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dc.rights.license© ESO 2019-
dc.contributor.authorMartín, S.-
dc.contributor.authorMartín Pintado, J.-
dc.contributor.authorBlanco Sánchez, C.-
dc.contributor.authorRivilla, V. M.-
dc.contributor.authorRodríguez Franco, A.-
dc.contributor.authorRico Villas, F.-
dc.contributor.otherUnidad de Excelencia Científica María de Maeztu Centro de Astrobiología del Instituto Nacional de Técnica Aeroespacial y CSIC, MDM-2017-0737-
dc.identifier.citationAstronomy and Astrophysics 631: A159(2019)es
dc.description.abstractContext. The increase in bandwidth and sensitivity of state-of-the-art radio observatories is providing a wealth of molecular data from nearby star-forming regions up to high-z galaxies. Analysing large data sets of spectral cubes requires efficient and user-friendly tools optimised for astronomers with a wide range of backgrounds. Aims. In this paper we present the detailed formalism at the core of Spectral Line Identification and Modelling (SLIM) within the MAdrid Data CUBe Analysis (MADCUBA) package and their main data-handling functionalities. These tools have been developed to visualise, analyse, and model large spectroscopic data cubes. Methods. We present the highly interactive on-the-fly visualisation and modelling tools of MADCUBA and SLIM, which includes a stand-alone spectroscopic database. The parameters stored therein are used to solve the full radiative transfer equation under local thermodynamic equilibrium (LTE). The SLIM package provides tools to generate synthetic LTE model spectra based on input physical parameters of column density, excitation temperature, velocity, line width, and source size. It also provides an automatic fitting algorithm to obtain the physical parameters (with their associated errors) better fitting the observations. Synthetic spectra can be overlayed in the data cubes/spectra to ease the task of multi-molecular line identification and modelling. Results. We present the Java-based MADCUBA and its internal module SLIM packages which provide all the necessary tools for manipulation and analysis of spectroscopic data cubes. We describe in detail the spectroscopic fitting equations and make use of this tool to explore the breaking conditions and implicit errors of commonly used approximations in the literature. Conclusions. Easy-to-use tools like MADCUBA allow users to derive physical information from spectroscopic data without the need for simple approximations. The SLIM tool allows the full radiative transfer equation to be used, and to interactively explore the space of physical parameters and associated uncertainties from observational
dc.description.sponsorshipThe MADCUBA and SLIM development has been partially funded through the Spanish grants ESP2013-21697-C05-01, ESP2015-65597-C4-01-R and ESP2017-86582-C4-01-R. V. M. R. has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 664931. S. M. and V. M. R. acknowledge support from the Joint ALMA Observatory Visitor Program; With funding from the Spanish government through the "María de Maeztu Unit of Excellence" accreditation (MDM-2017-0737).es
dc.publisherEDP Scienceses
dc.relationinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/ESP2017-86582-C4-1-R/ES/CONTRIBUCION ESPAÑOLA A LAS MISIONES ESPACIALES CRIOGENICAS SPICA Y ATHENA, POST-OPERACIONES DE HERSCHEL Y EXPLOTACION CIENTIFICA MULTIFRECUENCIA/-
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.subjectLine: identificationes
dc.subjectRadiative transferes
dc.subjectMethods: data analysises
dc.subjectISM: moleculeses
dc.subjectRdio lines: ISMes
dc.subjectSubmillimeter: ISMes
dc.titleSpectral Line Identification and Modelling (SLIM) in the MAdrid Data CUBe Analysis (MADCUBA) package Interactive software for data cube analysises
dc.contributor.orcidRivilla, V. M. [0000-0002-2887-5859]-
dc.contributor.orcidMartín Ruiz, S. [0000-0001-9281-2919]-
dc.contributor.orcidRico Villas, F. [0000-0002-5351-3497]-
dc.contributor.funderAgencia Estatal de Investigación (AEI)-
dc.contributor.funderMinisterio de Economía y Competitividad (MINECO)-
dc.contributor.funderEuropean Research Council (ERC)-
dc.description.peerreviewedPeer reviewes
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