Políticas públicas y estrategias institucionales para el desarrollo e implementación de energías renovables en Argentina (2006-2016)

El objetivo de este trabajo es analizar las principales políticas de promoción para el desarrollo e adopción de las ER implementadas en Argentina en los últimos 10 años. Para ello se propone un abordaje teórico-metodológico socio-técnico que supere las visiones tradicionales estrictamente técnicas o económicas. Para ello se presentan, en primer lugar, la propuesta teóricometodológica analizar las políticas y estrategias institucionales impulsadas en Argentina para el desarrollo e implementación de ER. A continuación se presenta un resumen de las diferentes experiencias, proyectos y políticas en Argentina a escala nacional, provincial y nacional-provincial, y finalmente se concluye con algunas observaciones acerca del proceso de construcción de funcionamiento de las mismas.The aim of this paper is analyze the public policies to promote the development and adoption of renewable energies implemented in Argentina in the last 10 years. This requires a theoretical and methodological approach that overcomes traditional views strictly technical or economical. In this work we show, in first place, the theoretical and methodological proposal to analyze the policies and strategies driven in Argentina for the development and implementation of renewable energies. Secondly, a summary of different experiences, projects and policies in Argentina at the national, provincial and national-provincial level is presented, and finally we concludes with some observations about the working/no working construction processes.Tema 12: Aspectos socioculturales y socio-económicos de la transferencia de tecnología en energías renovables. Experiencias. Metodologías.Facultad de Arquitectura y Urbanism

Políticas públicas y estrategias institucionales para el desarrollo e implementación de energías renovables en Argentina (2006-2016)

El objetivo de este trabajo es analizar las principales políticas de promoción para el desarrollo e adopción de las ER implementadas en Argentina en los últimos 10 años. Para ello se propone un abordaje teórico-metodológico socio-técnico que supere las visiones tradicionales estrictamente técnicas o económicas. Para ello se presentan, en primer lugar, la propuesta teóricometodológica analizar las políticas y estrategias institucionales impulsadas en Argentina para el desarrollo e implementación de ER. A continuación se presenta un resumen de las diferentes experiencias, proyectos y políticas en Argentina a escala nacional, provincial y nacional-provincial, y finalmente se concluye con algunas observaciones acerca del proceso de construcción de funcionamiento de las mismas.The aim of this paper is analyze the public policies to promote the development and adoption of renewable energies implemented in Argentina in the last 10 years. This requires a theoretical and methodological approach that overcomes traditional views strictly technical or economical. In this work we show, in first place, the theoretical and methodological proposal to analyze the policies and strategies driven in Argentina for the development and implementation of renewable energies. Secondly, a summary of different experiences, projects and policies in Argentina at the national, provincial and national-provincial level is presented, and finally we concludes with some observations about the working/no working construction processes.Tema 12: Aspectos socioculturales y socio-económicos de la transferencia de tecnología en energías renovables. Experiencias. Metodologías.Facultad de Arquitectura y Urbanism

Design, upgrade and characterization of the silicon photomultiplier front-end for the AMIGA detector at the Pierre Auger Observatory

AMIGA (Auger Muons and Infill for the Ground Array) is an upgrade of the Pierre Auger Observatory to complement the study of ultra-high-energy cosmic rays (UHECR) by measuring the muon content of extensive air showers (EAS). It consists of an array of 61 water Cherenkov detectors on a denser spacing in combination with underground scintillation detectors used for muon density measurement. Each detector is composed of three scintillation modules, with 10 m$^2$ detection area per module, buried at 2.3 m depth, resulting in a total detection area of 30 m$^2$. Silicon photomultiplier sensors (SiPM) measure the amount of scintillation light generated by charged particles traversing the modules. In this paper, the design of the front-end electronics to process the signals of those SiPMs and test results from the laboratory and from the Pierre Auger Observatory are described. Compared to our previous prototype, the new electronics shows a higher performance, higher efficiency and lower power consumption, and it has a new acquisition system with increased dynamic range that allows measurements closer to the shower core. The new acquisition system is based on the measurement of the total charge signal that the muonic component of the cosmic ray shower generates in the detector.Comment: 40 pages, 33 figure

Extraction of the Muon Signals Recorded with the Surface Detector of the Pierre Auger Observatory Using Recurrent Neural Networks

The Pierre Auger Observatory, at present the largest cosmic-ray observatory ever built, is instrumented with a ground array of 1600 water-Cherenkov detectors, known as the Surface Detector (SD). The SD samples the secondary particle content (mostly photons, electrons, positrons and muons) of extensive air showers initiated by cosmic rays with energies ranging from $10^{17}~$eV up to more than $10^{20}~$eV. Measuring the independent contribution of the muon component to the total registered signal is crucial to enhance the capability of the Observatory to estimate the mass of the cosmic rays on an event-by-event basis. However, with the current design of the SD, it is difficult to straightforwardly separate the contributions of muons to the SD time traces from those of photons, electrons and positrons. In this paper, we present a method aimed at extracting the muon component of the time traces registered with each individual detector of the SD using Recurrent Neural Networks. We derive the performances of the method by training the neural network on simulations, in which the muon and the electromagnetic components of the traces are known. We conclude this work showing the performance of this method on experimental data of the Pierre Auger Observatory. We find that our predictions agree with the parameterizations obtained by the AGASA collaboration to describe the lateral distributions of the electromagnetic and muonic components of extensive air showers.Comment: 23 pages, 15 figures. Version accepted for publication in JINS

