Publications

Order by: publication date number of citations

• Publishing statistical models: Getting the most out of particle physics experiments

  Kyle Cranmer, Sabine Kraml, Harrison B. Prosper, Philip Bechtle, Florian U. Bernlochner, Itay M. Bloch, Enzo Canonero, Marcin Chrzaszcz, Andrea Coccaro, Jan Conrad, Glen Cowan, Matthew Feickert, Nahuel Ferreiro Iachellini, Andrew Fowlie, Lukas Heinrich, Alexander Held, Thomas Kuhr, Anders Kvellestad, Maeve Madigan, Farvah Mahmoudi, Knut Dundas Morå, Mark S. Neubauer, Maurizio Pierini, Juan Rojo, Sezen Sekmen, Luca Silvestrini, Veronica Sanz, Giordon Stark, Riccardo Torre, Robert Thorne, Wolfgang Waltenberger, Nicholas Wardle, Jonas Wittbrodt

  SciPost Phys. 12, 037 (2022) · published 25 January 2022 | Toggle abstract · pdf

  The statistical models used to derive the results of experimental analyses are of incredible scientific value and are essential information for analysis preservation and reuse. In this paper, we make the scientific case for systematically publishing the full statistical models and discuss the technical developments that make this practical. By means of a variety of physics cases -- including parton distribution functions, Higgs boson measurements, effective field theory interpretations, direct searches for new physics, heavy flavor physics, direct dark matter detection, world averages, and beyond the Standard Model global fits -- we illustrate how detailed information on the statistical modelling can enhance the short- and long-term impact of experimental results.

  27 citations

• Cluster Hadronisation in Sherpa

  Gurpreet Singh Chahal, Frank Krauss

  SciPost Phys. 13, 019 (2022) · published 15 August 2022 | Toggle abstract · pdf

  We present the Sherpa cluster hadronization model and a simple model for non-perturbative colour reconnections. Using two different parton shower implementations we tuned the model to data and we show typical resulting distributions that are sensitive to hadronization effects in $e^+e^-$--annihilations into hadrons.

  27 citations

• 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.

  26 citations

• Strongly Interacting Light Dark Matter

  Sebastian Bruggisser, Francesco Riva, Alfredo Urbano

  SciPost Phys. 3, 017 (2017) · published 2 September 2017 | Toggle abstract · pdf

  In the presence of approximate global symmetries that forbid relevant interactions, strongly coupled light Dark Matter (DM) can appear weakly coupled at small energy and generate a sizable relic abundance. Fundamental principles like unitarity restrict these symmetries to a small class, where the leading interactions are captured by effective operators up to dimension-8. Chiral symmetry, spontaneously broken global symmetries and non-linearly realized supersymmetry are examples of this. Their DM candidates (composite fermions, pseudo Nambu-Goldstone Bosons and Goldstini) are interesting targets for LHC missing-energy searches.

  25 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.

  25 citations

• Constraining the SMEFT with Bayesian reweighting

  Samuel van Beek, Emanuele R. Nocera, Juan Rojo, Emma Slade

  SciPost Phys. 7, 070 (2019) · published 29 November 2019 | Toggle abstract · pdf

  We illustrate how Bayesian reweighting can be used to incorporate the constraints provided by new measurements into a global Monte Carlo analysis of the Standard Model Effective Field Theory (SMEFT). This method, extensively applied to study the impact of new data on the parton distribution functions of the proton, is here validated by means of our recent SMEFiT analysis of the top quark sector. We show how, under well-defined conditions and for the SMEFT operators directly sensitive to the new data, the reweighting procedure is equivalent to a corresponding new fit. We quantify the amount of information added to the SMEFT parameter space by means of the Shannon entropy and of the Kolmogorov-Smirnov statistic. We investigate the dependence of our results upon the choice of alternative expressions of the weights.

  24 citations

• 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.

  20 citations

• Uncertainties associated with GAN-generated datasets in high energy physics

  Konstantin T. Matchev, Alexander Roman, Prasanth Shyamsundar

  SciPost Phys. 12, 104 (2022) · published 24 March 2022 | Toggle abstract · pdf

  Recently, Generative Adversarial Networks (GANs) trained on samples of traditionally simulated collider events have been proposed as a way of generating larger simulated datasets at a reduced computational cost. In this paper we point out that data generated by a GAN cannot statistically be better than the data it was trained on, and critically examine the applicability of GANs in various situations, including a) for replacing the entire Monte Carlo pipeline or parts of it, and b) to produce datasets for usage in highly sensitive analyses or sub-optimal ones. We present our arguments using information theoretic demonstrations, a toy example, as well as in the form of a formal statement, and identify some potential valid uses of GANs in collider simulations.

  19 citations

• The rare and forbidden: Testing physics beyond the standard model with Mu3e

  Ann-Kathrin Perrevoort on behalf of the Mu3e Collaboration

  SciPost Phys. Proc. 1, 052 (2019) · published 22 February 2019 | Toggle abstract · pdf

  The upcoming Mu3e experiment aims to search for the lepton flavour violating decay $\mu^+ \to e^+ e^- e^+$ with an unprecedented final sensitivity of one signal decay in $10^{16}$ observed muon decays by making use of an innovative experimental design based on novel ultra-thin silicon pixel sensors. In a first phase, the experiment is operated at an existing muon beam line with rates of up to $10^8$ muons per second. Detailed simulation studies confirm the feasibility of background-free operation and project single event sensitivities in the order of $10^{-15}$ for signal decays modelled in an effective field theory approach. The precise tracking of the decay electrons and large geometric and momentum acceptance of Mu3e enable searches for physics beyond the Standard Model in further signatures. Examples of which are searches for lepton flavour violating two-body decays of the muon into an electron and an undetected boson as well as for electron-positron resonances in $\mu^+ \to e^+ e^- e^+ \bar{\nu}_\mu \nu_e$ which could result for instance from a dark photon decay. The Mu3e experiment is expected to be competitive in all of these channels already in phase I.

  18 citations

• Fingerprints of freeze-in dark matter in an early matter-dominated era

  Avik Banerjee, Debtosh Chowdhury

  SciPost Phys. 13, 022 (2022) · published 16 August 2022 | Toggle abstract · pdf

  We study the impact of an alternate cosmological history with an early matter-dominated epoch on the freeze-in production of dark matter. Such early matter domination is triggered by a meta-stable matter field dissipating into radiation. In general, the dissipation rate has a non-trivial temperature and scale factor dependence. Compared to the usual case of dark matter production via the freeze-in mechanism in a radiation-dominated universe, in this scenario, orders of magnitude larger coupling between the visible and the dark sector can be accommodated. Finally, as a proof of principle, we consider a specific model where the dark matter is produced by a sub-GeV dark photon having a kinetic mixing with the Standard Model photon. We point out that the parameter space of this model can be probed by the experiments in the presence of an early matter-dominated era.

  18 citations