IN2P3/IRFU Machine Learning workshop

Session

Mardi matin

16 mars 2021, 09:00

Remote only

Présidents de session

Mardi matin

• David Rousseau (IJCLab, CNRS/IN2P3, Université Paris-Saclay)

Documents de présentation

IN2P3IRFUML_20210316_1.mp4

IN2P3IRFUML_20210316_2.mp4

IN2P3IRFUML_20210316_3.mp4

61. Introduction

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-LHC

Sylvain 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,...

42. Mass and energy calibration of hadronic jets using DNN in ATLAS

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...

36. Reconstruction of di-tau mass using deep neural networks

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-$\tau$...

57. Mapping Machine-Learned Physics into a Human-Readable Space

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...

53. Reconstruction of generic decay trees using a Graph Neural Network

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 ...

50. Fitting a spectrum using ML

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.
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.

37. Auto-Encoder based algorithms for anomaly detection

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
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 time

Lauri Laatu

16/03/2021 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...

44. RNNs on Intel FPGAs for real time signal processing in the ATLAS LAr calorimeter

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...

40. Fink broker, enabling time-domain astronomy with ML

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...

59. GPUs @CC : utilisation et évolution

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.