Publications

Order by: publication date number of citations

• 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.

  12 citations

• 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.

  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

• 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.

  15 citations

• PC-JeDi: Diffusion for particle cloud generation in high energy physics

  Matthew Leigh, Debajyoti Sengupta, Guillaume Quétant, John Andrew Raine, Knut Zoch, Tobias Golling

  SciPost Phys. 16, 018 (2024) · published 19 January 2024 | Toggle abstract · pdf

  In this paper, we present a new method to efficiently generate jets in High Energy Physics called PC-JeDi. This method utilises score-based diffusion models in conjunction with transformers which are well suited to the task of generating jets as particle clouds due to their permutation equivariance. PC-JeDi achieves competitive performance with current state-of-the-art methods across several metrics that evaluate the quality of the generated jets. Although slower than other models, due to the large number of forward passes required by diffusion models, it is still substantially faster than traditional detailed simulation. Furthermore, PC-JeDi uses conditional generation to produce jets with a desired mass and transverse momentum for two different particles, top quarks and gluons.

  26 citations

• Snowmass2021 cosmic frontier white paper: Ultraheavy particle dark matter

  Daniel Carney, Nirmal Raj, Yang Bai, Joshua Berger, Carlos Blanco, Joseph Bramante, Christopher Cappiello, Maíra Dutra, Reza Ebadi, Kristi Engel, Edward Kolb, J. Patrick Harding, Jason Kumar, Gordan Krnjaic, Rafael F. Lang, Rebecca K. Leane, Benjamin V. Lehmann, Shengchao Li, Andrew J. Long, Gopolang Mohlabeng, Ibles Olcina, Elisa Pueschel, Nicholas L. Rodd, Carsten Rott, Dipan Sengupta, Bibhushan Shakya, Ronald L. Walsworth, Shawn Westerdale

  SciPost Phys. Core 6, 075 (2023) · published 6 November 2023 | Toggle abstract · pdf

  We outline the unique opportunities and challenges in the search for "ultraheavy" dark matter candidates with masses between roughly 10 TeV and the Planck scale $m_{\rm pl} ≈ 10^{16}$ TeV. This mass range presents a wide and relatively unexplored dark matter parameter space, with a rich space of possible models and cosmic histories. We emphasize that both current detectors and new, targeted search techniques, via both direct and indirect detection, are poised to contribute to searches for ultraheavy particle dark matter in the coming decade. We highlight the need for new developments in this space, including new analyses of current and imminent direct and indirect experiments targeting ultraheavy dark matter and development of new, ultra-sensitive detector technologies like next-generation liquid noble detectors, neutrino experiments, and specialized quantum sensing techniques.

  20 citations

• EPiC-GAN: Equivariant point cloud generation for particle jets

  Erik Buhmann, Gregor Kasieczka, Jesse Thaler

  SciPost Phys. 15, 130 (2023) · published 3 October 2023 | Toggle abstract · pdf

  With the vast data-collecting capabilities of current and future high-energy collider experiments, there is an increasing demand for computationally efficient simulations. Generative machine learning models enable fast event generation, yet so far these approaches are largely constrained to fixed data structures and rigid detector geometries. In this paper, we introduce EPiC-GAN - equivariant point cloud generative adversarial network - which can produce point clouds of variable multiplicity. This flexible framework is based on deep sets and is well suited for simulating sprays of particles called jets. The generator and discriminator utilize multiple EPiC layers with an interpretable global latent vector. Crucially, the EPiC layers do not rely on pairwise information sharing between particles, which leads to a significant speed-up over graph- and transformer-based approaches with more complex relation diagrams. We demonstrate that EPiC-GAN scales well to large particle multiplicities and achieves high generation fidelity on benchmark jet generation tasks.

  32 citations

• Update on the angular resolution of GRAPES-3 experiment based on Moon shadow analysis

  D. Pattanaik, S. Ahmad, M. Chakraborty, S. R. Dugad, U. D. Goswami, S. K. Gupta, B. Hariharan, Y. Hayashi, P. Jagadeesan, A. Jain, P. Jain, S. Kawakami, H. Kojima, S. Mahapatra, P. K. Mohanty, R. Moharana, Y. Muraki, P. K. Nayak, T. Nonaka, A. Oshima, B. P. Pant, M. Rameez, K. Ramesh, L. V. Reddy, S. Shibata, F. Varsi, M. Zuberi

  SciPost Phys. Proc. 13, 033 (2023) · published 29 September 2023 | Toggle abstract · pdf

  GRAPES-3 is an extensive air shower array located at Ooty (11.6$^\circ$N, 76.7$^\circ$E, 2200 m a.s.l.) in southern India. Recently, the angular resolution of the GRAPES-3 array has been improved by correcting the shower front curvature based on the shower size and age. Here, we present the results of the angular resolution and pointing accuracy of the array through observation of the Moon shadow. We have analyzed the data for the period of 2014-2016 containing $\sim$ 3$×$10$^9$ air shower events with a median energy of 15 TeV. A significant improvement in the angular resolution has been observed compared to the earlier analysis by the group through the Moon shadow method and is comparable to the arrays located at a 2 km higher altitude than the GRAPES-3 experiment.