversion Reconstruction in the ClearMind Project

Chi-Hsun Sung (IRFU)

26/09/2022 15:00

1 ML for object identification and reconstruction

Monday afternoon

ClearMind project aims to develop the TOF PET detection module providing a high detection efficiency, coincidence resolving time $<100$ ps (FWHM), and spatial resolution in a few mm (FWHM). ClearMind project uses a large monolithic PbWO$_4$ crystal for the position-sensitive detector, microchannel-plate photomultiplier (MCP-PMT), and the bialkali photoelectric layer deposited on the crystal....

91. M4CAST : an emerging national collaborative effort for IA applications to accelerators physics and technologies

Adnan Ghribi ({CNRS}UPR3266)

26/09/2022 15:50

8 ML for particle accelerators (only if does not fit in Tracks above)

Monday afternoon

M4CAST, standing for "Multiphysics Modelling, Machine learning and Model-based Control in Accelerator Sciences and Technologies", is a new collaborative effort gravitating around artificial intelligence applications for accelerator physics and technologies. It intends to bridge accelerators under operation and future projects. It also tries to bring closer various scientific communities. Among...

67. Echo State Networks for Dynamic Aperture prediction

Barbara Dalena (IRFU)

26/09/2022 16:10

8 ML for particle accelerators (only if does not fit in Tracks above)

Monday afternoon

The Reservoir Computing Echo State Network (ESN) are a class of recurrent neural networks that are computationally effective, since they avoid back-propagation and they require cross-validation only.
They have been proven to be universal approximant of dynamical systems.
We present the performance reached by ESN to predict the long term behavior of the extent of the phase space region in...

87. LinacNet: a new architecture for linear accelerator surrogate model

Emmanuel GOUTIERRE ({CNRS}UMR9012)

26/09/2022 16:30

3 ML for simulation and surrogate model : Application of Machine Learning to simulation or other cases where it is deemed to replace an existing complex model

Monday afternoon

Accelerator physics simulations are a powerful tool for optimizing particle accelerator experiments.
They give accurate predictions of the behavior of the beam along the machine according to the values of the input parameters of the machine.
However, simulations can be lengthy, and this computation time can limit their potential application.
Machine Learning can be used to learn...

106. Hackathon to fill in IN2P3 ML web site

26/09/2022 16:50

Monday afternoon

If you want your favourite publication, tutorial, or project to be on https://machine-learning.pages.in2p3.fr/ just stay around

69. An EIM-based compression-extrapolation tool for efficient treatment of homogenized cross-section data

Olivier Truffinet

27/09/2022 09:00

3 ML for simulation and surrogate model : Application of Machine Learning to simulation or other cases where it is deemed to replace an existing complex model

Tuesday morning

Nuclear reactor simulators implementing the widespread two-steps deterministic calculation scheme tend to produce a large volume of intermediate data at the interface of their two subcodes – up to dozens or even hundred of gigabytes – which can be so cumbersome that it hinders the global performance of the code. The vast majority of this data consists of ”few-groups homogenized...

77. Calibration and evaluation of an unsupervised machine learning algorithm for β+ imaging using an intracerebral micro probe

Samir El ketara (IJCLab, Université Paris-Saclay)

27/09/2022 09:20

2 ML for analysis : event classification, statistical analysis and inference, including anomaly detectio

Tuesday morning

Purpose: The correlation of molecular neuroimaging and behavior studies in the preclinical field is of major interest to unlock progress in the understanding of brain processes and assess the validity of preclinical studies in drug development. However, fully achieving such ambition requires being able to perform molecular images of awake and freely moving animals whereas currently, most...

75. Classification of Brain tumour tissues in Human Patients using Machine Learning

Françoise BOUVET (IJCLab)

27/09/2022 09:40

2 ML for analysis : event classification, statistical analysis and inference, including anomaly detectio

Tuesday morning

Delineating brain tumor margins as accurately as possible is a challenge faced by the neurosurgeon during tumor resections. The extent of resection is correlated with the survival rate of the patient while preserving healthy surrounding tissues is necessary. Real-time analysis of the endogenous fluorescence signal of brain tissues is a promising technique to answer this problem. For this...

68. Optimized Reconstruction of the Position of Interaction in High-Performances γ-Cameras

Théo Bossis (IJCLab/CNRS)

27/09/2022 10:00

1 ML for object identification and reconstruction

Tuesday morning

Introduction
Targeted radionuclide therapy is one of the most widespread treatment modality for benign and malignant thyroid diseases. In order to maximize the therapeutic effects on the target tissues while minimizing the toxicity for organs-at-risk with adapted dose, individually defined for each patient taking account their own biokinetics, dedicated $\gamma$-imaging devices are...

