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...
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...
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...
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...
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....
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...
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...
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...
If you want your favourite publication, tutorial, or project to be on https://machine-learning.pages.in2p3.fr/ just stay around
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...
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...
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...
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...
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...
https://www.cnrs.fr/fr/le-centre-artificial-intelligence-science-science-artificial-intelligence-aissai
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...
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...
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,...
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...
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...
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...
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...
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...
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...
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,...
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...
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...
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...
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...
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...
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...
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...
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...
