Measurement of atmospheric production depths of muons with the pierre auger observatory

ISBN: volume 1: 978-2-7598-1025-3; volume 2: 978-2-7598-1026-0International audienceThe time structure of muons at ground retains valuable information about the longitudinal development of the hadronic component in extensive air showers. Using the signals collected by the surface detector array of the Pierre Auger Observatory it is possible to reconstruct the Muon Production Depth (MPD) distribution. In this work we explore the main features of these reconstructions for zenith angles around 60° and different energies of the primary particle. From the MPDs we define a new observable, Xμmax as the depth, along the shower axis, where the maximum number of muons is produced. The potentiality of Xμmax to infer the mass composition of cosmic rays is studied

Studies of hadronic interactions at ultra-high energies with the Pierre Auger Observatory

International audienc

Studying the nuclear mass composition of Ultra-High Energy Cosmic Rays with the Pierre Auger Observatory

The Fluorescence Detector of the Pierre Auger Observatory measures the atmospheric depth, $X_{max}$, where the longitudinal profile of the high energy air showers reaches its maximum. This is sensitive to the nuclear mass composition of the cosmic rays. Due to its hybrid design, the Pierre Auger Observatory also provides independent experimental observables obtained from the Surface Detector for the study of the nuclear mass composition. We present $X_{max}$-distributions and an update of the average and RMS values in different energy bins and compare them to the predictions for different nuclear masses of the primary particles and hadronic interaction models. We also present the results of the composition-sensitive parameters derived from the ground level component.Comment: Proceedings of the 12th International Conference on Topics in Astroparticle and Underground Physics, TAUP 2011, Munich, German

Predicting Transport Effects of Scintillation Light Signals in Large-Scale Liquid Argon Detectors

Liquid argon is being employed as a detector medium in neutrino physics and Dark Matter searches. A recent push to expand the applications of scintillation light in Liquid Argon Time Projection Chamber neutrino detectors has necessitated the development of advanced methods of simulating this light. The presently available methods tend to be prohibitively slow or imprecise due to the combination of detector size and the amount of energy deposited by neutrino beam interactions. In this work we present a semi-analytical model to predict the quantity of argon scintillation light observed by a light detector with a precision better than $10\%$, based only on the relative positions between the scintillation and light detector. We also provide a method to predict the distribution of arrival times of these photons accounting for propagation effects. Additionally, we present an equivalent model to predict the number of photons and their arrival times in the case of a wavelength-shifting, highly-reflective layer being present on the detector cathode. Our proposed method can be used to simulate light propagation in large-scale liquid argon detectors such as DUNE or SBND, and could also be applied to other detector mediums such as liquid xenon or xenon-doped liquid argon.Comment: 23 pages, 27 figures, Accepted by EPJ

Reflections on the Implementation of Tidal Energy in Ecuador

Renewable energy is a topic frequently discussed due to the need to change the forms of generation, from the centralized to the distributed form and take advantage of the potentials that are scattered in the territory and use local resources and thereby diversify the schemes of distributed generation that allows the man in his daily work to pass from consumption of energy to generator, in this way the environmental impacts are reduced that today accelerate the change of temperature in the planet, noticing in recent years the oil and its derivatives are responsible for this phenomenon. The objective of the research is to reflect on tidal energy, knowing that the province of Manabí, is the one that has the largest coastal area and where there is a potential that can be studied for future use

A Novel Electrical Method to Measure Wire Tensions for Time Projection Chambers

We present a novel electrical technique to measure the tension of wires in multi-wire drift chambers. We create alternating electric fields by biasing adjacent wires on both sides of a test wire with a superposition of positive and negative DC voltages on an AC signal ($V_{\rm AC} \pm V_{\rm DC}$). The resulting oscillations of the wire will display a resonance at its natural frequency, and the corresponding change of the capacitance will lead to a measurable current. This scheme is scalable to multiple wires and therefore enables us to precisely measure the tension of a large number of wires in a short time. This technique can also be applied at cryogenic temperatures making it an attractive solution for future large time-projection chambers such as the DUNE detector. We present the concept, an example implementation and its performance in a real-world scenario and discuss the limitations of the sensitivity of the system in terms of voltage and wire length.Comment: 7 pages, 8 figures. Accepted by NIM

