71 research outputs found
Bulk and interfacial properties of a dipolar-quadrupolar fluid in a uniform electric field: A density-functional approach
Microflares and the Statistics of X-ray Flares
This review surveys the statistics of solar X-ray flares, emphasising the new
views that RHESSI has given us of the weaker events (the microflares). The new
data reveal that these microflares strongly resemble more energetic events in
most respects; they occur solely within active regions and exhibit
high-temperature/nonthermal emissions in approximately the same proportion as
major events. We discuss the distributions of flare parameters (e.g., peak
flux) and how these parameters correlate, for instance via the Neupert effect.
We also highlight the systematic biases involved in intercomparing data
representing many decades of event magnitude. The intermittency of the
flare/microflare occurrence, both in space and in time, argues that these
discrete events do not explain general coronal heating, either in active
regions or in the quiet Sun.Comment: To be published in Space Science Reviews (2011
Fontes de proteĂna, ingestĂŁo de alimentos e fluxo esplĂąncnico de nutrientes em ovinos
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. AdriĂĄn-MartĂnez 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
A muon-track reconstruction exploiting stochastic losses for large-scale Cherenkov detectors
IceCube is a cubic-kilometer Cherenkov telescope operating at the South Pole. The main goal of IceCube is the detection of astrophysical neutrinos and the identification of their sources. High-energy muon neutrinos are observed via the secondary muons produced in charge current interactions with nuclei in the ice. Currently, the best performing muon track directional reconstruction is based on a maximum likelihood method using the arrival time distribution of Cherenkov photons registered by the experiment\u27s photomultipliers. A known systematic shortcoming of the prevailing method is to assume a continuous energy loss along the muon track. However at energies >1 TeV the light yield from muons is dominated by stochastic showers. This paper discusses a generalized ansatz where the expected arrival time distribution is parametrized by a stochastic muon energy loss pattern. This more realistic parametrization of the loss profile leads to an improvement of the muon angular resolution of up to 20% for through-going tracks and up to a factor 2 for starting tracks over existing algorithms. Additionally, the procedure to estimate the directional reconstruction uncertainty has been improved to be more robust against numerical errors
Fontes de carboidratos e porcentagem de volumosos em dietas para ovinos: balanço de nitrogĂȘnio, digestibilidade e fluxo portal de nutrientes
Sensitivity of the KM3NeT/ARCA neutrino telescope to point-like neutrino sources
Instrumentatio
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