arXiv subjects

Luc Le Pottier

Publications and source records attributed to Luc Le Pottier.

7 recordsLinked to original sources

arXiv2026

Energy-resolved measurement of individual GeV muon tracks generated by electrons from a compact Laser-Plasma Accelerator

Recently, the possibility of LPA-produced muon beams has gained significant interest within the accelerator application community. Directional, multi-GeV muons can be produced via Bethe-Heitler interactions when multi-GeV electrons hit solid targets. They are highly penetrating and, thanks to the compactness of the LPA, offer a path toward a deployable, active muon source. At the BELLA Center of the Lawrence Berkeley National Laboratory, we previously unambiguously detected muons generated during the interaction of multi-GeV electron beams with a 4 meter-thick electron beam dump. A new campaign has now extended our diagnostic capabilities to single-muon trajectory reconstruction and energy measurements. The setup allowed us to individually reconstruct each muon trajectory, defined by us as a muon passing through three detectors used for the reconstruction. For a subset of events, we extracted the muon energy from the magnetic-field bending angle, demonstrating production of GeV-scale muons. This work provides a key demonstration of track-based active-source muography, which enables non-invasive 3D density mapping of concealed or inaccessible samples, and it will accelerate the development of active LPA-based muon sources where compactness, controlled directionality, low divergence, and deep penetration are required.

physics.acc-ph↗

arXiv2025

Testing the limits of ITkPixV2: the ATLAS inner tracker pixel detector readout chip

The ITkPixV2 chip is the final production readout chip for the ATLAS Phase 2 Inner Tracker (ITk) upgrade at the upcoming High-Luminosity LHC (HL-LHC). Due to the extraordinarily high peak luminosity at the HL-LHC of $5 \times 10^{34}$ cm$^{-1}$s$^{-1}$, ITkPixV2 must meet significant increases in nearly all design requirements compared to the current ATLAS Inner Detector (ID), including a 10x increase in trigger rate, a 7.5x increase in hit rate, a 3x increase in radiation tolerance, and a 12.5x decrease in pixel current draw per unit area, all while maintaining a similar power per unit area as present pixel detectors. Here we present the first measurements of the ITkPixV2 chip operated at the limits of the full chip design requirements, including in particular a measurement of the activity-induced current of the chip as a function of increasing hit rate.

physics.ins-det↗

arXiv2025

Transforming Simulation to Data Without Pairing

We explore a generative machine learning-based approach for estimating multi-dimensional probability density functions (PDFs) in a target sample using a statistically independent but related control sample - a common challenge in particle physics data analysis. The generative model must accurately reproduce individual observable distributions while preserving the correlations between them, based on the input multidimensional distribution from the control sample. Here we present a conditional normalizing flow model (CNF) based on a chain of bijectors which learns to transform unpaired simulation events to data events. We assess the performance of the CNF model in the context of LHC Higgs to diphoton analysis, where we use the CNF model to convert a Monte Carlo diphoton sample to one that models data. We show that the CNF model can accurately model complex data distributions and correlations. We also leverage the recently popularized Modified Differential Multiplier Method (MDMM) to improve the convergence of our model and assign physical meaning to usually arbitrary loss-function parameters.

physics.data-an↗

arXiv2024

Measurement of directional muon beams generated at the Berkeley Lab Laser Accelerator

We present the detection of directional muon beams produced using a PW laser at the Lawrence Berkeley National Laboratory. The muon source is a multi-GeV electron beam generated in a 30 cm laser plasma accelerator interacting with a high-Z converter target. The GeV photons resulting from the interaction are converted into a high-flux, directional muon beam via pair production. By employing scintillators to capture delayed events, we were able to identify the produced muons and characterize the source. Using theoretical knowledge of the muon production process combined with simulations that are in excellent agreement with the experiments, we demonstrate that the multi-GeV electron beams produce GeV-scale muons in numbers far exceeding those from cosmic background. Laser-plasma-accelerator-based muon sources can therefore enhance muon imaging applications thanks to their compactness, directionality, and high yields, which reduce the exposure time by orders of magnitude compared to cosmic ray muons. Using the Geant4-based simulation code we developed to gain insight into the experimental results, we can design future experiments and applications based on LPA-generated muons.

physics.acc-ph↗

arXiv2022

Autoencoders for Semivisible Jet Detection

The production of dark matter particles from confining dark sectors may lead to many novel experimental signatures. Depending on the details of the theory, dark quark production in proton-proton collisions could result in semivisible jets of particles: collimated sprays of dark hadrons of which only some are detectable by particle collider experiments. The experimental signature is characterised by the presence of reconstructed missing momentum collinear with the visible components of the jets. This complex topology is sensitive to detector inefficiencies and mis-reconstruction that generate artificial missing momentum. With this work, we propose a signal-agnostic strategy to reject ordinary jets and identify semivisible jets via anomaly detection techniques. A deep neural autoencoder network with jet substructure variables as input proves highly useful for analyzing anomalous jets. The study focuses on the semivisible jet signature; however, the technique can apply to any new physics model that predicts signatures with anomalous jets from non-SM particles.

hep-ph↗

arXiv2021

The LHC Olympics 2020: A Community Challenge for Anomaly Detection in High Energy Physics

A new paradigm for data-driven, model-agnostic new physics searches at colliders is emerging, and aims to leverage recent breakthroughs in anomaly detection and machine learning. In order to develop and benchmark new anomaly detection methods within this framework, it is essential to have standard datasets. To this end, we have created the LHC Olympics 2020, a community challenge accompanied by a set of simulated collider events. Participants in these Olympics have developed their methods using an R&D dataset and then tested them on black boxes: datasets with an unknown anomaly (or not). This paper will review the LHC Olympics 2020 challenge, including an overview of the competition, a description of methods deployed in the competition, lessons learned from the experience, and implications for data analyses with future datasets as well as future colliders.

hep-ph↗

arXiv2020

Simulation-Assisted Decorrelation for Resonant Anomaly Detection

A growing number of weak- and unsupervised machine learning approaches to anomaly detection are being proposed to significantly extend the search program at the Large Hadron Collider and elsewhere. One of the prototypical examples for these methods is the search for resonant new physics, where a bump hunt can be performed in an invariant mass spectrum. A significant challenge to methods that rely entirely on data is that they are susceptible to sculpting artificial bumps from the dependence of the machine learning classifier on the invariant mass. We explore two solutions to this challenge by minimally incorporating simulation into the learning. In particular, we study the robustness of Simulation Assisted Likelihood-free Anomaly Detection (SALAD) to correlations between the classifier and the invariant mass. Next, we propose a new approach that only uses the simulation for decorrelation but the Classification without Labels (CWoLa) approach for achieving signal sensitivity. Both methods are compared using a full background fit analysis on simulated data from the LHC Olympics and are robust to correlations in the data.

hep-ph↗