arXiv subjects

Louis Moureaux

Publications and source records attributed to Louis Moureaux.

11 recordsLinked to original sources

arXiv2026

Anomaly detection for multijet scenarios

Signals of physics beyond the Standard Model continue to resist discovery at the LHC. Recent years have seen the proliferation of new anomaly detection techniques, promising discovery with significantly fewer model assumptions than traditional approaches. Strategies based on weak supervision have been especially successful, but so far were largely limited in scope by their reliance on a resonance manifesting a decay into a pair of jets. In this work, we demonstrate that a well-established idea from jet substructure physics - recursive soft drop - in combination with the CATHODE technique for anomaly detection can be used to simultaneously perform anomaly detection for signals with an arbitrary number of jets in the final state, greatly increasing the scope of such searches.

hep-ph↗

arXiv2026

Kitchen Sink Anomaly Detection

An enormous amount of R&D effort has resulted in many new resonant anomaly detection methods being proposed in recent years. However, the vast majority of previous R&D studies have suffered from two limitations: they have focused on a very small set of simulated signal benchmark models; and they have either used small sets of carefully crafted high-level jet substructure observables, which can be highly performant but are prone to model dependence, or the full collider event phase space, which is more agnostic but suffers from reduced sensitivity. In this work, we address both limitations: we formulate a number of new simulated signal benchmarks, which we make publicly available in a format fully compatible with the LHCO R&D benchmark; and we explore a high-level, yet highly agnostic, observable set consisting of Energy Flow Polynomials in addition to the usual subjettiness variables. We evaluate this "kitchen sink" observable set for both an idealized anomaly detector and the CWoLa hunting task, along with three baseline observable sets (the Baseline LHC Olympics set, subjettiness observables, and Energy Flow Polynomials). We find that our kitchen sink approach is the most sensitive to a broad range of signal types. Furthermore, we show that an attribute bagging variant, in which each ensemble member is trained on a random subset of substructure observables, yields comparable anomaly detection performance while significantly reducing training cost.

hep-ph↗

arXiv2026

How to pick the best anomaly detector?

Anomaly detection has the potential to discover new physics in unexplored regions of the data. However, choosing the best anomaly detector for a given data set in a model-agnostic way is an important challenge which has hitherto largely been neglected. In this paper, we introduce the data-driven ARGOS metric, which has a sound theoretical foundation and is empirically shown to robustly select the most sensitive anomaly detection model given the data. Focusing on weakly-supervised, classifier-based anomaly detection methods, we show that the ARGOS metric outperforms other model selection metrics previously used in the literature, in particular the binary cross-entropy loss. We explore several realistic applications, including hyperparameter tuning as well as architecture and feature selection, and in all cases we demonstrate that ARGOS is robust to the noisy conditions of anomaly detection.

hep-ph↗

arXiv2026

Quirk SUEP

We propose searching for physics beyond the Standard Model in the low-transverse-momentum tracks accompanying hard-scatter events at the LHC. TeV-scale resonances connected to a dark QCD sector could be enhanced by selecting events with anomalies in the track distributions. As a benchmark, a quirk model with microscopic string lengths is developed, including a setup for event simulation. For this model, strategies are presented to enhance the sensitivity compared to inclusive resonance searches: a simple cut-based selection, a supervised search, and a model-agnostic weakly supervised anomaly search with the CATHODE method. Expected discovery potentials and exclusion limits are shown for 140 fb$^{-1}$ of 13 TeV proton-proton collisions at the LHC.

hep-ph↗

arXiv2025

Intrinsic $k_T$ and soft gluons in Monte Carlo event generators

Experimental measurements of the transverse momentum of Drell-Yan lepton pairs are sensitive to non-perturbative physics associated with the intrinsic parton transverse momentum $k_T$. We discuss recent determinations of intrinsic $k_T$ in the context of transverse momentum dependent (TMD) parton branching calculations and collinear parton-shower Monte Carlo generators. We illustrate the influence of the soft-gluon resolution scale and the non-perturbative Sudakov region on the intrinsic $k_T$ extraction. We emphasize the relevance of the correct treatment of correlated uncertainties between different transverse momentum bins in TMD fits and present an application to the determination of the intrinsic $k_T$ in the forward rapidity region.