105. Dealing with uncertainties in Machine Learning

Geoffrey Daniel (CEA/DES/ISAS/DM2S/STMF/LGLS)

27/09/2022 10:50

6 ML training, courses, tutorial, open datasets and challenges

Tuesday morning

Over the past decade, Deep Learning became an essential approach in many fields, from classical image processing to several scientific and very specific domains. It often shows very promising performances, outperforming human performances in some specific tasks, and even classical methods for some applications. However, because of a lack of theory that can guarantee their performances, the...

100. Scope of IN2P3 ML project

Alexandre Boucaud (APC / IN2P3), David Rousseau (IJCLab, CNRS/IN2P3, Université Paris-Saclay)

27/09/2022 11:40

Tuesday morning

96. AISSAI semestre IN2P3

Jalal Fadili (CNRS/INS2I)

27/09/2022 14:00

Tuesday afternoon

https://www.cnrs.fr/fr/le-centre-artificial-intelligence-science-science-artificial-intelligence-aissai

73. IN2P3 School of Statistics

Yann Coadou (CPPM, Aix-Marseille Université, CNRS/IN2P3)

27/09/2022 14:20

6 ML training, courses, tutorial, open datasets and challenges

Tuesday afternoon

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...

88. Enhanced GNN models for track reconstruction with ATLAS ITk

Sylvain Caillou (L2I Toulouse, CNRS/IN2P3)

27/09/2022 14:40

1 ML for object identification and reconstruction

Tuesday afternoon

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...

85. Reconstruction of electromagnetic showers in calorimeters using Deep Learning

Polina Simkina (CEA-Saclay)

27/09/2022 15:00

1 ML for object identification and reconstruction

Tuesday afternoon

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,...

86. Missing data reconstruction using ML techniques in the gaseous TPC PandaX-III experiment

Andrii Lobasenko (CEA-Saclay/IRFU/DPhN)

27/09/2022 15:20

1 ML for object identification and reconstruction

Tuesday afternoon

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...

76. GNN for hadronic flow calibration in ATLAS

Ana Paula Pereira Peixoto (Laboratoire de Physique Subatomique et de Cosmologie de Grenoble (LPSC/CNRS))

27/09/2022 16:10

1 ML for object identification and reconstruction

Tuesday afternoon

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...

78. Multiview Symbolic Regression

Etienne Russeil (Université Clermont Auvergne, LPC, Clermont Ferrand, France)

27/09/2022 16:30

7 ML for phenomenology and theory (only if does not fit in Tracks above)

Tuesday afternoon

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...

72. Multi-objective optimization for the CMS High Granularity Calorimeter Level 1 trigger

Alexandre Hakimi (LLR, école polytechnique/CNRS)

27/09/2022 16:50

4 Fast ML : Application of Machine Learning to DAQ/Trigger/Real Time Analysis

Tuesday afternoon

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...

98. European Open Science Cloud : open science, open data

Volker Beckmann (MESR / DGRI / SSRI / A7)

27/09/2022 17:10

Tuesday afternoon

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...

83. Deep learning for slit-less Spectroscopic Redshift survey Simulator (DISPERS)

Julien Zoubian (Aix Marseille Univ, CNRS/IN2P3, CPPM, Marseille, France)

28/09/2022 09:00

3 ML for simulation and surrogate model : Application of Machine Learning to simulation or other cases where it is deemed to replace an existing complex model

Wed morning

Since the discovery of the acceleration of the expansion of the Universe, the concordence model describes the Universe as spatially flat curvature with about 5 % of baryonic matter, 26 % of cold dark matter and 68 % of Dark Energy. However, understanding the nature of both, the dark matter and dark energy components remains unknown this puzzle is one of the greatest challenges in contemporary...

66. Deep Learning Applied to Cherenkov Telescope Array Images

Michaël Dell'aiera (LAPP, CNRS)

28/09/2022 09:20

2 ML for analysis : event classification, statistical analysis and inference, including anomaly detectio

Wed morning

Gamma-ray astronomy is a field of physics that studies astrophysics sources of high-energy photons. These uncharged particles travel in straight lines and are not deviated by magnetic fields along their journey to Earth, making possible the track of their origins. Once they interact with the atmosphere, they produce particle showers, emitting lights through the so-called Cherenkov process,...

