Abstract: Silicon pixel detectors are at the core of the current and planned upgrade of the ATLAS experiment at the LHC. Given their close proximity to the interaction point, these detectors will be exposed to an unprecedented amount of radiation over their lifetime. The current pixel detector will receive damage from non-ionizing radiation in excess of 10(15) 1 MeV n(eq)/cm(2), while the pixel detector designed for the high-luminosity LHC must cope with an order of magnitude larger fluence. This paper presents a digitization model incorporating effects of radiation damage to the pixel sensors. The model is described in detail and predictions for the charge collection efficiency and Lorentz angle are compared with collision data collected between 2015 and 2017 (<= 10(15) 1 MeV n(eq)/cm(2)).

Abstract: The analog signals generated in the read-out electronics of particle detectors are shaped prior to the digitization in order to improve the signal to noise ratio (SNR). The real amplitude of the analog signal is then obtained using digital filters, which provides information about the energy deposited in the detector. The classical digital filters have a good performance in ideal situations with Gaussian electronic noise and no pulse shape distortion. However, high-energy particle colliders, such as the Large Hadron Collider (LHC) at CERN, can produce multiple simultaneous events, which produce signal pileup. The performance of classical digital filters deteriorates in these conditions since the signal pulse shape gets distorted. In addition, this type of experiments produces a high rate of collisions, which requires high throughput data acquisitions systems. In order to cope with these harsh requirements, new read-out electronics systems are based on high-performance FPGAs, which permit the utilization of more advanced real-time signal reconstruction algorithms. In this paper, a deep learning method is proposed for real-time signal reconstruction in high pileup particle detectors. The performance of the new method has been studied using simulated data and the results are compared with a classical FIR filter method. In particular, the signals and FIR filter used in the ATLAS Tile Calorimeter are used as benchmark. The implementation, resources usage and performance of the proposed Neural Network algorithm in FPGA are also presented.

Abstract: The production of W bosons in association with two jets in proton-proton collisions at a centre-of-mass energy of root s = 7 TeV has been analysed for the presence of double-parton interactions using data corresponding to an integrated luminosity of 36 pb(-1), collected with the ATLAS detector at the Large Hadron Collider. The fraction of events arising from double-parton interactions, f(DP)((D)), has been measured through the p(T) balance between the two jets and amounts to f(DP)((D)) = 0.08 +/- 0.01 (stat.) +/- 0.02 (sys.) for jets with transverse momentum p(T) > 20 GeV and rapidity vertical bar y vertical bar < 2.8. This corresponds to a measurement of the effective area parameter for hard double-parton interactions of sigma(eff) = 15 +/- 3 (stat.)(-3)(+5) (sys.) mb.

Abstract: The large difference between the Planck scale and the electroweak scale, known as the hierarchy problem, is addressed in certain models through the postulate of extra spatial dimensions. A search for evidence of extra spatial dimensions in the diphoton channel has been performed using the full set of proton-proton collisions at root s = 7 TeV recorded in 2011 with the ATLAS detector at the CERN Large Hadron Collider. This dataset corresponds to an integrated luminosity of 4.9 fb(-1). The diphoton invariant mass spectrum is observed to be in good agreement with the Standard Model expectation. In the context of the model proposed by Arkani-Hamed, Dimopoulos and Dvali, 95% confidence level lower limits of between 2.52 and 3.92 TeV are set on the ultraviolet cutoff scale MS depending on the number of extra dimensions and the theoretical formalism used. In the context of the Randall-Sundrum model, a lower limit of 2.06 (1.00) TeV at 95% confidence level is set on the mass of the lightest graviton for couplings of k/(M) over bar (Pl) = 0.1(0.01). Combining with the ATLAS dilepton searches based on the 2011 data, the 95% confidence level lower limit on the Randall-Sundrum graviton mass is further tightened to 2.23 (1.03) TeV for k/(M) over bar (Pl) = 0.1(0.01).

Abstract: The ATLAS detector at the Large Hadron Collider reads out particle collision data from over 100 million electronic channels at a rate of approximately 100 kHz, with a recording rate for physics events of approximately 1 kHz. Before being certified for physics analysis at computer centres worldwide, the data must be scrutinised to ensure they are clean from any hardware or software related issues that may compromise their integrity. Prompt identification of these issues permits fast action to investigate, correct and potentially prevent future such problems that could render the data unusable. This is achieved through the monitoring of detector-level quantities and reconstructed collision event characteristics at key stages of the data processing chain. This paper presents the monitoring and assessment procedures in place at ATLAS during 2015-2018 data-taking. Through the continuous improvement of operational procedures, ATLAS achieved a high data quality efficiency, with 95.6% of the recorded proton-proton collision data collected at root s = 13 TeV certified for physics analysis.