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Valérie Gautard (CEA-Irfu), David Rousseau (IJCLab, CNRS/IN2P3, Université Paris-Saclay)16/03/2021 09:00
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51. Towards a realistic track reconstruction algorithm based on graph neural networks for the HL-LHCSylvain Caillou (L2I Toulouse, CNRS/IN2P3)16/03/2021 09:15
The physics reach of the HL-LHC will be limited by how efficiently the experiments can use the available computing resources, i.e. affordable software and computing are essential. The development of novel methods for charged particle reconstruction at the HL-LHC incorporating machine learning techniques or based entirely on machine learning is a vibrant area of research. In the past two years,...
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Pierre-Antoine Delsart (LPSC)16/03/2021 09:30
Because of the nature of QCD interactions with matter, the measured energies and masses of hadronic jets have to be calibrated before they are used in physics analysis. The correction depends on many characteristics of the jets, including the energy and mass themselves. Obtaining the correction is thus a multidimensionnal regression problem for wich DNN is a well suited approach.
In...
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Lucas TORTEROTOT ({UNIV CLAUDE BERNARD}UMR5822)16/03/2021 09:45
Reconstruction of di-$\tau$ mass in a faster and more accurate way than the existing methods is crucial to test any theory involving Higgs boson and Z boson which are decaying to $\tau^+ \tau^-$. However, it is an arduous task due to existence of neutrinos as decay product of each $\tau$ lepton which are invisible to detectors at LHC.
The present ongoing work aims at obtaining a di-$\tau$...
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Taylor Faucett (Université Clermont Auvergne)16/03/2021 10:30
Machine Learning methods are extremely powerful but often function as black-box problem solvers, providing improved performance at the expense of clarity. Our work describes a new machine learning approach which translates the strategy of a deep neural network into simple functions that are meaningful and intelligible to the physicist, without sacrificing performance improvements. We apply...
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Arthur Thaller (IPHC)16/03/2021 10:45
The decays of a B-meson with neutrinos or other undetected particles in the final state cannot be fully reconstructed without the information coming from the rest of the event. The Belle II experiment benefits from the clean environment of electron-positron collisions where B mesons are produced in pairs without other particles in the event. A complete reconstruction of ...
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Samuel Calvet (LPC)16/03/2021 11:00
Many of the searches for new physics consist in a bump hunt on invariant mass spectrum. In the cases for which the turn-on region may contain signal the usual fit methods do not apply.
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This talk presents the first ingredients towards a fitting method, based on DNN, that would allow to fit the entire spectrum, from the turn-on to the tail. -
Louis Vaslin (LPC Clermont), Ioan Dinu (INFIN-HH / LPC)16/03/2021 11:15
Among all of the applications of Machine Learning in HEP, anomaly detection
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methods have been receiving a growing interest over the last years. Their use
is especially promising in the development of model independent search tech-
niques. Following this trend line, we propose new algorithms based on the arti-
ficial neural network concept of the Auto-Encoder, augmented with... -
45. Artificial Intelligences for measuring energy deposits in the ATLAS LAr calorimeter in real timeLauri Laatu16/03/2021 12:00
Within the Phase-II upgrade of the LHC, the readout electronics of the ATLAS Liquid Argon (LAr) Calorimeters is prepared for high luminosity operation expecting a pile-up of up to 200 simultaneous pp interactions.
The Liquid Argon (LAr) calorimeters measure the energy of particles produced by LHC collisions, especially electrons and photons. The digitized signals from the LAr 182468...
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Etienne FORTIN (Aix Marseille Univ, CNRS/IN2P3, CPPM, Marseille, France)16/03/2021 12:15
Within the Phase-II upgrade of the LHC, the readout electronics of the ATLAS Liquid Argon (LAr) Calorimeters is prepared for high luminosity operation expecting a pile-up of up to 200 simultaneous pp interactions. The Liquid Argon calorimeters measure the energy of particles produced by LHC collisions, especially electrons and photons. The digitized signals from the LAr 182468 channels are...
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Anais Moller (CNRS / LPC Clermont)16/03/2021 12:30
Next generation experiments such as the Vera Rubin Observatory Legacy Survey of Space and Time (LSST) will provide an unprecedented volume of time-domain data opening a new era of big data in astronomy. To fully harness the power of these surveys, we require analysis methods capable of dealing with large data volumes that can identify promising transients within minutes for follow-up...
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Bertrand Rigaud (CC-IN2P3)16/03/2021 12:45
On se propose de faire un point sur les GPUs disponibles au CC-IN2P3 et leur utilisation actuelle. On présentera également quelques évolutions à venir.