71. Data/MC adaptation with adversarial training in KM3NeT

Joao Coelho (APC / CNRS)

28/09/2022 09:40

2 ML for analysis : event classification, statistical analysis and inference, including anomaly detectio

Wed morning

Data and MC represent different domains. Supervised learning in MC needs to be transferred to the real data domain and MC mismodelling can reduce the performance of the transferred models. In this work we are implementing the Domain Adversarial Neural Network (DANN) concept to the standard Graph Neural Network (GNN) classification and regression tasks in the KM3NeT/ORCA experiment. We will...

81. The ELAsTiCC data challenge: preparing the Fink broker for LSST

Emille Ishida (CNRS/LPC-Clermont)

28/09/2022 10:00

2 ML for analysis : event classification, statistical analysis and inference, including anomaly detectio

Wed morning

Fink is a community alert broker specifically designed to operate under the extreme data volume and complexity of the upcoming Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). It is a French-led international collaboration whose task is to select, add value and redistribute transient alerts to the astronomical community during the 10 years of LSST. The system is completely...

104. Neural Posterior Estimation with Differentiable Simulators

Justine Zeghal (APC)

28/09/2022 10:50

2 ML for analysis : event classification, statistical analysis and inference, including anomaly detectio

Wed morning

Simulation-Based Inference (SBI) is a promising Bayesian inference framework that alleviates the need for analytic likelihoods to estimate posterior distributions. Recent advances using neural density estimators in SBI algorithms have demonstrated the ability to achieve high-fidelity posteriors, at the expense of a large number of simulations ; which makes their application potentially very...

79. Auto-tuning of the material mapping with the ACTS track reconstruction suite

Corentin Allaire (IJCLab)

28/09/2022 11:10

9 Other

Wed morning

The reconstruction of particle trajectories is a key challenge of particle physics experiments as it directly impacts particle reconstruction and physics performances. To reconstruct these trajectories, different reconstruction algorithms are used sequentially. Each of these algorithms use many configuration parameters that need to be fine-tuned to properly account for the...

107. A reminder of some events : JI 2022 & DL Training

Françoise BOUVET (IJCLab)

28/09/2022 11:30

Wed morning

103. Round table on emerging topic

28/09/2022 11:35

Wed morning

65. B decays reconstruction at Belle II using graph neural networks

Jacopo Cerasoli (CNRS - IPHC)

28/09/2022 14:00

2 ML for analysis : event classification, statistical analysis and inference, including anomaly detectio

Wed afternoon

The Belle II experiment has unique features that allow to study B meson decays with neutrinos in the final state. It is possible to deduce the presence of such particles from the energy-momentum imbalance obtained after reconstructing the companion B meson produced in the event. This task is complicated by the thousands of possible final states B mesons can decay into, and is currently...

89. A new method for the data driven estimation of background using GAN

Victor Lohezic (IRFU (CEA) / Université Paris-Saclay)

28/09/2022 14:20

Wed afternoon

In this contribution we present a novel data driven method for the estimation of background by generating a new misidentified object using generative adversarial networks (GAN). In High Energy Physics, characterizing signal hypothesis requires distinguishing its signature from a large number of background processes with similar final states. Machine learning (ML) classification algorithms are...

84. Gamma-ray spectrometry of fission fragments: ML analysis of multi-dimensional spectra

Mattéo Ballu

28/09/2022 14:40

2 ML for analysis : event classification, statistical analysis and inference, including anomaly detectio

Wed afternoon

The analysis of gamma radiation emitted by fission fragments has become an essential tool for studying the nuclear fission process. It allows to probe the intrinsic properties of the fragments or to explore effects little studied experimentally such as the sharing of excitation energy between fragments at nuclear fission. It also provides nuclear data directly useful for reactor...

101. AISSAI IN2P3 Machine Learning semester brain storm

28/09/2022 15:00

Wed afternoon

102. Wrap-up and Farewell

Alexandre Boucaud (APC / IN2P3), David Rousseau (IJCLab, CNRS/IN2P3, Université Paris-Saclay), Valérie Gautard (CEA-Irfu)

28/09/2022 15:30

Wed afternoon

97. CC TBC

Monday afternoon

62. GNN for hadronic flow calibration in ATLAS

Pierre-Antoine Delsart (LPSC)

1 ML for object identification and reconstruction

Wed afternoon

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...