Deep-learning based reconstruction of the shower maximum Xmax using the water-Cherenkov detectors of the Pierre Auger Observatory

The atmospheric depth of the air shower maximum Xmax is an observable commonly used for the determination of the nuclear mass composition of ultra-high energy cosmic rays. Direct measurements of Xmax are performed using observations of the longitudinal shower development with fluorescence telescopes. At the same time, several methods have been proposed for an indirect estimation of Xmax from the characteristics of the shower particles registered with surface detector arrays. In this paper, we present a deep neural network (DNN) for the estimation of Xmax. The reconstruction relies on the signals induced by shower particles in the ground based water-Cherenkov detectors of the Pierre Auger Observatory. The network architecture features recurrent long short-term memory layers to process the temporal structure of signals and hexagonal convolutions to exploit the symmetry of the surface detector array. We evaluate the performance of the network using air showers simulated with three different hadronic interaction models. Thereafter, we account for long-term detector effects and calibrate the reconstructed Xmax using fluorescence measurements. Finally, we show that the event-by-event resolution in the reconstruction of the shower maximum improves with increasing shower energy and reaches less than 25 g/cm2 at energies above 2×1019 eV

Deep-Learning based Reconstruction of the Shower Maximum XmaxX_{\mathrm{max}} using the Water-Cherenkov Detectors of the Pierre Auger Observatory

The atmospheric depth of the air shower maximum $X_{\mathrm{max}}$ is an observable commonly used for the determination of the nuclear mass composition of ultra-high energy cosmic rays. Direct measurements of $X_{\mathrm{max}}$ are performed using observations of the longitudinal shower development with fluorescence telescopes. At the same time, several methods have been proposed for an indirect estimation of $X_{\mathrm{max}}$ from the characteristics of the shower particles registered with surface detector arrays. In this paper, we present a deep neural network (DNN) for the estimation of $X_{\mathrm{max}}$. The reconstruction relies on the signals induced by shower particles in the ground based water-Cherenkov detectors of the Pierre Auger Observatory. The network architecture features recurrent long short-term memory layers to process the temporal structure of signals and hexagonal convolutions to exploit the symmetry of the surface detector array. We evaluate the performance of the network using air showers simulated with three different hadronic interaction models. Thereafter, we account for long-term detector effects and calibrate the reconstructed $X_{\mathrm{max}}$ using fluorescence measurements. Finally, we show that the event-by-event resolution in the reconstruction of the shower maximum improves with increasing shower energy and reaches less than $25~\mathrm{g/cm^{2}}$ at energies above $2\times 10^{19}~\mathrm{eV}$.Comment: Published version, 29 pages, 12 figure

Expected performance of the AugerPrime Radio Detector

The AugerPrime Radio Detector will significantly increase the sky coverage of mass-sensitive measurements of ultra-high energy cosmic rays with the Pierre Auger Observatory. The detection of highly inclined air showers with the world’s largest 3000 km2 radio-antenna array in coincidence with the Auger water-Cherenkov detector provides a clean separation of the electromagnetic and muonic shower components. The combination of these highly complementary measurements yields a strong sensitivity to the mass composition of cosmic rays. We will present the first results of an end-to-end simulation study of the performance of the AugerPrime Radio Detector. The study features a complete description of the AugerPrime radio antennas and reconstruction of the properties of inclined air showers, in particular the electromagnetic energy. The performance is evaluated utilizing a comprehensive set of simulated air showers together with recorded background. The estimation of an energy- and direction-dependent aperture yields an estimation of the expected 10-year event statistics. The potential to measure the number of muons in air showers with the achieved statistics is outlined. Based on the achieved energy resolution, the potential to discriminate between different cosmic-ray primaries is presented

Design and implementation of the AMIGA embedded system for data acquisition

The Auger Muon Infill Ground Array (AMIGA) is part of the AugerPrime upgrade of the Pierre Auger Observatory. It consists of particle counters buried 2.3 m underground next to the water-Cherenkov stations that form the 23.5 km$^2$ large infilled array. The reduced distance between detectors in this denser area allows the lowering of the energy threshold for primary cosmic ray reconstruction down to about 10$^{17}$ eV. At the depth of 2.3 m the electromagnetic component of cosmic ray showers is almost entirely absorbed so that the buried scintillators provide an independent and direct measurement of the air showers muon content. This work describes the design and implementation of the AMIGA embedded system, which provides centralized control, data acquisition and environment monitoring to its detectors. The presented system was firstly tested in the engineering array phase ended in 2017, and lately selected as the final design to be installed in all new detectors of the production phase. The system was proven to be robust and reliable and has worked in a stable manner since its first deployment.Comment: Accepted for publication at JINST. Published version, 34 pages, 15 figures, 4 table