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Yann Coadou (CPPM, Aix-Marseille Université, CNRS/IN2P3)17/03/2021 09:00
The 2021 edition of the School of Statistics SOS2021 was held online for the first time (postponed from May 2020 in Carry-le-Rouet) from 18 to 29 January 2021. The school targets PHD students, post-docs and senior scientists wishing to strengthen their knowledge or discover new methods in statistical analysis applied in particle and astroparticle physics and cosmology.
The programme covers...
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Michel MUR (CEA Irfu)17/03/2021 09:15
The Saclay site is one of the 4 production sites for the New Small Wheels, a new Micromegas detector system intended to be installed end ’21 in the Atlas experiment at CERN. The detector modules are made of a sandwich assembly of 5 composite panels. These panels are built on instrumented granite tables in a dedicated clean room, and are scanned in place for planarity with a mobile gantry...
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Bastien Arcelin (APC), Alexandre Boucaud (APC / IN2P3)17/03/2021 09:35
I will present a first investigation of the suitability and performance of IPUs in deep learning applications in cosmology.
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As upcoming photometric galaxy surveys will produce an unprecedented amount of observational data, more and more people turn to deep learning for fast and accurate data processing. In this work I tested typical examples of tasks that will be required to process and... -
Emmanuel Le Guirriec (CPPM)17/03/2021 09:50
The neutrino telescopes KM3NeT search for cosmic neutrinos from distant
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astrophysical sources such as supernovae, gamma ray bursters or
colliding stars flaring blazars. Once the events are received, they are
rapidly reconstructed online. The online events must be classified to
identify signal neutrinos from atmospheric muon background events.
Dedicated applications will then analyse... -
Dr Geoffrey Daniel (CEA/DES/ISAS/DM2S/STMF/LGLS)17/03/2021 10:30
The localization of radioactive sources provides mandatory information for the monitoring and the diagnostic of radiological scenes and it still constitutes a critical challenge. Gamma-ray imaging is performed through coded mask aperture imaging when the energy of the photons is sufficiently low to insure photoelectric interactions into the mask. Then, classically, a deconvolution algorithm is...
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Mehdi Ben Ghali (IRFU - CEA)17/03/2021 10:45
Currently, dynamic aperture calculations of high-energy hadron colliders are
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generated through computer simulation, which is both a resource-heavy and
time-costly process.
The aim of this research is to use a reservoir computing machine learning
model in order to achieve a faster extrapolation of dynamic aperture values. In
order to achieve these results, a recurrent echo-state network... -
Dr Emille Ishida (LPC-UCA)17/03/2021 11:00
The next generation of astronomical surveys will completely change the discovery process in astronomy. Faced with millions of possible new sources per night, serendipitous discoveries will not occur. At the same time, given the significant improvement in detection efficiency it is also reasonable to expect that unforeseen astrophysical sources will be detected. However, if we do not have tools...
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Qiufan Lin (CPPM)17/03/2021 11:15
Deep Learning neural networks are powerful tools to extract information from input data, and have been increasingly applied in astrophysical studies. However, without proper treatment, data-driven algorithms such as neural networks usually cannot fully capture salient information concerned for certain tasks and thus result in a biased output harmful for subsequent analyses. It is therefore...
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Dr Thomas Vuillaume (LAPP, CNRS)17/03/2021 12:00
The Cherenkov Telecope Array (CTA) is the future of ground-based gamma astronomy and will be composed of tens of telescopes divided in two arrays in both hemispheres.
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GammaLearn is a project started in 2017 to develop innovative analysis for CTA event reconstruction based on deep learning.
Here we present a status report of the project, the network architecture developed for event... -
Benjamin Remy (CEA Paris-Saclay)17/03/2021 12:15
We present a novel methodology to address ill-posed inverse problems, by providing a description of the posterior distribution instead of a point estimate solution. Our approach combines Neural Score Matching for learning a prior distribution from physical simulations, and an Annealed Hamiltonian Monte-Carlo technique to sample the full high-dimensional posterior of our problem.
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In the... -
46. Automatically Differentiable Physics for Maximizing the Information Gain of Cosmological SurveysDenise Lanzieri17/03/2021 12:30
Weak gravitational lensing is one of the most promising tools of cosmology to constrain models and probe the evolution of dark-matter structures. Yet, the current analysis techniques are only able to exploit the 2-pt statistics of the lensing signal, ignoring a large fraction of the cosmological information contained in the non-Gaussian part of the signal. Exactly how much information is lost,...
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Fadi Nammour (CosmoStat, CEA Paris-Saclay)17/03/2021 12:45
Telescope images are corrupted with blur and noise. Generally, blur is represented by a convolution with a Point Spread Function and noise is modelled as Additive Gaussian Noise. Restoring galaxy images from the observations is an inverse problem that is ill-posed and specifically ill-conditioned. The majority of the standard reconstruction methods minimise the Mean Square Error to...
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