Conveners
Track 2: Data Analysis - Algorithms and Tools: Data analysis with ML
- Monique Werlen
- Erica Brondolin (CERN)
- Junghwan Goh (Kyung Hee University (KR))
- Monique Werlen (EPFL - Ecole Polytechnique Federale Lausanne (CH))
Track 2: Data Analysis - Algorithms and Tools: Tracking, reconstruction, and simulation
- Junghwan Goh (Kyung Hee University (KR))
- Noemi Calace (CERN)
- Erica Brondolin (CERN)
Track 2: Data Analysis - Algorithms and Tools: Efficient reconstruction, simulation, optimization, and analysis
- Adrian Alan Pol (CERN)
- Liliana Teodorescu
- Kyungho Kim (KISTI)
- Liliana Teodorescu
Track 2: Data Analysis - Algorithms and Tools: US-zone
- Aishik Ghosh (University of California Irvine (US))
- Jennifer Ngadiuba (FNAL)
- Kyungho Kim (KISTI)
Description
There are as many different algorithms and methods as their are physicists. Obviously, we can't list them all here, but here are some broad outlines of techniques that fit into this category. Of course, new and novel categories are part of what this conference is looking for.
More information of the scientific programme: https://indico.cern.ch/event/855454/program
We show how to adapt and deploy anomaly detection algorithms based on deep autoencoders, for the unsupervised detection of new physics signatures in the extremely challenging environment of a real-time event selection system at the Large Hadron Collider (LHC). We demonstrate that new physics signatures can be enhanced by three orders of magnitude, while staying within the strict latency and...
We present a machine-learning based strategy to detect data departures from a given reference model, with no prior bias on the nature of the new physics responsible for the discrepancy. The main idea behind this method is to build the likelihood-ratio hypothesis test by directly translating the problem of maximizing a likelihood-ratio into the minimization of a loss function. A neural network...
Photometric data-driven classification of supernovae is one of the fundamental problems in astronomy. Recent studies have demonstrated the superior quality of solutions based on various machine learning models. These models learn to classify supernova types using their light curves as inputs. Preprocessing of these curves is a crucial step that significantly affects the final quality. In this...
Over the last ten years, the popularity of Machine Learning (ML) has grown exponentially in all scientific fields, included particle physics. Industry has also developed new powerful tools that, imported into academia, could revolutionise research. One recent industry development that has not yet come to the attention of the particle physics community is Collaborative Learning (CL), a...
The Self-Organizing-Map (SOM) is a widely used neural
net for data analysis, dimension reduction and
clustering. It has yet to find use in high energy
particle physics. This paper discusses two
applications of SOM in particle physics. First, we were
able to obtain high separation of rare processes in
regions of the dimensionally reduced representation.
Second, we obtained Monte Carlo...
We investigate supervised and unsupervised quantum machine learning algorithms in the context of typical data analyses at the LHC. To deal with constraints on the problem size, dictated by limitations on the quantum hardware, we concatenate the quantum algorithms to the encoder of a classic autoencoder, used for dimensional reduction. We show results for a quantum classifier and a quantum...
High energy physics experiments relies on Monte Carlo simulation to accurately model their detector response. Most of the time dominated by shower simulation in the calorimeter, the detector response modelling is time consuming and CPU intensive especially with the upcoming High Luminosity LHC upgrade. Several research directions investigated the use of Machine Learning based models to...
The increasing luminosities of future data taking at Large Hadron Collider and next generation collider experiments require an unprecedented amount of simulated events to be produced. Such large scale productions demand a significant amount of valuable computing resources. This brings a demand to use new approaches to event generation and simulation of detector responses. In this talk, we...
Generative models (GM) are powerful tools to help validate theories by reducing the computation time of Monte Carlo (MC) simulations. GMs can learn expensive MC calculations and generalize to similar situations. In this work, we propose comparing a classical generative adversarial network (GAN) approach with a Born machine, both in his discrete (QCBM) and continuous (CVBM) form while...
AtlFast3 is the next generation of high precision fast simulation in ATLAS. It is being deployed by the collaboration and will replace AtlFastII, the fast simulation tool that was successfully used until now. AtlFast3 combines two Fast Calorimeter Simulations tools; a parameterization-based approach and a machine-learning based tool exploiting Generative Adversarial Networks (GANs). AtlFast3...
SND@LHC is a newly approved detector under construction at the LHC, aimed at studying the interactions of neutrinos of all flavours produced by proton-proton collisions at the LHC. The energy range under study, few hundreds MeVs up to about 5 TeVs, is currently unexplored. In particular, electron neutrino and tau neutrino cross sections are unknown in that energy range, whereas muon neutrino...
