Publications

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• Measurements and searches of Higgs boson production involving fermion couplings with the ATLAS detector

  Shigeki Hirose

  SciPost Phys. Proc. 8, 006 (2022) · published 11 July 2022 | Toggle abstract · pdf

  In the standard model, fermions acquire the mass via the Yukawa interaction. This mechanism can be tested by measuring couplings of the fermions with the Higgs boson. At the ATLAS experiment, the Higgs-fermion coupling measurements became possible thanks to the abundant dataset: the integrated luminosity of 139~fb$^{-1}$ at the centre-of-mass energy of 13~TeV as well as 25~fb$^{-1}$ at 7--8~TeV. This paper presents recent results from measurements of the Higgs boson productions in decays to a fermion pair in the final state with the ATLAS detector.

• Structure Functions and Parton Densities: a Session Summary

  Benjamin Nachman, Katarzyna Wichmann, Pia Zurita

  SciPost Phys. Proc. 8, 004 (2022) · published 11 July 2022 | Toggle abstract · pdf

  Studies of fragmentation and parton density functions are a core component of researchin high energy particle and nuclear physics. These quantities are inherently interestingas a probe of the quantum nature of the strong force and are also essential ingredients toadditional studies in high energy scattering experiments. These proceedings provide anoverview of the state of the art in this area, as presented at the Deep-Inelastic ScatteringConference in the Spring of 2021.

  1 citation

• Recent Highlights on Higgs Physics from the LHC

  Elisabeth Schopf

  SciPost Phys. Proc. 8, 001 (2022) · published 11 July 2022 | Toggle abstract · pdf

  Many of the Higgs boson production and decay channels, predicted by the StandardModel, have been observed since its 2012 discovery. Today, measurements of the Higgsboson’s properties in these channels deepen our understanding of the nature of thisfundamental particle by providing better information about its mass, production kine-matics, and CP structure. Increasing precision also enables targeting rare Higgs bosondecay modes and probing di-Higgs-boson production. In addition, searches for beyondStandard Model physics in the Higgs sector are performed. This article presents the lat-est Higgs boson measurement highlights from the ATLAS and CMS collaborations usingLHC proton-proton collision data at a centre of mass energy of 13 TeV.

• Inclusive decays of heavy quark hybrids

  Abhishek Mohapatra

  SciPost Phys. Proc. 6, 008 (2022) · published 31 May 2022 | Toggle abstract · pdf

  In order to understand the nature of the XYZ particles, theoretical predictions of the various XYZ decay modes are essential. In this work, we focus on the semi-inclusive decays of heavy quarkonium hybrids into traditional quarkonium in the EFT framework. We begin with weakly coupled potential NRQCD effective theory that describes systems with two heavy quarks and incorporates multipole expansions and use it to develop a Born-Oppenheimer effective theory (BOEFT) to describe the hybrids and compute the semi-inclusive decay rates. We compute both the spin-conserving and spin-flipping decay rates and find that our numerical results of the decay rates are different from the previous studies. We also develop a systematic framework in which the theoretical uncertainty can be systematically improved.

• SPANet: Generalized permutationless set assignment for particle physics using symmetry preserving attention

  Alexander Shmakov, Michael James Fenton, Ta-Wei Ho, Shih-Chieh Hsu, Daniel Whiteson, Pierre Baldi

  SciPost Phys. 12, 178 (2022) · published 30 May 2022 | Toggle abstract · pdf

  The creation of unstable heavy particles at the Large Hadron Collider is the most direct way to address some of the deepest open questions in physics. Collisions typically produce variable-size sets of observed particles which have inherent ambiguities complicating the assignment of observed particles to the decay products of the heavy particles. Current strategies for tackling these challenges in the physics community ignore the physical symmetries of the decay products and consider all possible assignment permutations and do not scale to complex configurations. Attention based deep learning methods for sequence modelling have achieved state-of-the-art performance in natural language processing, but they lack built-in mechanisms to deal with the unique symmetries found in physical set-assignment problems. We introduce a novel method for constructing symmetry-preserving attention networks which reflect the problem's natural invariances to efficiently find assignments without evaluating all permutations. This general approach is applicable to arbitrarily complex configurations and significantly outperforms current methods, improving reconstruction efficiency between 19\% - 35\% on typical benchmark problems while decreasing inference time by two to five orders of magnitude on the most complex events, making many important and previously intractable cases tractable. A full code repository containing a general library, the specific configuration used, and a complete dataset release, are avaiable at https://github.com/Alexanders101/SPANet

