70 research outputs found
Event reconstruction for KM3NeT/ORCA using convolutional neural networks
The authors acknowledge the financial support of the funding agencies: Agence Nationale de la Recherche (contract ANR-15-CE31-0020), Centre National de la Recherche Scientifique (CNRS), Commission Europeenne (FEDER fund and Marie Curie Program), Institut Universitaire de France (IUF), LabEx UnivEarthS (ANR-10-LABX-0023 and ANR-18-IDEX-0001), Paris Ile-de-France Region, France; Shota Rustaveli National Science Foundation of Georgia (SRNSFG, FR-18-1268), Georgia; Deutsche Forschungsgemeinschaft (DFG), Germany; The General Secretariat of Research and Technology (GSRT), Greece; Istituto Nazionale di Fisica Nucleare (INFN), Ministero dell'Universita e della Ricerca (MUR), PRIN 2017 program (Grant NAT-NET 2017W4HA7S) Italy; Ministry of Higher Education, Scientific Research and Professional Training, Morocco; Nederlandse organisatie voor Wetenschappelijk Onderzoek (NWO), the Netherlands; The National Science Centre, Poland (2015/18/E/ST2/00758); National Authority for Scientific Research (ANCS), Romania; Ministerio de Ciencia, Innovacion, Investigacion y Universidades (MCIU): Programa Estatal de Generacion de Conocimiento (refs. PGC2018-096663-B-C41, -A-C42, -B-C43, -B-C44) (MCIU/FEDER), Severo Ochoa Centre of Excellence and MultiDark Consolider (MCIU), Junta de Andalucia (ref. SOMM17/6104/UGR), Generalitat Valenciana: Grisolia (ref. GRISOLIA/2018/119) and GenT (ref. CIDEGENT/2018/034) programs, La Caixa Foundation (ref. LCF/BQ/IN17/11620019), EU: MSC program (ref. 713673), Spain.The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches.French National Research Agency (ANR)
ANR-15-CE31-0020Centre National de la Recherche Scientifique (CNRS), Commission Europeenne (FEDER fund)European Union (EU)Institut Universitaire de France (IUF)LabEx UnivEarthS
ANR-10-LABX-0023
ANR-18-IDEX-0001Shota Rustaveli National Science Foundation of Georgia
FR-18-1268German Research Foundation (DFG)Greek Ministry of Development-GSRTIstituto Nazionale di Fisica Nucleare (INFN)Ministry of Education, Universities and Research (MIUR)
Research Projects of National Relevance (PRIN)Ministry of Higher Education, Scientific Research and Professional Training, MoroccoNetherlands Organization for Scientific Research (NWO)National Science Centre, Poland
2015/18/E/ST2/00758National Authority for Scientific Research (ANCS), RomaniaMinisterio de Ciencia, Innovacion, Investigacion y Universidades
PGC2018-096663-B-C41
A-C42
B-C43
B-C44Severo Ochoa Centre of ExcellenceJunta de Andalucia
SOMM17/6104/UGRGeneralitat Valenciana: Grisolia
GRISOLIA/2018/119
CIDEGENT/2018/034La Caixa Foundation
LCF/BQ/IN17/11620019EU: MSC program
71367
gSeaGen: The KM3NeT GENIE-based code for neutrino telescopes
Program summary
Program Title: gSeaGen
CPC Library link to program files: http://dx.doi.org/10.17632/ymgxvy2br4.1
Licensing provisions: GPLv3
Programming language: C++
External routines/libraries: GENIE [1] and its external dependencies. Linkable to MUSIC [2] and PROPOSAL
[3].
Nature of problem: Development of a code to generate detectable events in neutrino telescopes, using
modern and maintained neutrino interaction simulation libraries which include the state-of-the-art
physics models. The default application is the simulation of neutrino interactions within KM3NeT [4].
Solution method: Neutrino interactions are simulated using GENIE, a modern framework for Monte
Carlo event generators. The GENIE framework, used by nearly all modern neutrino experiments, is
considered as a reference code within the neutrino community.
Additional comments including restrictions and unusual features: The code was tested with GENIE version
2.12.10 and it is linkable with release series 3. Presently valid up to 5 TeV. This limitation is not intrinsic
to the code but due to the present GENIE valid energy range.
References:
[1] C. Andreopoulos at al., Nucl. Instrum. Meth. A614 (2010) 87.
[2] P. Antonioli et al., Astropart. Phys. 7 (1997) 357.
[3] J. H. Koehne et al., Comput. Phys. Commun. 184 (2013) 2070.
[4] S. Adrián-Martínez et al., J. Phys. G: Nucl. Part. Phys. 43 (2016) 084001.The gSeaGen code is a GENIE-based application developed to efficiently generate high statistics samples
of events, induced by neutrino interactions, detectable in a neutrino telescope. The gSeaGen code is able
to generate events induced by all neutrino flavours, considering topological differences between tracktype
and shower-like events. Neutrino interactions are simulated taking into account the density and
the composition of the media surrounding the detector. The main features of gSeaGen are presented
together with some examples of its application within the KM3NeT project.French National Research Agency (ANR)
ANR-15-CE31-0020Centre National de la Recherche Scientifique (CNRS)European Union (EU)Institut Universitaire de France (IUF), FranceIdEx program, FranceUnivEarthS Labex program at Sorbonne Paris Cite
ANR-10-LABX-0023
ANR-11-IDEX-000502Paris Ile-de-France Region, FranceShota Rustaveli National Science Foundation of Georgia (SRNSFG), Georgia
FR-18-1268German Research Foundation (DFG)Greek Ministry of Development-GSRTIstituto Nazionale di Fisica Nucleare (INFN)Ministry of Education, Universities and Research (MIUR)PRIN 2017 program Italy
NAT-NET 2017W4HA7SMinistry of Higher Education, Scientific Research and Professional Training, MoroccoNetherlands Organization for Scientific Research (NWO)
Netherlands GovernmentNational Science Centre, Poland
2015/18/E/ST2/00758National Authority for Scientific Research (ANCS), RomaniaMinisterio de Ciencia, Innovacion, Investigacion y Universidades (MCIU): Programa Estatal de Generacion de Conocimiento, Spain (MCIU/FEDER)
PGC2018-096663-B-C41
PGC2018-096663-A-C42
PGC2018-096663-BC43
PGC2018-096663-B-C44Severo Ochoa Centre of Excellence and MultiDark Consolider (MCIU), Junta de Andalucia, Spain
SOMM17/6104/UGRGeneralitat Valenciana: Grisolia, Spain
GRISOLIA/2018/119GenT, Spain
CIDEGENT/2018/034La Caixa Foundation
LCF/BQ/IN17/11620019EU: MSC program, Spain
71367
The politics of gossip and denial in inter-organizational relations
Organizational gossip has largely been discussed in terms of effects at the individual level. In this article we turn our attention to the organization level. The article makes a research contribution that addresses gossip that spreads fact-based rumours about organizations in terms of their shifting role in circuits of power. The research question asks what happens when organizations officially formulate themselves as doing one thing while other organizational actors that are influential in significant organizational arenas (in which these formulations circulate) counter that these formulations are patently false. Theoretically, we draw on the literature on organizational gossip and rumour as well as on the politics of non-decision-making. Our argument is advanced by reference to a case study of the Australian Wheat Board and UN Resolution 661. Basically, organizational gossip plays a key role in the production of interorganizational power dynamics, an insight previously neglected. Copyright © 2008 The Tavistock Institute® SAGE Publications
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