Resultados de Búsqueda por Autor

1

objeto de conferencia

Vertex reconstruction of neutrino interactions using deep learning

Publicado por
Terwilliger A.M., Perdue G.N., Isele D., Patton R.M., Young S.R.

Publicado 2017

Deep learning offers new tools to improve our understanding of many important scientific problems. Neutrinos are the most abundant particles in existence and are hypothesized to explain the matter-antimatter asymmetry that dominates our universe. Definitive tests of this conjecture require a detailed understanding of neutrino interactions with a variety of nuclei. Many measurements of interest depend on vertex reconstruction – finding the origin of a neutrino interaction using data from the detector, which can be represented as images. Traditionally, this has been accomplished by utilizing methods that identify the tracks coming from the interaction. However, these methods are not ideal for interactions where an abundance of tracks and cascades occlude the vertex region. Manual algorithm engineering to handle these challenges is complicated and error prone. Deep learning extracts rich,...

2

objeto de conferencia

Neuromorphic computing for temporal scientific data classification

Publicado por
Schuman C.D., Potok T.E., Young S., Patton R., Perdue G., Chakma G., Wyer A., Rose G.S.

Publicado 2017

Enlace

This material is based upon work supported in part by the U.S. Department of Energy, Ofce of Science, Ofce of Advanced Scientifc Computing Research, under contract number DE-AC05-00OR22725. Research sponsored in part by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the U. S. Department of Energy. This research used resources of the Oak Ridge Leadership Computing Facility, which is a DOE Ofce of Science User Facility supported under Contract DE-AC05-00OR22725. We would like to thank the MINERvA collaboration for the use of their simulated data and for many useful and stimulating conversations. MINERvA is supported by the Fermi National Accelerator Laboratory under US Department of Energy contract No. DE-AC02-07CH11359 which included the MINERvA construction project. MINERvA construction support was also granted ...

3

artículo

Reducing model bias in a deep learning classifier using domain adversarial neural networks in the MINERvA experiment

Publicado por
Nuruzzaman, Perdue G.N., Ghosh A., Wospakrik M., Akbar F., Andrade D.A., Ascencio M., Bellantoni L., Bercellie A., Betancourt M., Vera G.F.R.C., Cai T., Carneiro M.F., Chaves J., Coplowe D., Motta H.D., Díaz G.A., Felix J., Fields L., Fine R., Gago A.M., Galindo R., Golan T., Gran R., Han J.Y., Harris D.A., Jena D., Kleykamp J., Kordosky M., Lu X.-G., Maher E., Mann W.A., Marshall C.M., McFarland K.S., McGowan A.M., Messerly B., Miller J., Nelson J.K., Nguyen C., Norrick A., Nuruzzaman N., Olivier A., Patton R., Ramírez M.A., Ransome R.D., Ray H., Ren L., Rimal D., Ruterbories D., Schellman H., Salinas C.J.S., Su H., Upadhyay S., Valencia E., Wolcott J., Yaeggy B., Young S.

Publicado 2018

Enlace

Construction support also was granted by the United States National Science Foundation under Award PHY-0619727 and by the University of Rochester. Additional support for participating scientists was provided by NSF and DOE (U.S.A.) by CAPES and CNPq (Brazil), by CoNaCyT (Mexico), by Proyecto Basal FB 0821, CONICYT PIA ACT1413, Fondecyt 3170845 and 11130133 (Chile), by PIIC (DGIP-UTFSM), by CONCYTEC, DGI-PUCP and IDI/IGI-UNI (Peru)