  24 citations

• Dark Matter direct detection of classical WIMPs

  Jodi Cooley

  SciPost Phys. Lect. Notes 55 (2022) · published 20 May 2022 | Toggle abstract · pdf

  One of the highest priorities in particle physics today is the identification of the constituents of dark matter. This manuscript is a supplement to pedagogical lectures given at the 2021 Les Houches Summer School on Dark Matter. The lectures cover topics related to the direct detection of WIMP dark matter, including the distribution of dark matter, nuclear scattering, backgrounds, planning and designing of experiments, and a sampling of planned and ongoing experiments.

  8 citations

• Accelerating Monte Carlo event generation -- rejection sampling using neural network event-weight estimates

  Katharina Danziger, Timo Janßen, Steffen Schumann, Frank Siegert

  SciPost Phys. 12, 164 (2022) · published 18 May 2022 | Toggle abstract · pdf

  The generation of unit-weight events for complex scattering processes presents a severe challenge to modern Monte Carlo event generators. Even when using sophisticated phase-space sampling techniques adapted to the underlying transition matrix elements, the efficiency for generating unit-weight events from weighted samples can become a limiting factor in practical applications. Here we present a novel two-staged unweighting procedure that makes use of a neural-network surrogate for the full event weight. The algorithm can significantly accelerate the unweighting process, while it still guarantees unbiased sampling from the correct target distribution. We apply, validate and benchmark the new approach in high-multiplicity LHC production processes, including $Z/W$+4 jets and $t\bar{t}$+3 jets, where we find speed-up factors up to ten.

  23 citations

• An introduction to axions and their detection

  Igor García Irastorza

  SciPost Phys. Lect. Notes 45 (2022) · published 29 March 2022 | Toggle abstract · pdf

  In these notes I try to introduce the reader to the topic of axions: their theoretical motivation and expected phenomenology, their role in astrophysics and as dark matter candidate, and the experimental techniques to detect them. Special emphasis is made in this last point, for which a relatively updated review of worldwide efforts and future prospects is made. The material is intended as an introduction to the topic, and it was prepared as lecture notes for Les Houches summer school 2021. Abundant references are included to direct the reader to deeper insight on the different aspects of axion physics.

  13 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.

  16 citations

• The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider

  T. Aarrestad, M. van Beekveld, M. Bona, A. Boveia, S. Caron, J. Davies, A. De Simone, C. Doglioni, J. M. Duarte, A. Farbin, H. Gupta, L. Hendriks, L. Heinrich, J. Howarth, P. Jawahar, A. Jueid, J. Lastow, A. Leinweber, J. Mamuzic, E. Merényi, A. Morandini, P. Moskvitina, C. Nellist, J. Ngadiuba, B. Ostdiek, M. Pierini, B. Ravina, R. Ruiz de Austri, S. Sekmen, M. Touranakou, M. Vaškevičiūte, R. Vilalta, J. R. Vlimant, R. Verheyen, M. White, E. Wulff, E. Wallin, K. A. Wozniak, Z. Zhang

  SciPost Phys. 12, 043 (2022) · published 28 January 2022 | Toggle abstract · pdf

  We describe the outcome of a data challenge conducted as part of the Dark Machines Initiative and the Les Houches 2019 workshop on Physics at TeV colliders. The challenged aims at detecting signals of new physics at the LHC using unsupervised machine learning algorithms. First, we propose how an anomaly score could be implemented to define model-independent signal regions in LHC searches. We define and describe a large benchmark dataset, consisting of >1 Billion simulated LHC events corresponding to $10~\rm{fb}^{-1}$ of proton-proton collisions at a center-of-mass energy of 13 TeV. We then review a wide range of anomaly detection and density estimation algorithms, developed in the context of the data challenge, and we measure their performance in a set of realistic analysis environments. We draw a number of useful conclusions that will aid the development of unsupervised new physics searches during the third run of the LHC, and provide our benchmark dataset for future studies at https://www.phenoMLdata.org. Code to reproduce the analysis is provided at https://github.com/bostdiek/DarkMachines-UnsupervisedChallenge.

  57 citations