MoEDAL Collaboration(Acharya, B. et al), Bernabeu, J., Garcia, C., King, M., Mitsou, V. A., Vento, V., et al. (2016). Search for magnetic monopoles with the MoEDAL prototype trapping detector in 8 TeV proton-proton collisions at the LHC. J. High Energy Phys., 08(8), 067–25pp.
Abstract: The MoEDAL experiment is designed to search for magnetic monopoles and other highly-ionising particles produced in high-energy collisions at the LHC. The largely passive MoEDAL detector, deployed at Interaction Point 8 on the LHC ring, relies on two dedicated direct detection techniques. The first technique is based on stacks of nuclear-track detectors with surface area similar to 18 m(2), sensitive to particle ionisation exceeding a high threshold. These detectors are analysed offline by optical scanning microscopes. The second technique is based on the trapping of charged particles in an array of roughly 800 kg of aluminium samples. These samples are monitored offline for the presence of trapped magnetic charge at a remote superconducting magnetometer facility. We present here the results of a search for magnetic monopoles using a 160 kg prototype MoEDAL trapping detector exposed to 8TeV proton-proton collisions at the LHC, for an integrated luminosity of 0.75 fb(-1). No magnetic charge exceeding 0.5g(D) (where g(D) is the Dirac magnetic charge) is measured in any of the exposed samples, allowing limits to be placed on monopole production in the mass range 100 GeV <= m <= 3500 GeV. Model-independent cross-section limits are presented in fiducial regions of monopole energy and direction for 1g(D) <= vertical bar g vertical bar <= 6g(D), and model-dependent cross-section limits are obtained for Drell-Yan pair production of spin-1/2 and spin-0 monopoles for 1g(D) <= vertical bar g vertical bar <= 4g(D). Under the assumption of Drell-Yan cross sections, mass limits are derived for vertical bar g vertical bar = 2g(D) and vertical bar g vertical bar = 3g(D) for the first time at the LHC, surpassing the results from previous collider experiments.
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MoEDAL Collaboration(Acharya, B. et al), Bernabeu, J., Garcia, C., King, M., Mitsou, V. A., Vento, V., et al. (2014). The physics programme of the MoEDAL experiment at the LHC. Int. J. Mod. Phys. A, 29(23), 1430050–91pp.
Abstract: The MoEDAL experiment at Point 8 of the LHC ring is the seventh and newest LHC experiment. It is dedicated to the search for highly-ionizing particle avatars of physics beyond the Standard Model, extending significantly the discovery horizon of the LHC. A MoEDAL discovery would have revolutionary implications for our fundamental understanding of the Microcosm. MoEDAL is an unconventional and largely passive LHC detector comprised of the largest array of Nuclear Track Detector stacks ever deployed at an accelerator, surrounding the intersection region at Point 8 on the LHC ring. Another novel feature is the use of paramagnetic trapping volumes to capture both electrically and magnetically charged highly-ionizing particles predicted in new physics scenarios. It includes an array of TimePix pixel devices for monitoring highly-ionizing particle backgrounds. The main passive elements of the MoEDAL detector do not require a trigger system, electronic readout, or online computerized data acquisition. The aim of this paper is to give an overview of the MoEDAL physics reach, which is largely complementary to the programs of the large multipurpose LHC detectors ATLAS and CMS.
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Martin-Luna, P., Gimeno, B., Gonzalez-Iglesias, D., Esperante, D., Blanch, C., Fuster-Martinez, N., et al. (2023). On the Magnetic Field of a Finite Solenoid. IEEE Trans. Magn., 59(4), 7000106–6pp.
Abstract: The magnetostatic field of a finite solenoid with infinitely thin walls carrying a dc current oriented in the azimuthal direction is calculated everywhere in space in terms of complete elliptic integrals by direct integration of the Biot-Savart law. The solution is particularized near the solenoid axis and in the midplane perpendicular to the axis obtaining expressions that agree with some typical approximations that are made in introductory courses of electromagnetism or in the technical literature. The range of validity of these approximations has been studied comparing them with the obtained general expression.
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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. (2014). Monitoring and data quality assessment of the ATLAS liquid argon calorimeter. J. Instrum., 9, P07024–55pp.
Abstract: The liquid argon calorimeter is a key component of the ATLAS detector installed at the CERN Large Hadron Collider. The primary purpose of this calorimeter is the measurement of electron and photon kinematic properties. It also provides a crucial input for measuring jets and missing transverse momentum. An advanced data monitoring procedure was designed to quickly identify issues that would affect detector performance and ensure that only the best quality data are used for physics analysis. This article presents the validation procedure developed during the 2011 and 2012 LHC data-taking periods, in which more than 98% of the proton-proton luminosity recorded by ATLAS at a centre-of-mass energy of 7-8 TeV had calorimeter data quality suitable for physics analysis.
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LHCb Collaboration(Aaij, R. et al), Martinez-Vidal, F., Oyanguren, A., Ruiz Valls, P., & Sanchez Mayordomo, C. (2015). LHCb detector performance. Int. J. Mod. Phys. A, 30(7), 1530022–73pp.
Abstract: The LHCb detector is a forward spectrometer at the Large Hadron Collider (LHC) at CERN. The experiment is designed for precision measurements of CP violation and rare decays of beauty and charm hadrons. In this paper the performance of the various LHCb sub-detectors and the trigger system are described, using data taken from 2010 to 2012. It is shown that the design criteria of the experiment have been met. The excellent performance of the detector has allowed the LHCb collaboration to publish a wide range of physics results, demonstrating LHCb's unique role, both as a heavy flavour experiment and as a general purpose detector in the forward region.
