Publications

Order by: publication date number of citations

• Neutrino experiments probe hadrophilic light dark matter

  Yohei Ema, Filippo Sala, Ryosuke Sato

  SciPost Phys. 10, 072 (2021) · published 24 March 2021 | Toggle abstract · pdf

  We use Super-K data to place new strong limits on interactions of sub-GeV Dark Matter (DM) with nuclei, that rely on the DM flux inevitably induced by cosmic-ray upscatterings. We derive analogous sensitivities at Hyper-K and DUNE and compare them with others, e.g. at JUNO. Using simplified models, we find that our proposal tests genuinely new parameter space, allowed both by theoretical consistency and by other direct detection experiments, cosmology, meson decays and our recast of monojet. Our results thus motivate and shape a new physics case for any large volume detector sensitive to nuclear recoils.

  50 citations

• Dark matter, fine-tuning and $(g-2)_{\mu}$ in the pMSSM

  Melissa van Beekveld, Wim Beenakker, Marrit Schutten, Jeremy De Wit

  SciPost Phys. 11, 049 (2021) · published 7 September 2021 | Toggle abstract · pdf

  In this paper we perform for the first time an in-depth analysis of the spectra in the phenomenological supersymmetric Standard Model that simultaneously offer an explanation for the $(g-2)_{\mu}$ discrepancy $\Delta a_{\mu}$, result in the right dark-matter relic density $\Omega_{\rm DM} h^2$ and are minimally fine-tuned. The resulting spectra may be obtained from [1]. To discuss the experimental exclusion potential for our models, we analyse the resulting LHC phenomenology as well as the sensitivity of dark-matter direct detection experiments to these spectra. We find that the latter type of experiments with sensitivity to the spin-dependent dark-matter\,--\,nucleon scattering cross section $\sigma_{\rm SD,p}$ will probe all of our found solutions.

  49 citations

• Deep-learning jets with uncertainties and more

  Sven Bollweg, Manuel Haussmann, Gregor Kasieczka, Michel Luchmann, Tilman Plehn, Jennifer Thompson

  SciPost Phys. 8, 006 (2020) · published 16 January 2020 | Toggle abstract · pdf

  Bayesian neural networks allow us to keep track of uncertainties, for example in top tagging, by learning a tagger output together with an error band. We illustrate the main features of Bayesian versions of established deep-learning taggers. We show how they capture statistical uncertainties from finite training samples, systematics related to the jet energy scale, and stability issues through pile-up. Altogether, Bayesian networks offer many new handles to understand and control deep learning at the LHC without introducing a visible prior effect and without compromising the network performance.

  49 citations

• Asymmetric dark matter: residual annihilations and self-interactions

  Iason Baldes, Marco Cirelli, Paolo Panci, Kalliopi Petraki, Filippo Sala, Marco Taoso

  SciPost Phys. 4, 041 (2018) · published 26 June 2018 | Toggle abstract · pdf

  Dark matter (DM) coupled to light mediators has been invoked to resolve the putative discrepancies between collisionless cold DM and galactic structure observations. However, $\gamma$-ray searches and the CMB strongly constrain such scenarios. To ease the tension, we consider asymmetric DM. We show that, contrary to the common lore, detectable annihilations occur even for large asymmetries, and derive bounds from the CMB, $\gamma$-ray, neutrino and antiproton searches. We then identify the viable space for self-interacting DM. Direct detection does not exclude this scenario, but provides a way to test it.

  49 citations

• QED at NNLO with McMule

  P. Banerjee, T. Engel, A. Signer, Y. Ulrich

  SciPost Phys. 9, 027 (2020) · published 31 August 2020 | Toggle abstract · pdf

  McMule is a framework for fully differential higher-order QED calculations of scattering and decay processes involving leptons. It keeps finite lepton masses, which regularises collinear singularities. Soft singularities are treated with dimensional regularisation and using FKS$^\ell$ subtraction. We describe the implementation of the framework in Fortran 95, list the processes that are currently implemented, and give instructions on how to run the code. In addition, we present new phenomenological results for muon-electron scattering and lepton-proton scattering, including the dominant NNLO corrections. While the applications presented focus on MUonE, MUSE, and P2, the code can be used for a large number of planned and running experiments.

