LHCb Collaboration(Aaij, R. et al), Jashal, B. K., Martinez-Vidal, F., Oyanguren, A., Remon Alepuz, C., & Ruiz Vidal, J. (2022). Identification of charm jets at LHCb. J. Instrum., 17(2), P02028–23pp.
Abstract: The identification of charm jets is achieved at LHCb for data collected in 2015-2018 using a method based on the properties of displaced vertices reconstructed and matched with jets. The performance of this method is determined using a dijet calibration dataset recorded by the LHCb detector and selected such that the jets are unbiased in quantities used in the tagging algorithm. The charm-tagging efficiency is reported as a function of the transverse momentum of the jet. The measured efficiencies are compared to those obtained from simulation and found to be in good agreement.
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Ayala, C., & Cvetic, G. (2016). anQCD: Fortran programs for couplings at complex momenta in various analytic QCD models. Comput. Phys. Commun., 199, 114–117.
Abstract: We provide three Fortran programs which evaluate the QCD analytic (holomorphic) couplings A(v)(Q(2)) for complex or real squared momenta Q(2). These couplings are holomorphic analogs of the powers a(Q(2))(v) of the underlying perturbative QCD (pQCD) coupling a(Q(2)) equivalent to alpha(s)(Q(2))/pi, in three analytic QCD models (anQCD): Fractional Analytic Perturbation Theory (FAPT), Two-delta analytic QCD (2 delta anQCD), and Massive Perturbation Theory (MPT). The index v can be noninteger. The provided programs do basically the same job as the Mathematica package anQCD.m published by us previously (Ayala and Cvetic, 2015), but are now written in Fortran. Program summary Program title: AanQCDext Catalogue identifier: AEYKv10 Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AEYICv1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 12105 No. of bytes in distributed program, including test data, etc.: 98822 Distribution format: tar.gz Programming language: Fortran. Computer: Any work-station or PC where Fortran 95/200312008 (gfortran) is running. Operating system: Operating system Linux (Ubuntu and Scientific Linux), Windows (in all cases using gfortran). Classification: 11.1, 11.5. Nature of problem: Calculation of values of the running analytic couplings A(v)(Q(2); N-f) for general complex squared momenta Q(2) equivalent to -q(2), in three analytic QCD models, where A(v)(Q(2); N-f) is the analytic (holomorphic) analog of the power (alpha(s)(Q(2); N-f)/pi)(v). Here, A(v)(Q(2); N-f) is a holomorphic function in the Q(2) complex plane, with the exception of the negative semiaxis (-infinity, -M-thr(2)], reflecting the analyticity properties of the spacelike renormalization invariant quantities D(Q(2)) in QCD. In contrast, the perturbative QCD power (alpha(s)(Q(2); N-f)/pi)(v) has singularities even outside the negative semiaxis (Landau ghosts). The three considered models are: Analytic Perturbation theory (APT); Two-delta analytic QCD (2 delta anQCD); Massive Perturbation Theory (MPT). We refer to Ref. [1] for more details and literature. Solution method: The Fortran programs for FAPT and 2 delta anQCD models contain routines and functions needed to perform two-dimensional numerical integrations involving the spectral function, in order to evaluate A(v)(Q(2)) couplings. In MPT model, one-dimensional numerical integration involving A(1)(Q(2)) is sufficient to evaluate any A(v)(Q(2)) coupling. Restrictions: For unphysical choices of the input parameters the results are meaningless. When Q(2) is close to the cut region of the couplings (Q(2) real negative), the calculations can take more time and can have less precision. Running time: For evaluation of a set of about 10 related couplings, the times vary in the range t similar to 10(1)-10(2) s. MPT requires less time, t similar to 1-10(1) s. References: [1] C. Ayala and G. Cvetic, anQCD: a Mathematica package for calculations in general analytic QCD models, Comput. Phys. Commun. 190 (2015) 182.
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Clausse, A., Soto, L., & Tarifeño-Saldivia, A. (2015). Influence of the Anode Length on the Neutron Emission of a 50 J Plasma Focus: Modeling and Experiment. IEEE Trans. Plasma Sci., 43(2), 629–636.
Abstract: A comprehensive set of electric data measured in a small plasma focus (PF) device of 50 J correlated with the corresponding neutron emissions is taken as the base for developing a semiempirical model of the current sheet dynamics and the neutron yield. The model is able to explain the dependence of the neutron yield with the pressure and anode length with good accuracy, and suggests a physical interpretation of the drive parameter commonly used in PF design.
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Delgado, R. L., Gomez-Ambrosio, R., Martinez-Martin, J., Salas-Bernardez, A., & Sanz-Cillero, J. J. (2024). Production of two, three, and four Higgs bosons: where SMEFT and HEFT depart. J. High Energy Phys., 03(3), 037–45pp.
Abstract: In this article we study the phenomenological implications of multiple Higgs boson production from longitudinal vector boson scattering in the context of effective field theories. We find compact representations for effective tree-level amplitudes with up to four final state Higgs bosons. Total cross sections are then computed for scenarios relevant at the LHC in which we find the general Higgs Effective Theory (HEFT) prediction avoids the heavy suppression observed in Standard Model Effective Field Theory (SMEFT).
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Kasieczka, G. et al, & Sanz, V. (2021). The LHC Olympics 2020: a community challenge for anomaly detection in high energy physics. Rep. Prog. Phys., 84(12), 124201–64pp.
Abstract: A new paradigm for data-driven, model-agnostic new physics searches at colliders is emerging, and aims to leverage recent breakthroughs in anomaly detection and machine learning. In order to develop and benchmark new anomaly detection methods within this framework, it is essential to have standard datasets. To this end, we have created the LHC Olympics 2020, a community challenge accompanied by a set of simulated collider events. Participants in these Olympics have developed their methods using an R&D dataset and then tested them on black boxes: datasets with an unknown anomaly (or not). Methods made use of modern machine learning tools and were based on unsupervised learning (autoencoders, generative adversarial networks, normalizing flows), weakly supervised learning, and semi-supervised learning. This paper will review the LHC Olympics 2020 challenge, including an overview of the competition, a description of methods deployed in the competition, lessons learned from the experience, and implications for data analyses with future datasets as well as future colliders.
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