The Large Hadron Collider at CERN is designed to collide proton bunches at 40 MHz in the ATLAS detector. This event rate must be reduced to about 200 Hz for storage by selecting the most interesting events for physics analysis. This will be achieved in ATLAS with a three level trigger system. This work proposes the application of the Nonlinear Independent Component Analysis model in the ATLAS second-level trigger to estimate underlying factors of calorimeter information. As a pre-processing step, the calorimeter Region of Interest data are transformed into concentric energy deposition rings. In order to take advantage of the full detector segmentation, the feature extraction procedure is performed at calorimeter layer level. The independent features estimated from each layer were concatenated into a single feature vector, which was used to feed the input nodes of a neural network classifier for highly-efficient electron identification. The proposed approach achieved 97.0% of electron identification for 7.7% of background noise (QCD Jets) acceptance.

Low power quality may cause serious problems in industrial, corporative and residential electrical networks. Among the most common problems one can mention the reduction of equipment lifetime, false activation of protection devices and electrical and thermal losses increase. Considering this, it is very important to monitor the power quality of a given facility. This paper describes the architecture of custom application-specific integrated circuit (ASIC) for power quality measurement. The digital signal processing measurements are based on IEC standards (61000-4-30, 61000-4-7, and 61000-4-15 class-A). The proposed Integrated Circuit (IC) supports poly-phase measuring for 7 channels with high precision estimation of parameters such as RMS value, crest factor, harmonics, interharmonics, total harmonics distortion, angle, unbalance, active power, apparent power, and instantaneous frequency. There is also a Real Time Clock (RTC) that enables system synchronization, predicted in the standards to perform phasor measurement. The main focus of this paper is describing the digital signal processing blocks of the proposed IC. The designed ASIC was produced using TowerJazz 180nm CMOS technology. Multiple clocks are used to reduce area (by optimizing single port memories), enable faster external communication, and reduce power consumption.This full text paper was peer-reviewed at the direction of IEEE Instrumentation and Measurement Society prior to the acceptance and publication.978-1-4799-6144-6/15/$31.00 ©2015 IEEE

The ATLAS online trigger system has three filtering levels and relies very much on calorimeter information, which is segmented into seven detection layers. Due to differences both in depth and cell granularity of these layers, trigger algorithms may benefit from performing feature extraction at the layer level. This work addresses electron/jet separation at the second level (LVL2) filtering restricted to calorimeter data. Segmented Independent Component Analysis (SICA) is applied over the calorimeter layers in order to extract relevant features for particle identification. The number of independent components to be extracted from a Region of Interest (RoI) is estimated through different signal compaction strategies, such as Principal Component Analysis, Nonlinear Principal Component Analysis and Principal Components for Discrimination. These compaction techniques are evaluated with respect to dimensionality reduction (and processing speed) and classification efficiency. The hypothesis testing is performed by a Multi-Layer Perceptron classifier fed from the segmented independent components. It is shown that the proposed discriminators outperform the baseline design for ATLAS second-level trigger system, achieving a detection efficiency of 99% for a rejection factor smaller than 2%.

Resumo O experimento ν-Angra tem como objetivo construir um dispositivo de detecção de antineutrinos capaz de monitorar a atividade do reator nuclear de Angra dos Reis. O sistema proposto considera um detector operando em superfície, o que faz o mesmo ficar exposto a uma alta taxa de ruído de fundo, principalmente devido a raios cósmicos. Assim sendo, o sistema de veto tem um papel fundamental na viabilidade do experimento. Este artigo propõe uma metodologia inovadora para supervisão da variação do ganho das fotomultiplicadoras no sistema de veto, durante sua operação, baseado no reconhecimento de padrões de raios cósmicos de múons, utilizando redes neurais artificiais.

A correta identificação de partículas é um dos principais objetivos de experimentos de física de altas energias. Devido a alta taxa de eventos no Grande Colisor de Hádrons (LHC), o experimento ATLAS tem empregado técnicas baseadas em aprendizado de máquina a fim de encontrar eventos raros em grandes massas de dados. Entre eles está o NeuralRinger, um conjunto classificador de rede neural projetado para detecção rápida de elétrons com base em anéis concêntricos de calorímetro (sistema de medição de energia). Nesse contexto, o presente trabalho propõe a adaptação desta técnica, que opera identificando elétrons, para a detecção rápida de fótons. Os resultados com dados simulados mostram a eficiência do método proposto no ambiente do experimento ATLAS.