Stochastic reserving with a stacked model based on a hybridized Artificial Neural Network

Currently, legal requirements demand that insurance companies increase their emphasis on monitoring the risks linked to the underwriting and asset management activities. Regarding underwriting risks, the main uncertainties that insurers must manage are related to the premium sufficiency to cover future claims and the adequacy of the current reserves to pay outstanding claims. Both risks are calibrated using stochastic models due to their nature. This paper introduces a reserving model based on a set of machine learning techniques such as Gradient Boosting, Random Forest and Artificial Neural Networks. These algorithms and other widely used reserving models are stacked to predict the shape of the runoff. To compute the deviation around a former prediction, a log-normal approach is combined with the suggested model. The empirical results demonstrate that the proposed methodology can be used to improve the performance of the traditional reserving techniques based on Bayesian statistics and a Chain Ladder, leading to a more accurate assessment of the reserving risk

Chemical enrichment and star formation in the Milky Way disk III. Chemodynamical constraints

In this paper, we investigate some chemokinematical properties of the Milky Way disk, by using a sample composed by 424 late-type dwarfs. We show that the velocity dispersion of a stellar group correlates with the age of this group, according to a law proportional to t^0.26, where t is the age of the stellar group. The temporal evolution of the vertex deviation is considered in detail. It is shown that the vertex deviation does not seem to depend strongly on the age of the stellar group. Previous studies in the literature seem to not have found it due to the use of statistical ages for stellar groups, rather than individual ages. The possibility to use the orbital parameters of a star to derive information about its birthplace is investigated, and we show that the mean galactocentric radius is likely to be the most reliable stellar birthplace indicator. However, this information cannot be presently used to derive radial evolutionary constraints, due to an intrinsic bias present in all samples constructed from nearby stars. An extensive discussion of the secular and stochastic heating mechanisms commonly invoked to explain the age-velocity dispersion relation is presented. We suggest that the age-velocity dispersion relation could reflect the gradual decrease in the turbulent velocity dispersion from which disk stars form, a suggestion originally made by Tinsley and Larson (1978) and supported by several more recent disk evolution calculations. A test to distinguish between the two types of models using high-redshift galaxies is proposed.Comment: 20 pages, 10 encapsulated postscript figures, LaTeX, uses Astronomy and Astrophysics macro aa.cls, graphicx package, to be published in Astronomy and Astrophysics (2004), Also available at: http://www.astro.iag.usp.br/~macie

Research priorities for maintaining biodiversity’s contributions to people in Latin America

Maintaining biodiversity is crucial for ensuring human well-being. The authors participated in a workshop held in Palenque, Mexico, in August 2018, that brought together 30 mostly early-career scientists working in different disciplines (natural, social and economic sciences) with the aim of identifying research priorities for studying the contributions of biodiversity to people and how these contributions might be impacted by environmental change. Five main groups of questions emerged: (1) Enhancing the quantity, quality, and availability of biodiversity data; (2) Integrating different knowledge systems; (3) Improved methods for integrating diverse data; (4) Fundamental questions in ecology and evolution; and (5) Multi-level governance across boundaries. We discuss the need for increased capacity building and investment in research programmes to address these challenges

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

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 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

Calibration of the underground muon detector of the Pierre Auger Observatory

To obtain direct measurements of the muon content of extensive air showers with energy above 10$^{16.5}$ eV, the Pierre Auger Observatory is currently being equipped with an underground muon detector (UMD), consisting of 219 10 m$^{2}$-modules, each segmented into 64 scintillators coupled to silicon photomultipliers (SiPMs). Direct access to the shower muon content allows for the study of both of the composition of primary cosmic rays and of high-energy hadronic interactions in the forward direction. As the muon density can vary between tens of muons per m2 close to the intersection of the shower axis with the ground to much less than one per m2 when far away, the necessary broad dynamic range is achieved by the simultaneous implementation of two acquisition modes in the read-out electronics: the binary mode, tuned to count single muons, and the ADC mode, suited to measure a high number of them. In this work, we present the end-to-end calibration of the muon detector modules: first, the SiPMs are calibrated by means of the binary channel, and then, the ADC channel is calibrated using atmospheric muons, detected in parallel to the shower data acquisition. The laboratory and field measurements performed to develop the implementation of the full calibration chain of both binary and ADC channels are presented and discussed. The calibration procedure is reliable to work with the high amount of channels in the UMD, which will be operated continuously, in changing environmental conditions, for several years

A Catalog of the Highest-energy Cosmic Rays Recorded during Phase I of Operation of the Pierre Auger Observatory

A catalog containing details of the highest-energy cosmic rays recorded through the detection of extensive air-showers at the Pierre Auger Observatory is presented with the aim of opening the data to detailed examination. Descriptions of the 100 showers created by the highest-energy particles recorded between 1 January 2004 and 31 December 2020 are given for cosmic rays that have energies in the range 78 EeV to 166 EeV. Details are also given of a further nine very-energetic events that have been used in the calibration procedure adopted to determine the energy of each primary. A sky plot of the arrival directions of the most energetic particles is shown. No interpretations of the data are offered