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

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

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

Characterization of Patients with Chronic Diseases and Complex Care Needs: A New High-Risk Emergent Population

Background: To analyze the prevalence and main epidemiological, clinical and outcome features of in-Patients with Complex Chronic conditions (PCC) in internal medicine areas, using a pragmatic working definition. Methods: Prospective study in 17 centers from Spain, with 97 in-hospital, monthly prevalence cuts. A PCC was considered when criteria of polypathological patient (two or more major chronic diseases) were met, or when a patient suffered one major chronic disease plus one or more of nine predefined complexity criteria like socio-familial risk, alcoholism or malnutrition among others (PCC without polypathology). A complete set of baseline features as well as 12-months survival were collected. Then, we compared clinical, outcome variables, and PROFUND index accuracy between polypathological patients and PCC without polypathology. Results: The global prevalence of PCC was 61% (40% of them were polypathological patients, and 21% PCC withouth polypathology) out of the 2178 evaluated patients. Their median age was 82 (59.5% men), suffered 2.3 ± 1.1 major diseases (heart diseases (70.5%), neurologic (41.5%), renal (36%), and lung diseases (26%)), 5.5 ± 2.5 other chronic conditions, met 2.5 ± 1.5 complexity criteria, and presented functional decline (Barthel index 55 (25-90)). Compared to polypathological patients, the subgroup of PCC without polypathology were younger, with a different pattern of major diseases and comorbidities, a better functional status, and lower 12-months mortality rates ((36.2% vs 46.8%; p = .003; OR 0.7(0.48-0.86). The PROFUND index obtained adequate calibration and discrimination power (AUC-ROC 0.67 (0.63-0.69)) in predicting 12-month mortality of PCC. Conclusion: Patients with complex chronic conditions are highly prevalent in internal medicine areas; their clinical pattern has changed in parallel to socio-epidemiological modifications, but their death-risk is still adequately predicted by PROFUND index

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

Reconstruction and measurement of (100) MeV energy electromagnetic activity from π0 arrow γγ decays in the MicroBooNE LArTPC

We present results on the reconstruction of electromagnetic (EM) activity from photons produced in charged current νμ interactions with final state π0s. We employ a fully-automated reconstruction chain capable of identifying EM showers of (100) MeV energy, relying on a combination of traditional reconstruction techniques together with novel machine-learning approaches. These studies demonstrate good energy resolution, and good agreement between data and simulation, relying on the reconstructed invariant π0 mass and other photon distributions for validation. The reconstruction techniques developed are applied to a selection of νμ + Ar → μ + π0 + X candidate events to demonstrate the potential for calorimetric separation of photons from electrons and reconstruction of π0 kinematics