  49 citations

• Phase space sampling and inference from weighted events with autoregressive flows

  Bob Stienen, Rob Verheyen

  SciPost Phys. 10, 038 (2021) · published 17 February 2021 | Toggle abstract · pdf

  We explore the use of autoregressive flows, a type of generative model with tractable likelihood, as a means of efficient generation of physical particle collider events. The usual maximum likelihood loss function is supplemented by an event weight, allowing for inference from event samples with variable, and even negative event weights. To illustrate the efficacy of the model, we perform experiments with leading-order top pair production events at an electron collider with importance sampling weights, and with next-to-leading-order top pair production events at the LHC that involve negative weights.

  48 citations

• Constraining new physics from Higgs measurements with Lilith: update to LHC Run 2 results

  Sabine Kraml, Tran Quang Loc, Dao Thi Nhung, Le Duc Ninh

  SciPost Phys. 7, 052 (2019) · published 18 October 2019 | Toggle abstract · pdf

  Lilith is a public Python library for constraining new physics from Higgs signal strength measurements. We here present version 2.0 of Lilith together with an updated XML database which includes the current ATLAS and CMS Run 2 Higgs results for 36/fb. Both the code and the database were extended from the ordinary Gaussian approximation employed in Lilith-1.1 to using variable Gaussian and Poisson likelihoods. Moreover, Lilith can now make use of correlation matrices of arbitrary dimension. We provide detailed validations of the implemented experimental results as well as a status of global fits for reduced Higgs couplings, Two-Higgs-doublet models of Type I and Type II, and invisible Higgs decays. Lilith-2.0 is available on GitHub and ready to be used to constrain a wide class of new physics scenarios.

  47 citations

• Quark-gluon tagging: Machine learning vs detector

  Gregor Kasieczka, Nicholas Kiefer, Tilman Plehn, Jennifer M. Thompson

  SciPost Phys. 6, 069 (2019) · published 18 June 2019 | Toggle abstract · pdf

  Distinguishing quarks from gluons based on low-level detector output is one of the most challenging applications of multi-variate and machine learning techniques at the LHC. We first show the performance of our 4-vector-based LoLa tagger without and after considering detector effects. We then discuss two benchmark applications, mono-jet searches with a gluon-rich signal and di-jet resonances with a quark-rich signal. In both cases an immediate benefit compared to the standard event-level analysis exists.

  47 citations

• STrEAMlining EFT Matching

  Timothy Cohen, Xiaochuan Lu, Zhengkang Zhang

  SciPost Phys. 10, 098 (2021) · published 3 May 2021 | Toggle abstract · pdf

  This paper presents STrEAM (SuperTrace Evaluation Automated for Matching), a Mathematica package that calculates all functional supertraces which arise when matching a generic UV model onto a relativistic Effective Field Theory (EFT) at one loop and to arbitrary order in the heavy mass expansion. STrEAM implements the covariant derivative expansion to automate the most tedious step of the streamlined functional matching prescription presented in arXiv:2011.02484 . The code and an example notebook are available at https://www.github.com/EFTMatching/STrEAM .

