ATLAS Collaboration(Aad, G. et al), Aparisi Pozo, J. A., Bailey, A. J., Cabrera Urban, S., Cardillo, F., Castillo Gimenez, V., et al. (2021). The ATLAS Fast TracKer system. J. Instrum., 16(7), P07006–61pp.
Abstract: The ATLAS Fast TracKer (FTK) was designed to provide full tracking for the ATLAS high-level trigger by using pattern recognition based on Associative Memory (AM) chips and fitting in high-speed field programmable gate arrays. The tracks found by the FTK are based on inputs from all modules of the pixel and silicon microstrip trackers. The as-built FTK system and components are described, as is the online software used to control them while running in the ATLAS data acquisition system. Also described is the simulation of the FTK hardware and the optimization of the AM pattern banks. An optimization for long-lived particles with large impact parameter values is included. A test of the FTK system with the data playback facility that allowed the FTK to be commissioned during the shutdown between Run 2 and Run 3 of the LHC is reported. The resulting tracks from part of the FTK system covering a limited eta-phi region of the detector are compared with the output from the FTK simulation. It is shown that FTK performance is in good agreement with the simulation.
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ATLAS Collaboration(Aad, G. et al), Cabrera Urban, S., Castillo Gimenez, V., Costa, M. J., Fassi, F., Ferrer, A., et al. (2013). Characterisation and mitigation of beam-induced backgrounds observed in the ATLAS detector during the 2011 proton-proton run. J. Instrum., 8, P07004–72pp.
Abstract: This paper presents a summary of beam-induced backgrounds observed in the ATLAS detector and discusses methods to tag and remove background contaminated events in data. Trigger-rate based monitoring of beam-related backgrounds is presented. The correlations of backgrounds with machine conditions, such as residual pressure in the beam-pipe, are discussed. Results from dedicated beam-background simulations are shown, and their qualitative agreement with data is evaluated. Data taken during the passage of unpaired, i.e. non-colliding, proton bunches is used to obtain background-enriched data samples. These are used to identify characteristic features of beam-induced backgrounds, which then are exploited to develop dedicated background tagging tools. These tools, based on observables in the Pixel detector, the muon spectrometer and the calorimeters, are described in detail and their efficiencies are evaluated. Finally an example of an application of these techniques to a monojet analysis is given, which demonstrates the importance of such event cleaning techniques for some new physics searches.
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Agaras, M. N. et al, & Fiorini, L. (2023). Laser calibration of the ATLAS Tile Calorimeter during LHC Run 2. J. Instrum., 18(6), P06023–35pp.
Abstract: This article reports the laser calibration of the hadronic Tile Calorimeter of the ATLAS experiment in the LHC Run 2 data campaign. The upgraded Laser II calibration system is described. The system was commissioned during the first LHC Long Shutdown, exhibiting a stability better than 0.8% for the laser light monitoring. The methods employed to derive the detector calibration factors with data from the laser calibration runs are also detailed. These allowed to correct for the response fluctuations of the 9852 photomultiplier tubes of the Tile Calorimeter with a total uncertainty of 0.5% plus a luminosity-dependent sub-dominant term. Finally, we report the regular monitoring and performance studies using laser events in both standalone runs and during proton collisions. These studies include channel timing and quality inspection, and photomultiplier linearity and response dependence on anode current.
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de los Rios, M., Petac, M., Zaldivar, B., Bonaventura, N. R., Calore, F., & Iocco, F. (2023). Determining the dark matter distribution in simulated galaxies with deep learning. Mon. Not. Roy. Astron. Soc., 525(4), 6015–6035.
Abstract: We present a novel method of inferring the dark matter (DM) content and spatial distribution within galaxies, using convolutional neural networks (CNNs) trained within state-of-the-art hydrodynamical simulations (Illustris-TNG100). Within the controlled environment of the simulation, the framework we have developed is capable of inferring the DM mass distribution within galaxies of mass similar to 10(11)-10(13)M(circle dot) from the gravitationally baryon-dominated internal regions to the DM-rich, baryon-depleted outskirts of the galaxies, with a mean absolute error always below approximate to 0.25 when using photometrical and spectroscopic information. With respect to traditional methods, the one presented here also possesses the advantages of not relying on a pre-assigned shape for the DM distribution, to be applicable to galaxies not necessarily in isolation, and to perform very well even in the absence of spectroscopic observations.
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LHCb Collaboration(Aaij, R. et al), Martinez-Vidal, F., Oyanguren, A., Ruiz Valls, P., & Sanchez Mayordomo, C. (2015). Identification of beauty and charm quark jets at LHCb. J. Instrum., 10, P06013–29pp.
Abstract: Identification of jets originating from beauty and charm quarks is important for measuring Standard Model processes and for searching for new physics. The performance of algorithms developed to select b- and c-quark jets is measured using data recorded by LHCb from proton-proton collisions at root s = 7TeV in 2011 and at root s = 8TeV in 2012. The efficiency for identifying a b (c) jet is about 65%(25%) with a probability for misidentifying a light-parton jet of 0.3% for jets with transverse momentum pT > 20GeV and pseudorapidity 2 : 2 < eta < 4.2. The dependence of the performance on the pT and eta of the jet is also measured.
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