ATLAS Collaboration(Aaboud, M. et al), Alvarez Piqueras, D., Bailey, A. J., Barranco Navarro, L., Cabrera Urban, S., Castillo Gimenez, V., et al. (2018). Comparison between simulated and observed LHC beam backgrounds in the ATLAS experiment at E-beam=4 TeV. J. Instrum., 13, P12006–41pp.
Abstract: Results of dedicated Monte Carlo simulations of beam-induced background (BIB) in the ATLAS experiment at the Large Hadron Collider (LHC) are presented and compared with data recorded in 2012. During normal physics operation this background arises mainly from scattering of the 4 TeV protons on residual gas in the beam pipe. Methods of reconstructing the BIB signals in the ATLAS detector, developed and implemented in the simulation chain based on the FLUKA Monte Carlo simulation package, are described. The interaction rates are determined from the residual gas pressure distribution in the LHC ring in order to set an absolute scale on the predicted rates of BIB so that they can be compared quantitatively with data. Through these comparisons the origins of the BIB leading to different observables in the ATLAS detectors are analysed. The level of agreement between simulation results and BIB measurements by ATLAS in 2012 demonstrates that a good understanding of the origin of BIB has been reached.
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ATLAS Collaboration(Aad, G. et al), Alvarez Piqueras, D., Aparisi Pozo, J. A., Bailey, A. J., Cabrera Urban, S., Castillo, F. L., et al. (2019). Electron and photon performance measurements with the ATLAS detector using the 2015-2017 LHC proton-proton collision data. J. Instrum., 14, P12006–69pp.
Abstract: This paper describes the reconstruction of electrons and photons with the ATLAS detector, employed for measurements and searches exploiting the complete LHC Run 2 dataset. An improved energy clustering algorithm is introduced, and its implications for the measurement and identification of prompt electrons and photons are discussed in detail. Corrections and calibrations that affect performance, including energy calibration, identification and isolation efficiencies, and the measurement of the charge of reconstructed electron candidates are determined using up to 81 fb(-1) of proton-proton collision data collected at root s = 13 TeV between 2015 and 2017.
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Renner, J., Cervera-Villanueva, A., Hernando, J. A., Izmaylov, A., Monrabal, F., Muñoz, J., et al. (2015). Improved background rejection in neutrinoless double beta decay experiments using a magnetic field in a high pressure xenon TPC. J. Instrum., 10, P12020–19pp.
Abstract: We demonstrate that the application of an external magnetic field could lead to an improved background rejection in neutrinoless double-beta (0 nu beta beta) decay experiments using a high-pressure xenon (HPXe) TPC. HPXe chambers are capable of imaging electron tracks, a feature that enhances the separation between signal events (the two electrons emitted in the 0 nu beta beta decay of Xe-136) and background events, arising chiefly from single electrons of kinetic energy compatible with the end-point of the 0 nu beta beta decay (Q(beta beta)). Applying an external magnetic field of sufficiently high intensity (in the range of 0.5-1 Tesla for operating pressures in the range of 5-15 atmospheres) causes the electrons to produce helical tracks. Assuming the tracks can be properly reconstructed, the sign of the curvature can be determined at several points along these tracks, and such information can be used to separate signal (0 nu beta beta) events containing two electrons producing a track with two different directions of curvature from background (single-electron) events producing a track that should spiral in a single direction. Due to electron multiple scattering, this strategy is not perfectly efficient on an event-by-event basis, but a statistical estimator can be constructed which can be used to reject background events by one order of magnitude at a moderate cost (about 30%) in signal efficiency. Combining this estimator with the excellent energy resolution and topological signature identification characteristic of the HPXe TPC, it is possible to reach a background rate of less than one count per ton-year of exposure. Such a low background rate is an essential feature of the next generation of 0 nu beta beta experiments, aiming to fully explore the inverse hierarchy of neutrino masses.
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Davesne, D., Pastore, A., & Navarro, J. (2014). Fitting (NLO)-L-3 pseudo-potentials through central plus tensor Landau parameters. J. Phys. G, 41(6), 065104–12pp.
Abstract: Landau parameters determined from phenomenological finite-range interactions are used to get an estimation of next-to-next-to-next-to-leading order ((NLO)-L-3) pseudo-potentials parameters. The parameter sets obtained in this way are shown to lead to consistent results concerning saturation properties. The uniqueness of this procedure is discussed, and an estimate of the error induced by the truncation at (NLO)-L-3 is given.
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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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