The LHCb RICH PMTs Readout Electronics and the Monitoring of the HPDs Quantum Efficiency

LHCb is one of the four main experiments under construction on the Large Hadron Collider at CERN. Its purpose is to study CP violation in B mesons and to look for new physics effects in rare decays of b-hadrons. Particle identification will be essential to enhance the signal/background ratio in the selection of physics channels. For this reason, the Ring Imaging Cherenkov technique has been implemented: two RICH detectors (RICH1 and RICH2) have been designed to cover the wide momentum range 1-150 GeV/c. The produced Cherenkov photons will be focused on two planes of Hybrid PhotoDetectors (HPDs), which are sensitive to external magnetic fields and then need to be shielded. Despite the shielding, however, there will be some fringe field inside the HPDs volume and so it is necessary to experimentally check what is the behaviour of each photodetector when the LHCb dipole magnet is on and the HPDs are illuminated by test patterns. In RICH2, two LED projectors based on the Digital Light Processing technology are exploited to generate the test patterns, which have to be precisely aligned on the two HPD planes. The matching procedure is carried out using six PMTs permanently placed inside the HPD matrices. The work described in this thesis concerns the design, realization and test of the PMTs readout system, both on the HW and SW level. In the last chapter, I will also try to evaluate the possibility to periodically monitor the HPDs Q.E. using the same beamer selected for the magnetic distortion tests. Chapter 1 is an introduction to CERN and the LHCb experiment. Paragraph 1.2 focuses on the two RICH sub-systems, while in 1.3 the HPD working principle is described. In paragraph 2.1 I describe the PMTs installed in the RICH2, while the rest of the chapter is dedicated to the DLP projectors to be used during the magnetic distortion tests. In particular, 2.2 illustrates the DLP technology, while 2.4 and 2.5 are about the beamers tests. Chapter 3 is dedicated to the PMTs readout electronics design, realization and test. After a theoretical study carried out in paragraph 3.1, in 3.2 I describe the realized shaper amplifier prototype. In 3.3 the choice of the digitiser to be installed in cascade to the shaper is discussed and the DAQ software program is described, while 3.4 summarizes the results obtained testing the prototype with the real signals. In 3.5 the final six-channel shaper amplifier + ADC is presented and tested, while 3.6 describes the installation of this module in the pit environment. Finally, in chapter 4 I estimate the sensitivity of the HPD Q.E. monitoring based on the magnetic distortion test apparatus

Anomalous Weak Values and the Violation of a Multiple-measurement Leggett-Garg Inequality

Quantum mechanics presents peculiar properties that, on the one hand, have been the subject of several theoretical and experimental studies about its very foundations and, on the other hand, provide tools for developing new technologies, the so-called quantum technologies. The nonclassicality pointed out by Leggett-Garg inequalities has represented, with Bell inequalities, one of the most investigated subject. In this letter we study the connection of Leggett-Garg inequalities with a new emerging field of quantum measurement, the weak values. In particular, we perform an experimental study of the four-time correlators Legget-Garg test, by exploiting single and sequential weak measurements performed on heralded single photons. We show violation of a four-parameters Leggett-Garg inequality in different experimental conditions, demonstrating an interesting connection between Leggett-Garg inequality violation and anomalous weak values

A comparison of three learning methods to predict N2O fluxes and N leaching

International audienceThe environmental costs of intensive farming activities are often under-estimated or not included into rural development plans, even though they play an important role in addressing future society's needs. This paper focuses on the use of statistical learning methods to predict N2O emissions and N leaching under several conservative scenarios, in order to provide an alternative approach to deterministic models on a macro-scale. To that aim, three learning methods, namely neural networks (multilayer perceptrons), SVM and random forests, are compared and provide accurate solutions

A comparison of eight metamodeling techniques for the simulation of N2O fluxes and N leaching from corn crops

International audienceThe environmental costs of intensive farming activities are often under-estimated or not traded by the market, even though they play an important role in addressing future society's needs. The estimation of nitrogen (N) dynamics is thus an important issue which demands detailed simulation based methods and their integrated use to correctly represent complex and non-linear interactions into cropping systems. To calculate the N2O flux and N leaching from European arable lands, a modeling framework has been developed by linking the CAPRI agro-economic dataset with the DNDC-EUROPE bio-geo-chemical model. But, despite the great power of modern calculators, their use at continental scale is often too computationally costly. By comparing several statistical methods this paper aims to design a metamodel able to approximate the expensive code of the detailed modeling approach, devising the best compromise between estimation performance and simulation speed. We describe the use of two parametric (linear) models and six non-parametric approaches: two methods based on splines (ACOSSO and SDR), one method based on kriging (DACE), a neural networks method (multilayer perceptron, MLP), SVM and a bagging method (random forest, RF). This analysis shows that, as long as few data are available to train the model, splines approaches lead to best results, while when the size of training dataset increases, SVM and RF provide faster and more accurate solutions

Spherical GEMs for parallax-free detectors

We developed a method to make GEM foils with a spherical geometry. Tests of this procedure and with the resulting spherical \textsc{gem}s are presented. Together with a spherical drift electrode, a spherical conversion gap can be formed. This would eliminate the parallax error for detection of x-rays, neutrons or UV photons when a gaseous converter is used. This parallax error limits the spatial resolution at wide scattering angles. The method is inexpensive and flexible towards possible changes in the design. We show advanced plans to make a prototype of an entirely spherical triple-GEM detector, including a spherical readout structure. This detector will have a superior position resolution, also at wide angles, and a high rate capability. A completely spherical gaseous detector has never been made before.Comment: Contribution to the 2009 IEEE Nuclear Science Symposium, Orlando, Florid

Tropical deforestation modelling : a comparative analysis of different predictive approaches. The case study of Peten, Guatemala.

