J-PLUS: The javalambre photometric local universe survey

ABSTRACT: TheJavalambrePhotometric Local UniverseSurvey (J-PLUS )isanongoing 12-band photometricopticalsurvey, observingthousands of squaredegrees of theNorthernHemispherefromthededicated JAST/T80 telescope at the Observatorio Astrofísico de Javalambre (OAJ). The T80Cam is a camera with a field of view of 2 deg2 mountedon a telescopewith a diameter of 83 cm, and isequippedwith a uniquesystem of filtersspanningtheentireopticalrange (3500–10 000 Å). Thisfiltersystemis a combination of broad-, medium-, and narrow-band filters, optimallydesigned to extracttherest-framespectralfeatures (the 3700–4000 Å Balmer break region, Hδ, Ca H+K, the G band, and the Mg b and Ca triplets) that are key to characterizingstellartypes and delivering a low-resolutionphotospectrumforeach pixel of theobservedsky. With a typicaldepth of AB ∼21.25 mag per band, thisfilter set thusallowsforanunbiased and accuratecharacterization of thestellarpopulation in our Galaxy, itprovidesanunprecedented 2D photospectralinformationforall resolved galaxies in the local Universe, as well as accuratephoto-z estimates (at the δ z/(1 + z)∼0.005–0.03 precisionlevel) formoderatelybright (up to r ∼ 20 mag) extragalacticsources. Whilesomenarrow-band filters are designedforthestudy of particular emissionfeatures ([O II]/λ3727, Hα/λ6563) up to z < 0.017, theyalsoprovidewell-definedwindowsfortheanalysis of otheremissionlines at higherredshifts. As a result, J-PLUS has thepotential to contribute to a widerange of fields in Astrophysics, both in thenearbyUniverse (MilkyWaystructure, globular clusters, 2D IFU-likestudies, stellarpopulations of nearby and moderate-redshiftgalaxies, clusters of galaxies) and at highredshifts (emission-line galaxies at z ≈ 0.77, 2.2, and 4.4, quasi-stellarobjects, etc.). Withthispaper, wereleasethefirst∼1000 deg2 of J-PLUS data, containingabout 4.3 millionstars and 3.0 milliongalaxies at r <  21mag. With a goal of 8500 deg2 forthe total J-PLUS footprint, thesenumbers are expected to rise to about 35 millionstars and 24 milliongalaxiesbytheend of thesurvey.Funding for the J-PLUS Project has been provided by the Governments of Spain and Aragón through the Fondo de Inversiones de Teruel, the Spanish Ministry of Economy and Competitiveness (MINECO; under grants AYA2017-86274-P, AYA2016-77846-P, AYA2016-77237-C3-1-P, AYA2015-66211-C2-1-P, AYA2015-66211-C2-2, AYA2012-30789, AGAUR grant SGR-661/2017, and ICTS-2009-14), and European FEDER funding (FCDD10-4E-867, FCDD13-4E-2685

