ATLAS Collaboration(Aad, G. et al), Alvarez Piqueras, D., Aparisi Pozo, J. A., Bailey, A. J., Barranco Navarro, L., Cabrera Urban, S., et al. (2019). Properties of jet fragmentation using charged particles measured with the ATLAS detector in pp collisions at root s=13 TeV. Phys. Rev. D, 100(5), 052011–38pp.
Abstract: This paper presents a measurement of quantities related to the formation of jets from high-energy quarks and gluons (fragmentation). Jets with transverse momentum 100 GeV < p(T) < 2.5 TeV and pseudorapidity vertical bar eta vertical bar < 2.1 from an integrated luminosity of 33 fb(-1) of root s = 13 TeV proton-proton collisions are reconstructed with the ATLAS detector at the Large Hadron Collider. Charged-particle tracks with p(T) > 500 MeV and vertical bar eta vertical bar < 2.5 are used to probe the detailed structure of the jet. The fragmentation properties of the more forward and the more central of the two leading jets from each event are studied. The data are unfolded to correct for detector resolution and acceptance effects. Comparisons with parton shower Monte Carlo generators indicate that existing models provide a reasonable description of the data across a wide range of phase space, but there are also significant differences. Furthermore, the data are interpreted in the context of quark- and gluon-initiated jets by exploiting the rapidity dependence of the jet flavor fraction. A first measurement of the charged-particle multiplicity using model-independent jet labels (topic modeling) provides a promising alternative to traditional quark and gluon extractions using input from simulation. The simulations provide a reasonable description of the quark-like data across the jet p(T) range presented in-this measurement, but the gluon-like data have systematically fewer charged particles than the simulation.
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Caputo, A., Sberna, L., Frias, M., Blas, D., Pani, P., Shao, L. J., et al. (2019). Constraints on millicharged dark matter and axionlike particles from timing of radio waves. Phys. Rev. D, 100(6), 063515–7pp.
Abstract: We derive constraints on millicharged dark matter and axionlike particles using pulsar timing and fast radio burst observations. For dark matter particles of charge epsilon e, the constraint from time of arrival (TOA) of waves is epsilon/m(milli) less than or similar to 10(-8) eV(-1), for masses m(milli) greater than or similar to 10(-6) eV. For axionlike particles, the polarization of the signals from pulsars yields a bound in the axial coupling g/ m(a) less than or similar to 10(-13) Gev(-1)/(10(-22) eV),for m(a) less than or similar to 10(-19) eV. Both bounds scale as (rho/rho(dm))(1/2 )for fractions of the total dark matter energy density rho(dm). We make a precise study of these bounds using TOA from several pulsars, FRB 121102, and polarization measurements of PSR J0437 – 4715. Our results rule out a new region of the parameter space for these dark matter models.
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Coppola, M., Gomez Dumm, D., Noguera, S., & Scoccola, N. N. (2019). Neutral and charged pion properties under strong magnetic fields in the NJL model. Phys. Rev. D, 100(5), 054014–17pp.
Abstract: In the framework of the Nambu-Jona-Lasino (NJL) model, we study the effect of an intense external uniform magnetic field on neutral and charged pion masses and decay form factors. In particular, the treatment of charged pions is carried out on the basis of the Ritus eigenfunction approach to magnetized relativistic systems. Our analysis shows that in the presence of the magnetic field three and four nonvanishing pion-to-vacuum hadronic form factors can be obtained for the case of the neutral and charged pions, respectively. As expected, it is seen that for nonzero magnetic field the pi(0) meson can still be treated as a pseudo Nambu-Goldstone boson, and consequently the corresponding form factors are shown to satisfy various chiral relations. For definite parametrizations of the model, numerical results for pi(0) and pi(+/-) masses and decay constants are obtained and compared with previous calculations given in the literature.
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ATLAS Collaboration(Aad, G. et al), Alvarez Piqueras, D., Aparisi Pozo, J. A., Bailey, A. J., Barranco Navarro, L., Cabrera Urban, S., et al. (2019). Resolution of the ATLAS muon spectrometer monitored drift tubes in LHC Run 2. J. Instrum., 14, P09011–35pp.
Abstract: The momentum measurement capability of the ATLAS muon spectrometer relies fundamentally on the intrinsic single-hit spatial resolution of the monitored drift tube precision tracking chambers. Optimal resolution is achieved with a dedicated calibration program that addresses the specific operating conditions of the 354 000 high-pressure drift tubes in the spectrometer. The calibrations consist of a set of timing offsets and drift time to drift distance transfer relations, and result in chamber resolution functions. This paper describes novel algorithms to obtain precision calibrations from data collected by ATLAS in LHC Run 2 and from a gas monitoring chamber, deployed in a dedicated gas facility. The algorithm output consists of a pair of correction constants per chamber which are applied to baseline calibrations, and determined to be valid for the entire ATLAS Run 2. The final single-hit spatial resolution, averaged over 1172 monitored drift tube chambers, is 81.7 +/- 2.2 μm.
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Ortiz Arciniega, J. L., Carrio, F., & Valero, A. (2019). FPGA implementation of a deep learning algorithm for real-time signal reconstruction in particle detectors under high pile-up conditions. J. Instrum., 14, P09002–13pp.
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.
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