The MYRRHA (Multi-purpose hYbrid Research Reactor Reactor for High-tech Applications) linear accelerator has to meet very high reliability and stability requirements, i.e. during one operating cycle of 3 months a maximum of 10 beam trips longer than 3 seconds are allowed. To meet this requirements, multiple innovative solutions are planned such as redundancies and an optimized control system....
Our goal is investigation and demonstration of applicability and efficient use of Machine Learning (ML) techniques for advanced control and optimization of particle accelerators. With main effort concentrated on the application of novel algorithms to projects we already working on, we will qualify ML concepts for possible generalization and application for particle accelerators. Recently, ML...
Accurate simulations of a showers from particles from the Large Hadron Collider in the ATLAS calorimeter are incredibly resource intensive, consuming the largest fraction of CPU time on the CERN computing grid. Generative Adversarial Networks, where one Generative Network is trained to fool two Adversarial Networks are investigated as a scalable solution for modelling the response of the...
In this talk we will address several ongoing works related to ML and/or DL in CEA Fundamental Research Direction division (thought this is not an exhaustive list of ML/DL ongoing work). We will present several test cases on how ML/DL helped to accelerate time to solution or efficiently analyze data. We will then present several ongoing CEA collaborations and European/International consortium...
With the advent of digital electronics, signals from detectors can be sampled and processed by complex algorithms.
Such processing can be performed online, in FPGA and/or in computer farms.
In some cases, sampled signals are also registered on disk for offline re-processing : in nuclear physics, this is the case for the gamma-ray tracking array AGATA
and the neutron detector...
The Cherenkov Telescope Array (CTA) is the next generation of ground-based gamma-ray telescopes. Two arrays will be deployed composed of 19 telescopes in the Northern hemisphere and 99 telescopes in the Southern hemisphere. Observatory operations are planned to start in 2021 but CTA is currently in pre-production phase, prototypes already producing data, and first data from on site prototypes...
LHCb is a single-arm forward spectrometer designed to study b-physics at the LHC. As the beam luminosity will increase in the upcoming years, new challenges are expected in reconstructing high density events. The electromagnetic calorimeter in particular will be subject to much larger occupancy and the overlap of showers is expected to drastically limit reconstruction efficiency with current...
The apparent superposition of galaxies with other astrophysical objects along the line of sight, a problem known as blending, will be a major challenge for upcoming, ground-based, deep, photometric galaxy surveys, such as the Large Synoptic Survey Telescope (LSST). Blending contributes to the systematic error budget of weak lensing studies by perturbing object detection and affecting...
Recent advances in statistical learning have opened the possibility to forecast the behavior of chaotic systems using recurrent neural networks. In this letter we investigate the applicability of this framework to geophysical flows, known to be intermittent and turbulent. We show that both turbulence and intermittency introduce severe limitations on the applicability of recurrent neural...
Many applications of machine learning in physics are currently limited by their lack of uncertainty propagation and estimation through the model. Such limitation can partly be overcome by shifting to the use of probabilistic models, which provide an output distribution instead of single values.
I will show an example of such shift on image processing in astrophysics.
In this talk, we will present the two currently available GPU farms available at the CC-IN2P3 Computing Center.
In the first part we will discuss both the hardware specifications of the farms, and the software environment available.
In a second part we will show the last year usage of the farms, and discuss the resources’ requests for 2020.
Finally we will draw some conclusions and perspectives.
How does it feel to be among the handful of physicists present in a 10k+ conference of experts in ML?
We apply artificial neural networks to spectral identification of radionuclides for nuclear monitoring of unknown scenes, which requires a fast and fully automatic remote sensing analysis.
We propose a new Bayesian Convolutional Neural Network (BCNN) architecture able to perform fast identification of radionuclides whose signal is recorded in a spectrum by means of a compact CdTe based...
In phenomenological studies of BSM theories, the computation of production cross sections over large parameter spaces usually takes a large amount of time. A proposed solution is to build deep neural networks (DNN) that accurately predict the production cross sections of a given BSM model substantially reducing computational costs. In this contribution, I will present a status report on the...
The 2020 edition of the School on Statistical data analysis
will be held in South-eastern France near Marseille from May 11th to 15th, 2020. Block the date! The school targets PHD students, post-docs and senior scientists wishing to strengthen their knowledge or discover new methods in statistical analysis applied
in subatomic and astroparticle physics and cosmology.
...
Measurements of production of multiple electroweak bosons at the LHC constitute a stringent test of the electroweak sector and provide model-independent means to search for new physics at the TeV scale.
In particular, the full leptonic WZ process is among the most suitable channels for such studies. This talk reviews some promising approches investigated to tackle the challenges specific to...
The associated ttH production was observed at the LHC in 2018, mostly driven by the multilepton and gammagamma final states. The H->bb final state remains so far elusive. The latest results from ATLAS in this channel rely on boosted decision trees, used to assign jets to partons and to separate the ttH signal from the main ttbar+HF background. In this talk several new ways to tackle this...
Preliminary studies for model-independent search for new physics at the LHC will be presented in the context of anomaly detection by training semi-supervised methods (e.g. autoencoders). The use-case of a search for a dijet resonance on a QCD background will be considered using toy datasets (Delphes sample and the LHC Olympics 2020 data).
The High Luminosity Large Hadron Collider is expected to have a 10 times higher readout rate than the current state, significantly increasing the computational load required. It is then essential to explore new hardware paradigms. In this work we consider the Optical Processing Units (OPU) from [LightOn][1], which compute random matrix multiplications on large datasets in an analog, fast and...
The Generalized Parton Distributions (GPDs) describe the correlations between the transverse position and the longitudinal momentum of the partons (i.e. quarks and gluons) inside the nucleon. They can be extracted from exclusive inelastic processes, i.e. processes with a fully characterized final state. In the Hall B of the Jefferson Laboratory, the CLAS12 collaboration probes the inner...
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.
The precise measurement of the W boson mass is an important task from both experimental and theoretical point of view. One of the key observables for the measurement is called hadronic recoil. It allows to reconstruct the W pT spectrum based on the calorimeter response to the particle flow objects that compensate the W pT. Due to the calorimeter resolution effects the hadronic recoil also...
The traditional methods of training a classifier to separate signal and background events for measurement of a theory parameter break down in the context of quantum interference between signal and background processes. How can we train a Machine Learning model without the concept of labels?
A first feasibility study is performed to bringing the recently developed Likelihood-free inference...
Despite the breakthrough discoveries of multiple gravitational wave (GW) events made by the LIGO and Virgo detectors, the exploitation of the gravitational-wave data is still limited by the non-Gaussian transient noises, called “glitches” that contaminate the data and mimic the astrophysical signal. Separate the glitches from the astrophysical signal is a very challenging task, since glitches...
Vector boson scattering (VBS) is a pure electroweak process arising in high-energy collisions and playing a crucial role in the electroweak symmetry breaking. The analysis of VBS processes, with the measurement of the longitudinal polarization of the vector bosons, constitutes a promising way to investigate unitarity restoration with the Higgs mechanism, and search for possible new physics....