Comparison of Smartphone Pedometer Apps on a Treadmill versus Outdoors

Previous research has focused on the accuracy of smartphone pedometer apps in laboratory settings, however less information is available in outdoor (free living) environments. PURPOSE: Determine the accuracy of 5 smartphone apps at recording steps at a walking speed in a laboratory versus an outdoor setting. METHODS: Twenty-three healthy college students consented (Mean±SD; 22±3.8yrs; BMI 24.9±4.13kg/m2) to participate in 2 separate visits. During the first visit participants walked 500 steps at 3mph on a treadmill while wearing a pedometer and a smartphone placed in the pocket using 5 pedometer apps concurrently (Moves, Google Fit (G-Fit), Runtastic, Accupedo, S-Health). During the second visit, participants walked 400 meters at 3mph on a sidewalk outside. Actual steps for each visit were recorded using a hand tally counter device. Zero and negative values were replaced with the mean value for that trial. Statistical analyses were performed using IBM SPSS 23.0. Mean bias scores were calculated between the step count for each app and the respective tally count for each trial. Mean bias scores were correlated between trials for each app using Pearson correlations and significance was set at p\u3c0.05. Mean Absolute Percent Error (MAPE) values were also calculated for each app for both trials. RESULTS: G-Fit recorded 2 zero values and 2 negative values and Moves recorded 1 zero value. Mean bias scores were significantly correlated between the indoor and outdoor protocols for the pedometer (r=0.67, p\u3c0.01) and S-Health (r=0.46, p\u3c0.5). The remaining apps were not correlated between protocols. The outdoor protocol producing a greater mean bias for the outdoor protocol for G-Fit, Runtastic, and Accupedo (mean bias ± SD indoor, outdoor; -4.3±53.1, -19.3±120.0; -10.7±63.3, -33.4±118.7; 16.0±143.6, 79.0±75.0; respectively) and a greater mean bias for the indoor protocol for the pedometer, Moves, and S-Health (mean bias indoor, outdoor; -1.4±41.5, 0.0±34.1; -117.4±196.7, -42.2±209.6; 11.3±28.4, 0.0±58.7; respectively). MAPE was below 5% for the pedometer and S-Health for both trials. CONCLUSION: Apps with the lowest error in a controlled setting may be less affected when used in other settings, while apps with greater variation in a controlled setting may be affected when used in a different environment

The Pandora multi-algorithm approach to automated pattern recognition of cosmic-ray muon and neutrino events in the MicroBooNE detector.

The development and operation of liquid-argon time-projection chambers for neutrino physics has created a need for new approaches to pattern recognition in order to fully exploit the imaging capabilities offered by this technology. Whereas the human brain can excel at identifying features in the recorded events, it is a significant challenge to develop an automated, algorithmic solution. The Pandora Software Development Kit provides functionality to aid the design and implementation of pattern-recognition algorithms. It promotes the use of a multi-algorithm approach to pattern recognition, in which individual algorithms each address a specific task in a particular topology. Many tens of algorithms then carefully build up a picture of the event and, together, provide a robust automated pattern-recognition solution. This paper describes details of the chain of over one hundred Pandora algorithms and tools used to reconstruct cosmic-ray muon and neutrino events in the MicroBooNE detector. Metrics that assess the current pattern-recognition performance are presented for simulated MicroBooNE events, using a selection of final-state event topologies

Calibration of the charge and energy loss per unit length of the MicroBooNE liquid argon time projection chamber using muons and protons

We describe a method used to calibrate the position- and time-dependent response of the MicroBooNE liquid argon time projection chamber anode wires to ionization particle energy loss. The method makes use of crossing cosmic-ray muons to partially correct anode wire signals for multiple effects as a function of time and position, including cross-connected TPC wires, space charge effects, electron attachment to impurities, diffusion, and recombination. The overall energy scale is then determined using fully-contained beam-induced muons originating and stopping in the active region of the detector. Using this method, we obtain an absolute energy scale uncertainty of 2% in data. We use stopping protons to further refine the relation between the measured charge and the energy loss for highly-ionizing particles. This data-driven detector calibration improves both the measurement of total deposited energy and particle identification based on energy loss per unit length as a function of residual range. As an example, the proton selection efficiency is increased by 2% after detector calibration