hep-ph↗

arXiv2025

Weakly supervised anomaly detection with event-level variables

We introduce a new topology for weakly supervised anomaly detection searches, di-object plus~X. In this topology, one looks for a resonance decaying to two standard model particles produced in association with other anomalous event activity (X). This additional activity is used for classification. We demonstrate how anomaly detection techniques which have been developed for di-jet searches focusing on jet substructure anomalies can be applied to event-level anomaly detection in this topology. To robustly capture event-level features of multi-particle kinematics, we employ new physically motivated variables derived from the geometric structure of a collision's phase space manifold. As a proof of concept, we explore the application of this approach to several benchmark signals in the di-$τ$ and di-$μ$ plus~X final states. We demonstrate that our anomaly detection approach can reach discovery-level significances for signals that would be missed in a conventional bump-hunt approach.

hep-ph↗

arXiv2025

Universal New Physics Latent Space

We develop a machine learning method for mapping data originating from both Standard Model processes and various theories beyond the Standard Model into a unified representation (latent) space while conserving information about the relationship between the underlying theories. We apply our method to three examples of new physics at the LHC of increasing complexity, showing that models can be clustered according to their LHC phenomenology: different models are mapped to distinct regions in latent space, while indistinguishable models are mapped to the same region. This opens interesting new avenues on several fronts, such as model discrimination, selection of representative benchmark scenarios, and identifying gaps in the coverage of model space.

hep-ph↗

arXiv2024

Accelerating Resonance Searches via Signature-Oriented Pre-training

The search for heavy resonances beyond the Standard Model (BSM) is a key objective at the LHC. While the recent use of advanced deep neural networks for boosted-jet tagging significantly enhances the sensitivity of dedicated searches, it is limited to specific final states, leaving vast potential BSM phase space underexplored. We introduce a novel experimental method, Signature-Oriented Pre-training for Heavy-resonance ObservatioN (Sophon), which leverages deep learning to cover an extensive number of boosted final states. Pre-trained on the comprehensive JetClass-II dataset, the Sophon model learns intricate jet signatures, ensuring the optimal constructions of various jet tagging discriminates and enabling high-performance transfer learning capabilities. We show that the method can not only push widespread model-specific searches to their sensitivity frontier, but also greatly improve model-agnostic approaches, accelerating LHC resonance searches in a broad sense.

hep-ph↗

arXiv2024

Results of the 2022 ECFA Early-Career Researchers Panel survey on career prospects and diversity

This document presents the outcomes of a comprehensive survey conducted among early career researchers (ECRs) in academic particle physics. Running from September 24, 2022, to March 3, 2023, the survey gathered responses from 759 ECRs employed in 39 countries. The study aimed to gain insights into the career prospects and experiences of ECRs while also delving into diversity and sociological aspects within particle physics research. The survey results are presented in a manner consistent with the survey choices. The document offers insights for the particle physics community, and provides a set of recommendations for enhancing career prospects, fostering diversity, and addressing sociological dimensions within this field.

physics.soc-ph↗

arXiv2022

The ECFA Early Career Researcher's Panel: composition, structure, and activities, 2021 -- 2022

The European Committee for Future Accelerators (ECFA) Early Career Researcher's (ECR) panel, which represents the interests of the ECR community to ECFA, officially began its activities in January 2021. In the first two years, the panel has defined its own internal structure, responded to ECFA requests for feedback, and launched its own initiatives to better understand and support the diverse interests of early career researchers. This report summarises the panel composition and structure, as well as the different activities the panel has been involved with during the first two years of its existence.

physics.acc-ph↗

arXiv2021

Results of the 2021 ECFA Early-Career Researcher Survey on Training in Instrumentation

The European Committee for Future Accelerators (ECFA) Early-Career Researchers (ECR) Panel was invited by the ECFA Detector R&D Roadmap conveners to collect feedback from the European ECR community. A working group within the ECFA ECR panel held a Townhall Meeting to get first input, and then designed and broadly circulated a detailed survey to gather feedback from the larger ECR community. A total of 473 responses to this survey were received, providing a useful overview of the experiences of ECRs in instrumentation training and related topics. This report summarises the feedback received, and is intended to serve as an input to the ECFA Detector R&D Roadmap process.

physics.ins-det↗