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

Deep brain stimulation in neurological diseases and other pathologies

Deep brain stimulation (DBS) is a surgical procedure used to treat various neurological pathologies, being its greatest use in movement disorders. The FDA first approved deep brain stimulation in 1997 to treat essential tremor, in 2002 it was approved for Parkinson's disease, in 2003 for dystonia and in 2009 for obsessive compulsive disorder. However, until recently this technique began to be implemented for the treatment of other neurological diseases. To conduct research on the different neurological diseases where deep brain stimulation is used articles were chosen from Pubmed, Google Scholar, Redalyc and Scielo databases, references from the last 10 years to date were taken. The keywords that were written in the search engine were ECP/ECP + the required pathology. 75 references were found on the use of DBS in the following pathologies: Parkinson's disease, Alzheimer's, refractory epilepsy, depression, obsessive–compulsive disorder, Gilles Tourette syndrome, aggressiveness, addictions, anorexia nervosa, restless legs syndrome, headache, dystonia, essential tremor and obesity. The use of DBS is growing as technology advances, increasingly focused on neurological diseases, psychosurgery and even systemic diseases, however, its use is only approved by the FDA for some movement disorders, including Parkinson's disease, Dystonia, essential tremor and OCD. Resumen: La estimulación cerebral profunda (ECP) es un procedimiento quirúrgico utilizado para tratar diversas patologías neurológicas, siendo su mayor uso en los trastornos del movimiento. La FDA aprobó por primera vez la estimulación cerebral profunda en 1997 para tratar el temblor esencial, en 2002 fue aprobada para la enfermedad de Parkinson, en 2003 para la distonía y en 2009 para el trastorno obsesivo compulsivo. No obstante, hasta hace poco tiempo se empezó a implementar esta técnica para el tratamiento de otras enfermedades neurológicas. Para realizar esta investigación sobre las diferentes enfermedades neurológicas en las que se utiliza la estimulación cerebral profunda se eligieron artículos de las bases de datos Pubmed, Google Scholar, Redalyc y Scielo, se tomaron referencias de los últimos 10 años a la fecha. Las palabras claves que se escribieron en el buscador fueron ECP/ECP + + la patología requerida. Se encontraron 75 referencias sobre el uso de ECP en las siguientes patologías: enfermedad de Parkinson, Alzheimer, epilepsia refractaria, depresión, trastorno obsesivo-compulsivo, síndrome de Gilles Tourette, agresividad, adicciones, anorexia nerviosa, síndrome de piernas inquietas, cefalea, distonía, temblor esencial y obesidad. El uso de ECP crece a medida que avanza la tecnología, cada vez más enfocado en enfermedades neurológicas, psicocirugía e incluso enfermedades sistémicas, sin embargo, su uso solo está aprobado por la FDA para algunos trastornos del movimiento, incluida la enfermedad de Parkinson, la distonía, el temblor esencial y TOC

The miniJPAS survey quasar selection I: Mock catalogues for classification

In this series of papers, we employ several machine learning (ML) methods to classify the point-like sources from the miniJPAS catalogue, and identify quasar candidates. Since no representative sample of spectroscopically confirmed sources exists at present to train these ML algorithms, we rely on mock catalogues. In this first paper we develop a pipeline to compute synthetic photometry of quasars, galaxies and stars using spectra of objects targeted as quasars in the Sloan Digital Sky Survey. To match the same depths and signal-to-noise ratio distributions in all bands expected for miniJPAS point sources in the range $17.5\leq r<24$, we augment our sample of available spectra by shifting the original $r$-band magnitude distributions towards the faint end, ensure that the relative incidence rates of the different objects are distributed according to their respective luminosity functions, and perform a thorough modeling of the noise distribution in each filter, by sampling the flux variance either from Gaussian realizations with given widths, or from combinations of Gaussian functions. Finally, we also add in the mocks the patterns of non-detections which are present in all real observations. Although the mock catalogues presented in this work are a first step towards simulated data sets that match the properties of the miniJPAS observations, these mocks can be adapted to serve the purposes of other photometric surveys

The miniJPAS survey quasar selection I: Mock catalogues for classification

In this series of papers, we employ several machine learning (ML) methods to classify the point-like sources from the miniJPAS catalogue, and identify quasar candidates. Since no representative sample of spectroscopically confirmed sources exists at present to train these ML algorithms, we rely on mock catalogues. In this first paper we develop a pipeline to compute synthetic photometry of quasars, galaxies and stars using spectra of objects targeted as quasars in the Sloan Digital Sky Survey. To match the same depths and signal-to-noise ratio distributions in all bands expected for miniJPAS point sources in the range $17.5\leq r<24$, we augment our sample of available spectra by shifting the original $r$-band magnitude distributions towards the faint end, ensure that the relative incidence rates of the different objects are distributed according to their respective luminosity functions, and perform a thorough modeling of the noise distribution in each filter, by sampling the flux variance either from Gaussian realizations with given widths, or from combinations of Gaussian functions. Finally, we also add in the mocks the patterns of non-detections which are present in all real observations. Although the mock catalogues presented in this work are a first step towards simulated data sets that match the properties of the miniJPAS observations, these mocks can be adapted to serve the purposes of other photometric surveys