The LUXE experiment (LASER Und XFEL Experiment) is a new experiment in planning at DESY Hamburg that will study Quantum Electrodynamics (QED) at the strong-field frontier. In this regime, QED is non-perturbative. This manifests itself in the creation of physical electron-positron pairs from the QED vacuum. LUXE intends to measure the positron production rate in this unprecedented regime by...
To sustain the harsher conditions of the high-luminosity LHC, the CMS collaboration is designing a novel endcap calorimeter system. The new calorimeter will predominantly use silicon sensors to achieve sufficient radiation tolerance and will maintain highly-granular information in the readout to help mitigate the effects of pileup. In regions characterized by lower radiation levels, small...
In the European Center of Excellence in Exascale Computing "Research on AI- and Simulation-Based Engineering at Exascale" (CoE RAISE), researchers from science and industry develop novel, scalable Artificial Intelligence technologies towards Exascale. In this work, we leverage HPC resources to perform large scale hyperparameter optimization using distributed training on multiple compute nodes,...
During the LHC Run 3 the ALICE online computing farm will process up to 50 times more Pb-Pb events per second than in Run 2. The implied computing resource scaling requires a shift in the approach that comprises the extensive usage of Graphics Processing Units (GPU) for the processing. We will give an overview of the state of the art for the data reconstruction on GPUs in ALICE, with...
RooFit is a toolkit for statistical modelling and fitting, and together with RooStats it is used for measurements and statistical tests by most experiments in particle physics, particularly the LHC experiments. As the LHC program progresses, physics analysis becomes more computationally demanding. Therefore, the focus of RooFit developments in recent years was performance optimization....
The analysis of high-frequency financial trading data faces similar problems as High Energy Physics (HEP) analysis. The data is noisy, irregular in shape, and large in size. Recent research on the intra-day behaviour of financial markets shows a lack of tools specialized for finance data, and describes this problem as a computational burden. In contrary to HEP data, finance data consists of...
We present two applications of declarative interfaces for HEP data analysis allowing users to avoid writing event loops that simplify code and enable performance improvements to be decoupled from analysis development. One example is FuncADL, an analysis description language inspired by functional programming developed using Python as a host language. In addition to providing a declarative,...
The Circular Electron Positron Collider (CEPC) [1] is one of future experiments which aims to study the properties of Higgs boson and to perform searches for new physics beyond the Standard Model. The drift chamber is a design option for the outer tracking detector. With the development of new technology in electronics, employment of primary ionization counting method [2-3] to identify charged...
The AI for Experimental Controls project at Jefferson Lab is developing an AI system to control and calibrate a large drift chamber system in near-real time. The AI system will monitor environmental variables and beam conditions to recommend new high voltage settings that maintain consistent dE/dx gain and optimal resolution throughout the experiment. At present, calibrations are performed...
Jiangmen Underground Neutrino Observatory (JUNO), located at the southern part of China, will be the world’s largest liquid scintillator(LS) detector. Equipped with 20 kton LS, 17623 20-inch PMTs and 25600 3-inch PMTs, JUNO will provide a unique apparatus to probe the mysteries of neutrinos, particularly the neutrino mass ordering puzzle. One of the challenges for JUNO is the high precision...
Particle identification is one of most fundamental tools in various particle physics experiments. For the BESIII experiment on the BEPCII, the realization of numerous physical goals heavily relies on advanced particle identification algorithms. In recent years, the emerging of quantum machine learning could potentially arm particle physics experiments with a powerful new toolbox. In this work,...
The particle-flow (PF) algorithm at CMS combines information across different detector subsystems to reconstruct a global particle-level picture of the event. At a fundamental level, tracks are extrapolated to the calorimeters and the muons system, and combined with energy deposits to reconstruct charged and neutral hadron candidates, as well as electron, photon and muon candidates.
In...
We present an application of anomaly detection techniques based on deep recurrent autoencoders to the problem of detecting gravitational wave signals in laser interferometers. Trained on noise data, this class of algorithms could detect signals using an unsupervised strategy, i.e., without targeting a specific kind of source. We develop a custom architecture to analyze the data from two...
Generative models (GM) are powerful tools to help validate theories by reducing the computation time of Monte Carlo (MC) simulations. GMs can learn expensive MC calculations and generalize to similar situations. In this work, we propose comparing a classical generative adversarial network (GAN) approach with a Born machine, both in his discrete (QCBM) and continuous (CVBM) form while...
We investigate the application of object condensation to particle tracking at the LHC. Designed having in mind calorimeter clustering and successfully employed on high-granularity calorimeter reconstruction for HL-LHC, object condensation is a generic clustering methods that could be applied to many problems within and outside HEP. Using the TrackML challenge dataset, we train a tracking...