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KM3NeT Collaboration(Adrian-Martinez, S. et al), Barrios-Marti, J., Calvo, D., Hernandez-Rey, J. J., Illuminati, G., Lotze, M., et al. (2016). A method to stabilise the performance of negatively fed KM3NeT photomultipliers. J. Instrum., 11, P12014–12pp.
Abstract: The KM3NeT research infrastructure, currently under construction in the Mediterranean Sea, will host neutrino telescopes for the identification of neutrino sources in the Universe and for studies of the neutrino mass hierarchy. These telescopes will house hundreds of thousands of photomultiplier tubes that will have to be operated in a stable and reliable fashion. In this context, the stability of the dark counts has been investigated for photomultiplier tubes with negative high voltage on the photocathode and held in insulating support structures made of 3D printed nylon material. Small gaps between the rigid support structure and the photomultiplier tubes in the presence of electric fields can lead to discharges that produce dark count rates that are highly variable. A solution was found by applying the same insulating varnish as used for the high voltage bases directly to the outside of the photomultiplier tubes. This transparent conformal coating provides a convenient and inexpensive method of insulation.
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KM3NeT Collaboration(Aiello, S. et al), Alves Garre, S., Calvo, D., Carretero, V., Colomer, M., Corredoira, I., et al. (2020). Event reconstruction for KM3NeT/ORCA using convolutional neural networks. J. Instrum., 15(10), P10005–39pp.
Abstract: The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches.
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KM3NeT Collaboration(Aiello, S. et al), Calvo, D., Coleiro, A., Colomer, M., Gozzini, S. R., Hernandez-Rey, J. J., et al. (2020). The Control Unit of the KM3NeT Data Acquisition System. Comput. Phys. Commun., 256, 107433–16pp.
Abstract: The KM3NeT Collaboration runs a multi-site neutrino observatory in the Mediterranean Sea. Water Cherenkov particle detectors, deep in the sea and far off the coasts of France and Italy, are already taking data while incremental construction progresses. Data Acquisition Control software is operating off-shore detectors as well as testing and qualification stations for their components. The software, named Control Unit, is highly modular. It can undergo upgrades and reconfiguration with the acquisition running. Interplay with the central database of the Collaboration is obtained in a way that allows for data taking even if Internet links fail. In order to simplify the management of computing resources in the long term, and to cope with possible hardware failures of one or more computers, the KM3NeT Control Unit software features a custom dynamic resource provisioning and failover technology, which is especially important for ensuring continuity in case of rare transient events in multi-messenger astronomy. The software architecture relies on ubiquitous tools and broadly adopted technologies and has been successfully tested on several operating systems.
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LHCb Collaboration(Aaij, R. et al), Martinez-Vidal, F., Oyanguren, A., Ruiz Valls, P., & Sanchez Mayordomo, C. (2014). Study of the kinematic dependences of Lambda(0)(b) production in pp collisions and a measurement of the Lambda(0)(b) -> Lambda(+)(c)pi(-) branching fraction. J. High Energy Phys., 08(8), 143–19pp.
Abstract: The kinematic dependences of the relative production rates, f(Lambda b)(0)/f(d), of Lambda(0)(b) baryons and B-0 mesons are measured using Lambda(0)(b) -> Lambda(+)(c)pi(-) and (B) over bar (0) -> D+pi(-) decays. The measurements use proton-proton collision data, corresponding to an integrated luminosity of 1 fb(-1) at a centre-of-mass energy of 7 TeV, recorded in the forward region with the LHCb experiment. The relative production rates are observed to depend on the transverse momentum, pT, and pseudorapidity, eta, of the beauty hadron, in the studied kinematic region 1.5 < pT < 40 GeV/c and 2 < eta < 5. Using a previous LHCb measurement of f(Lambda b)(0)/f(d) in semileptonic decays, the branching fraction B (Lambda(0)(b) -> Lambda(+)(c)pi(-)) = (4.30 +/- 0.03(-0.11)(+0.12)+/- 0.26 +/- 0.21) x 10(-3) is obtained, where the first uncertainty is statistical, the second is systematic, the third is from the previous LHCb measurement of f(Lambda b)(0)/f(d) and the fourth is due to the (B) over bar (0) -> D+pi(-) branching fraction. This is the most precise measurement of a Lambda(0)(b) branching fraction to date.
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Candido, A., Garcia, A., Magni, G., Rabemananjara, T., Rojo, J., & Stegeman, R. (2023). Neutrino structure functions from GeV to EeV energies. J. High Energy Phys., 05(5), 149–78pp.
Abstract: The interpretation of present and future neutrino experiments requires accurate theoretical predictions for neutrino-nucleus scattering rates. Neutrino structure functions can be reliably evaluated in the deep-inelastic scattering regime within the perturbative QCD (pQCD) framework. At low momentum transfers (Q(2) less than or similar to few GeV2), inelastic structure functions are however affected by large uncertainties which distort event rate predictions for neutrino energies E-nu up to the TeV scale. Here we present a determination of neutrino inelastic structure functions valid for the complete range of energies relevant for phenomenology, from the GeV region entering oscillation analyses to the multi-EeV region accessible at neutrino telescopes. Our NNSF nu approach combines a machine-learning parametrisation of experimental data with pQCD calculations based on state-of-the-art analyses of proton and nuclear parton distributions (PDFs). We compare our determination to other calculations, in particular to the popular Bodek-Yang model. We provide updated predictions for inclusive cross sections for a range of energies and target nuclei, including those relevant for LHC far-forward neutrino experiments such as FASER nu, SND@LHC, and the Forward Physics Facility. The NNSF nu determination is made available as fast interpolation LHAPDF grids, and it can be accessed both through an independent driver code and directly interfaced to neutrino event generators such as GENIE.
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