Publications

Order by: publication date number of citations

• ATLAS results on exotic hadronic resonances

  Yue Xu

  SciPost Phys. Proc. 15, 013 (2024) · published 2 April 2024 | Toggle abstract · pdf

  Recent results from the proton-proton collision data taken by the ATLAS experiment on exotic resonances are presented. A search for $J/\psi p$ resonances in $\Lambda_{b}→ J/\psi p K$ decays with large $p K$ invariant masses is reported. A search for exotic resonances in the four-muon final state is shown.

• Recent ATLAS results on forward physics and diffraction

  Rafał Staszewski

  SciPost Phys. Proc. 15, 011 (2024) · published 2 April 2024 | Toggle abstract · pdf

  The ATLAS detector at the LHC is equipped with dedicated systems designed for the detection of forward protons produced in diffractive and photon-induced processes. These detectors significantly extend the ATLAS physics reach. Recent measurements performed using these forward proton detectors: elastic proton-proton scattering, exclusive charged pion-pair production, and two-photon production of lepton pairs are presented.

• Recent LHCb results on forward physics and diffraction

  Luke McConnell

  SciPost Phys. Proc. 15, 009 (2024) · published 2 April 2024 | Toggle abstract · pdf

  The LHCb detector at the LHC offers unique coverage of forward rapidities for studies of Central Exclusive Production (CEP) and soft QCD. CEP measurements allow the investigation of the nature of pomerons, and provide constraints on low-x gluon phenomenology, probing potential saturation effects. Moreover LHCb can test phenomenological models of soft QCD processes, by measuring the production of forward hadrons in pp collisions. In this talk the latest results from the LHCb experiment will be presented.

• Forward proton measurements with ATLAS

  Mustafa Schmidt

  SciPost Phys. Proc. 15, 008 (2024) · published 2 April 2024 | Toggle abstract · pdf

  The ALFA subdetector is designed to measure elastic proton-proton scattering from which the total cross section and $\rho$-parameter are determined. In 2016, special runs at a center-of-mass energy of $\sqrt{s}$ = 13 TeV and with $\beta^\star$ = 2.5 km were recorded.

• Towards better discrimination and improved modelling of dark-sector showers

  Andy Buckley, Deepak Kar, Sukanya Sinha

  SciPost Phys. Proc. 15, 006 (2024) · published 2 April 2024 | Toggle abstract · pdf

  As no evidence for classic WIMP-based signatures of dark matter have been found at the LHC, several phenomenological studies have raised the possibility of accessing a strongly-interacting dark sector through new collider-event topologies. If dark mesons exist, their evolution and hadronization procedure are currently little constrained. They could decay promptly and result in QCD-like jet structures, even though the original decaying particles are dark sector ones; they could behave as semi-visible jets; or they could behave as completely detector-stable hadrons, in which case the final state is just the missing transverse momentum. In this contribution we will introduce a study performed to explore use of jet substructure methods to distinguish dark-sector from QCD jets in the first two scenarios, using observables in a IRC-safe linear basis, and discuss ways forward for this approach to dark-matter at the LHC.

• Photon-photon fusion and tau g-2 measurement in ATLAS

  Agnieszka Ogrodnik

  SciPost Phys. Proc. 15, 003 (2024) · published 2 April 2024 | Toggle abstract · pdf

  Relativistic heavy-ion beams at the LHC are accompanied by a large flux of equivalent photons. New measurements of exclusive dilepton production (electron, muon, and tau pairs) performed by the ATLAS experiment are discussed. We present the photon-induced production of tau pairs and constraints on the tau lepton's anomalous magnetic dipole moment. In addition, measurements of photon-induced electron and muon pair production are presented, which provide strong constraints on the nuclear photon flux and its dependence on the impact parameter and photon energy. Forward neutrons are utilised to provide an experimental handle on the impact parameter range sampled in the events.

• High-dimensional and permutation invariant anomaly detection

  Vinicius Mikuni, Benjamin Nachman

  SciPost Phys. 16, 062 (2024) · published 29 February 2024 | Toggle abstract · pdf

  Methods for anomaly detection of new physics processes are often limited to low-dimensional spaces due to the difficulty of learning high-dimensional probability densities. Particularly at the constituent level, incorporating desirable properties such as permutation invariance and variable-length inputs becomes difficult within popular density estimation methods. In this work, we introduce a permutation-invariant density estimator for particle physics data based on diffusion models, specifically designed to handle variable-length inputs. We demonstrate the efficacy of our methodology by utilizing the learned density as a permutation-invariant anomaly detection score, effectively identifying jets with low likelihood under the background-only hypothesis. To validate our density estimation method, we investigate the ratio of learned densities and compare to those obtained by a supervised classification algorithm.

  2 citations

• Codebase release 0.5 for mapyde

  Giordon Stark, Camila Aristimuno Ots, Mike Hance

  SciPost Phys. Codebases 27-r0.5 (2024) · published 28 February 2024 | Toggle abstract · src

  Searches for new physics at the Large Hadron Collider have constrained many models of physics beyond the Standard Model. Many searches also provide resources that allow them to be reinterpreted in the context of other models. We describe a reinterpretation pipeline that examines previously untested models of new physics using supplementary information from ATLAS Supersymmetry (SUSY) searches in a way that provides accurate constraints even for models that differ meaningfully from the benchmark models of the original analysis. The public analysis information, such as public analysis routines and serialized probability models, is combined with common event generation and simulation toolkits MADGRAPH, PYTHIA8, and DELPHES into workflows steered by TOML configuration files, and bundled into the mapyde Python package. The use of mapyde is demonstrated by constraining previously untested SUSY models with compressed sleptons and electroweakinos using ATLAS results.

  1 citation

• Reduce, reuse, reinterpret: An end-to-end pipeline for recycling particle physics results

  Giordon Stark, Camila Aristimuno Ots, Mike Hance

  SciPost Phys. Codebases 27 (2024) · published 28 February 2024 | Toggle abstract · pdf

  Searches for new physics at the Large Hadron Collider have constrained many models of physics beyond the Standard Model. Many searches also provide resources that allow them to be reinterpreted in the context of other models. We describe a reinterpretation pipeline that examines previously untested models of new physics using supplementary information from ATLAS Supersymmetry (SUSY) searches in a way that provides accurate constraints even for models that differ meaningfully from the benchmark models of the original analysis. The public analysis information, such as public analysis routines and serialized probability models, is combined with common event generation and simulation toolkits MADGRAPH, PYTHIA8, and DELPHES into workflows steered by TOML configuration files, and bundled into the mapyde Python package. The use of mapyde is demonstrated by constraining previously untested SUSY models with compressed sleptons and electroweakinos using ATLAS results.

  1 citation

• An unfolding method based on conditional invertible neural networks (cINN) using iterative training

  Mathias Backes, Anja Butter, Monica Dunford, Bogdan Malaescu

  SciPost Phys. Core 7, 007 (2024) · published 21 February 2024 | Toggle abstract · pdf

  The unfolding of detector effects is crucial for the comparison of data to theory predictions. While traditional methods are limited to representing the data in a low number of dimensions, machine learning has enabled new unfolding techniques while retaining the full dimensionality. Generative networks like invertible neural networks~(INN) enable a probabilistic unfolding, which map individual data events to their corresponding unfolded probability distribution. The accuracy of such methods is however limited by how well simulated training samples model the actual data that is unfolded. We introduce the iterative conditional INN (IcINN) for unfolding that adjusts for deviations between simulated training samples and data. The IcINN unfolding is first validated on toy data and then applied to pseudo-data for the $pp \to Z \gamma \gamma$ process.