The frequent use of predictive models for analysing of complex, natural or artificial, phenomena is changing the traditional approaches to environmental and hazard problems. The continuous improvement of computer performances allows more detailed numerical methods, based on space-time discretisation, to be developed and run for a predictive modeling of complex real systems, reproducing the way their spatial patterns evolve and pointing out the degree of simulation accuracy. In this contribution we present an application of several models (Geomatics, Neural Networks, Land Cover Modeler and Dinamica EGO) in a tropical training area of Peten, Guatemala. During the last decades this region, included into the Biosphere Maya reserve, has known a fast demographic raise and a subsequent uncontrolled pressure on its own geo-resources; the test area can be divided into several sub-regions characterized by different land use dynamics. Understand and quantify these differences permits a better approximation of real system; moreover we have to consider all the physic, socio-economic parameters which will be of use for represent the complex and sometime at random, human impact. Because of the absence of detailed data for our test area, nearly all information were derived from the image processing of 41 ETM+, TM and SPOT scenes; we pointed out the past environmental dynamics and we built the Input layers for the predictive models. The data from 1998 and 2000 were used during the calibration to simulate the Land Cover changes in 2003, selected as reference date for the validation. The basic statistics permit to highlight the qualities or the weaknesses for each model on the different sub-regions

Guided resonances in photonic crystals with point-defected aperiodically-ordered supercells

In this paper, we study the excitation of guided resonances (GRs) in photonic-crystal slabs based on point-defected aperiodically-ordered supercells. With specific reference to perforated-slab structures and the Ammann-Beenker octagonal lattice geometry, we carry out full-wave numerical studies of the plane-wave responses and of the underlying modal structures, which illustrate the representative effects induced by the introduction of symmetry-preserving and symmetry-breaking defects. Our results demonstrate that breaking the supercell mirror symmetries via the judicious introduction of point-defects enables for the excitation of otherwise uncoupled GRs, with control on the symmetry properties of their field distributions, thereby constituting an attractive alternative to those GR-engineering approaches based on the asymmetrization of the hole shape. In this framework, aperiodically-ordered supercells seem to be inherently suited, in view of the variety of inequivalent defect sites that they can offer.Comment: 13 pages, 12 figures, 1 table. Slight change in the title; major changes in the text and figure

Hydrothermal Decomposition of Amino Acids and Origins of Prebiotic Meteoritic Organic Compounds

The organic compounds found in carbonaceous chondrite meteorites provide insight into primordial solar system chemistry. Evaluating the formation and decomposition mechanisms of meteoritic amino acids may aid our understanding of the origins of life and homochirality on Earth. The amino acid glycine is widespread in meteorites and other extraterrestrial environments; other amino acids, such as isovaline, are found with enantiomeric excesses in some meteorites. The relationship between meteoritic amino acids and other compounds with similar molecular structures, such as aliphatic monoamines and monocarboxylic acids is unclear; experimental results evaluating the decomposition of amino acids have produced inconclusive results about the preferred pathways, reaction intermediates, and if the conditions applied may be compatible with those occurring inside meteoritic parent bodies. In this work, we performed extensive tandem metadynamics, umbrella sampling, and committor analysis to simulate the neutral mild hydrothermal decomposition mechanisms of glycine and isovaline and put them into context for the origins of meteoritic organic compounds. Our ab initio simulations aimed to determine free energy profiles and decomposition pathways for glycine and isovaline. We found that under our modeled conditions, methylammonium, glycolic acid, and sec-butylamine are the most likely decomposition products. These results suggest that meteoritic aliphatic monocarboxylic acids are not produced from decomposition of meteoritic amino acids. Our results also indicate that the decomposition of L-isovaline prefers an enantioselective pathway resulting in the production of (S)-sec-butylamine

A Comparison of Three Learning Methods to Predict N2O Fluxes and N Leaching

The environmental costs of intensive farming activities are often under-estimated or not included into the rural development plans, even though they play an important role in addressing future society¿s needs. This paper focus on the use of statistical learning methods to predict the N2O emissions and N leaching under several conservative scenarios, in order to provide an alternative approach to deterministic models at macro-scale. To that aim, three learning methods, namely neural networks (multilayer perceptrons), SVM and random forests, are compared and provide accurate solutions.JRC.DDG.H.2-Climate chang