  47 citations

• Event generators for high-energy physics experiments

  J. M. Campbell, M. Diefenthaler, T. J. Hobbs, S. Höche, J. Isaacson, F. Kling, S. Mrenna, J. Reuter, S. Alioli, J. R. Andersen, C. Andreopoulos, A. M. Ankowski, E. C. Aschenauer, A. Ashkenazi, M. D. Baker, J. L. Barrow, M. van Beekveld, G. Bewick, S. Bhattacharya, C. Bierlich, E. Bothmann, P. Bredt, A. Broggio, A. Buckley, A. Butter, J. M. Butterworth, E. P. Byrne, C. M. Carloni-Calame, S. Chakraborty, X. Chen, M. Chiesa, J. T. Childers, J. Cruz-Martinez, J. Currie, N. Darvishi, M. Dasgupta, A. Denner, F. A. Dreyer, S. Dytman, B. K. El-Menoufi, T. Engel, S. Ferrario Ravasio, D. Figueroa, L. Flower, J. R. Forshaw, R. Frederix, A. Friedland, S. Frixione, H. Gallagher, K. Gallmeister, S. Gardiner, R. Gauld, J. Gaunt, A. Gavardi, T. Gehrmann, A. Gehrmann-De Ridder, L. Gellersen, W. Giele, S. Gieseke, F. Giuli, E. W. N. Glover, M. Grazzini, A. Grohsjean, C. Gütschow, K. Hamilton, T. Han, R. Hatcher, G. Heinrich, I. Helenius, O. Hen, V. Hirschi, M. Höfer, J. Holguin, A. Huss, P. Ilten, S. Jadach, A. Jentsch, S. P. Jones, W. Ju, S. Kallweit, A. Karlberg, T. Katori, M. Kerner, W. Kilian, M. M. Kirchgaeßer, S. Klein, M. Knobbe, C. Krause, F. Krauss, J. Lang, J. -N. Lang, G. Lee, S. W. Li, M. A. Lim, J. M. Lindert, D. Lombardi, L. Lönnblad, M. Löschner, N. Lurkin, Y. Ma, P. Machado, V. Magerya, A. Maier, I. Majer, F. Maltoni, M. Marcoli, G. Marinelli, M. R. Masouminia, P. Mastrolia, O. Mattelaer, J. Mazzitelli, J. McFayden, R. Medves, P. Meinzinger, J. Mo, P. F. Monni, G. Montagna, T. Morgan, U. Mosel, B. Nachman, P. Nadolsky, R. Nagar, Z. Nagy, D. Napoletano, P. Nason, T. Neumann, L. J. Nevay, O. Nicrosini, J. Niehues, K. Niewczas, T. Ohl, G. Ossola, V. Pandey, A. Papadopoulou, A. Papaefstathiou, G. Paz, M. Pellen, G. Pelliccioli, T. Peraro, F. Piccinini, L. Pickering, J. Pires, W. Placzek, S. Plätzer, T. Plehn, S. Pozzorini, S. Prestel, C. T. Preuss, A. C. Price, S. Quackenbush, E. Re, D. Reichelt, L. Reina, C. Reuschle, P. Richardson, M. Rocco, N. Rocco, M. Roda, A. Rodriguez Garcia, S. Roiser, J. Rojo, L. Rottoli, G. P. Salam, M. Schönherr, S. Schuchmann, S. Schumann, R. Schürmann, L. Scyboz, M. H. Seymour, F. Siegert, A. Signer, G. Singh Chahal, A. Siódmok, T. Sjöstrand, P. Skands, J. M. Smillie, J. T. Sobczyk, D. Soldin, D. E. Soper, A. Soto-Ontoso, G. Soyez, G. Stagnitto, J. Tena-Vidal, O. Tomalak, F. Tramontano, S. Trojanowski, Z. Tu, S. Uccirati, T. Ullrich, Y. Ulrich, M. Utheim, A. Valassi, A. Verbytskyi, R. Verheyen, M. Wagman, D. Walker, B. R. Webber, L. Weinstein, O. White, J. Whitehead, M. Wiesemann, C. Wilkinson, C. Williams, R. Winterhalder, C. Wret, K. Xie, T-Z. Yang, E. Yazgan, G. Zanderighi, S. Zanoli, K. Zapp

  SciPost Phys. 16, 130 (2024) · published 24 May 2024 | Toggle abstract · pdf

  We provide an overview of the status of Monte-Carlo event generators for high-energy particle physics. Guided by the experimental needs and requirements, we highlight areas of active development, and opportunities for future improvements. Particular emphasis is given to physics models and algorithms that are employed across a variety of experiments. These common themes in event generator development lead to a more comprehensive understanding of physics at the highest energies and intensities, and allow models to be tested against a wealth of data that have been accumulated over the past decades. A cohesive approach to event generator development will allow these models to be further improved and systematic uncertainties to be reduced, directly contributing to future experimental success. Event generators are part of a much larger ecosystem of computational tools. They typically involve a number of unknown model parameters that must be tuned to experimental data, while maintaining the integrity of the underlying physics models. Making both these data, and the analyses with which they have been obtained accessible to future users is an essential aspect of open science and data preservation. It ensures the consistency of physics models across a variety of experiments.

  47 citations