Single Event Effects in the Pixel readout chip for BTeV

In future experiments the readout electronics for pixel detectors is required to be resistant to a very high radiation level. In this paper we report on irradiation tests performed on several preFPIX2 prototype pixel readout chips for the BTeV experiment exposed to a 200 MeV proton beam. The prototype chips have been implemented in commercial 0.25 um CMOS processes following radiation tolerant design rules. The results show that this ASIC design tolerates a large total radiation dose, and that radiation induced Single Event Effects occur at a manageable level.Comment: 15 pages, 6 Postscript figure

PROPRIETES MECANIQUES ET ANTICORROSIVES DES REVETEMENTS ELECTRODEPOSES A BASE DE NICKEL RENFORCES PAR DES NANOPARTICULES DE TIO

Le besoin d'améliorer les revêtements pour de meilleures propriétés, a permis le développement des dépôts électrolytiques composites, par l’incorporation de particules solides dans la structure du nickel, tels que l’oxyde de titane connu par sa dureté remarquable et sa stabilité chimique. L’objet de notre travail est l’élaboration et la caractérisation des dépôts composites nickel- oxyde de titane, sur un substrat en acier doux E34. Ces dépôts sont obtenus à partir de bains de watts d’électrodéposition sulfatés. La caractérisation est faite par des méthodes conventionnelles telles que la diffraction des RX et la microscopie électronique à balayage MEB pour les caractérisations morphologiques et structurales. Les mesures mécaniques et les tests de corrosion dans une solution de 3,5 % NaCl viennent pour confirmer la faisabilité des couches déposées. Les techniques utilisées sont celles de masse la perdue et de la polarisation. Les résultats obtenus ont révélé une résistance à la corrosion élevée des dépôts composites. The need for improved the coatings with better properties has developed the requirement for the use of composite electrodeposits, by embedding solid particles in the structure of nickel, such as titanium oxide (TiO2) which is a hard compound, chemically stable and irreducible. The objective of this work is the characterization of the composite deposits nickel titanium on mild steel substrate E 34. These deposits are obtained from watts bath of electrodeposition chlorinated. The characterization has been carried out by XR diffraction and scanning electronic microscopy SEM for the structural and morphological characterization. Mechanical measurements and corrosion tests in a 3,5 % NaCl solution are used to confirm the riability of the deposited films. The techniques used are the weight loss and polarization. The results have revealed a higher corrosion resistance of the composite deposit

Performance of prototype BTeV silicon pixel detectors in a high energy pion beam

The silicon pixel vertex detector is a key element of the BTeV spectrometer. Sensors bump-bonded to prototype front-end devices were tested in a high energy pion beam at Fermilab. The spatial resolution and occupancies as a function of the pion incident angle were measured for various sensor-readout combinations. The data are compared with predictions from our Monte Carlo simulation and very good agreement is found.Comment: 24 pages, 20 figure

Beam Test of BTeV Pixel Detectors

The silicon pixel vertex detector is one of the key elements of the BTeV spectrometer. Detector prototypes were tested in a beam at Fermilab. We report here on the measured spatial resolution as a function of the incident angles for different sensor-readout electronics combinations. We compare the results with predictions from our Monte Carlo simulation.Comment: 7 pages, 5 figures, Invited talk given by J.C. Wang at "Vertex 2000, 9th International Workshop on Vertex Detectors", Michigan, Sept 10-15, 2000. To be published in NIM

Gene-gene interaction detection with deep learning

The extent to which genetic interactions affect observed phenotypes is generally unknown because current interaction detection approaches only consider simple interactions between top SNPs of genes. We introduce an open-source framework for increasing the power of interaction detection by considering all SNPs within a selected set of genes and complex interactions between them, beyond only the currently considered multiplicative relationships. In brief, the relation between SNPs and a phenotype is captured by a neural network, and the interactions are quantified by Shapley scores between hidden nodes, which are gene representations that optimally combine information from the corresponding SNPs. Additionally, we design a permutation procedure tailored for neural networks to assess the significance of interactions, which outperformed existing alternatives on simulated datasets with complex interactions, and in a cholesterol study on the UK Biobank it detected nine interactions which replicated on an independent FINRISK dataset.An open-source framework combines deep learning and permutations of gene interaction neural networks to detect complex gene-gene interactions and their significance in contributions to phenotypes.Peer reviewe