Virtual observatory publishing with DaCHS

Demleitner, Markus; Neves, Margarida Castro; Rothmaier, F.; Wambsganß, J.

doi:10.1016/j.ascom.2014.08.003

Cited by 21 publications

(19 citation statements)

References 14 publications

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“…This task would be extremely tedious without the usage of the Virtual Observatory technology based on the IVOA standards, namely the combination of the Table Access (Nandrekar-Heinis et al 2014) and Simple Spectra Access (Tody et al 2012) protocols and VO client applications such as TOPCAT (Taylor 2005), Aladin (Bonnarel et al 2000), or SPLAT-VO (Škoda et al 2014). All LAMOST DR2 FITS files, converted into the linear wavelength in Ångströms, were ingested into a VO server based on the DACHS system (Demleitner et al 2014) that runs locally in Ondřejov, and the links (called accref or access_url) to individual spectra on that server were joined with spectrum names in the candidate table. This allowed an immediate visualisation and interactive zooming of every spectrum in SPLAT-VO.…”

Section: Discussionmentioning

confidence: 99%

Active deep learning method for the discovery of objects of interest in large spectroscopic surveys

Škoda

Podsztavek

Tvrdík

2020

A&A

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Context. Current archives of the LAMOST telescope contain millions of pipeline-processed spectra that have probably never been seen by human eyes. Most of the rare objects with interesting physical properties, however, can only be identified by visual analysis of their characteristic spectral features. A proper combination of interactive visualisation with modern machine learning techniques opens new ways to discover such objects. Aims. We apply active learning classification methods supported by deep convolutional neural networks to automatically identify complex emission-line shapes in multi-million spectra archives. Methods. We used the pool-based uncertainty sampling active learning method driven by a custom-designed deep convolutional neural network with 12 layers. The architecture of the network was inspired by VGGNet, AlexNet, and ZFNet, but it was adapted for operating on one-dimensional feature vectors. The unlabelled pool set is represented by 4.1 million spectra from the LAMOST data release 2 survey. The initial training of the network was performed on a labelled set of about 13 000 spectra obtained in the 400 Å wide region around Hα by the 2 m Perek telescope of the Ondřejov observatory, which mostly contains spectra of Be and related early-type stars. The differences between the Ondřejov intermediate-resolution and the LAMOST low-resolution spectrographs were compensated for by Gaussian blurring and wavelength conversion. Results. After several iterations, the network was able to successfully identify emission-line stars with an error smaller than 6.5%. Using the technology of the Virtual Observatory to visualise the results, we discovered 1 013 spectra of 948 new candidates of emission-line objects in addition to 664 spectra of 549 objects that are listed in SIMBAD and 2 644 spectra of 2 291 objects identified in an earlier paper of a Chinese group led by Wen Hou. The most interesting objects with unusual spectral properties are discussed in detail.

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Section: Discussionmentioning

confidence: 99%

Active deep learning method for the discovery of objects of interest in large spectroscopic surveys

Škoda

Podsztavek

Tvrdík

2020

A&A

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“…Data services are installed in the provider institutes and are declared in the standard IVOA registries; they are thus always visible and searchable by query interfaces. A standard procedure has been identified for design and publication, based on the DaCHS server (Demleitner et al 2014), although other solutions are possible. At…”

Section: Data Servicesmentioning

confidence: 99%

Virtual European Solar & Planetary Access (VESPA): A Planetary Science Virtual Observatory Cornerstone

Érard

Cecconi

Sidaner

et al. 2020

Data Science Journal

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The Europlanet-2020 programme, which ended on Aug 31 st , 2019, included an activity called VESPA (Virtual European Solar and Planetary Access), which focused on adapting Virtual Observatory (VO) techniques to handle Planetary Science data. This paper describes some aspects of VESPA at the end of this 4-years development phase and at the onset of the newly selected Europlanet-2024 programme starting in 2020. The main objectives of VESPA are to facilitate searches both in big archives and in small databases, to enable data analysis by providing simple data access and online visualization functions, and to allow research teams to publish derived data in an interoperable environment as easily as possible. VESPA encompasses a wide scope, including surfaces, atmospheres, magnetospheres and planetary plasmas, small bodies, heliophysics, exoplanets, and spectroscopy in solid phase. This system relies in particular on standards and tools developed for the Astronomy VO (IVOA) and extends them where required to handle specificities of Solar System studies. It also aims at making the VO compatible with tools and protocols developed in different contexts, for instance GIS for planetary surfaces, or time series tools for plasma-related measurements. An essential part of the activity is to publish a significant amount of high-quality data in this system, with a focus on derived products resulting from data analysis or simulations.

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“…Data publication to VESPA can be done by means of the German Astronomical Virtual Observatory (GAVO) Data Center Helper Suite (DaCHS). GAVO DaCHS is a Python server designed to provide VO‐compliant access to data through a variety of IVOA standard protocols (Demleitner et al, ) of which we only use EuroPlaNet Table Access Protocol (EPN‐TAP) (Erard, Cecconi, Le Sidaner, Berthier, Henry, Molinaro et al, ) version 2.0.…”

Section: Toolsmentioning

confidence: 99%

Bridging the Gap Between Geographical Information Systems and Planetary Virtual Observatory

Minin

Rossi

Figuera

et al. 2019

Earth and Space Science

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Virtual Observatory tools are specifically designed for astronomical data, but they can be adapted to work with geospatial data by providing existing Geographical Information System tools with Simple Application Messaging Protocol interface. Open source QGIS package was chosen as a platform for this. The Simple Application Messaging Protocol interface was made with Python plug‐ins. Geospatial data were exposed to Virtual European Solar and Planetary Access via a dedicated German Astronomical Virtual Observatory Data Center Helper Suite server and several tables exposing existing Open Geospatial Consortium—compliant planetary services were published to Virtual European Solar and Planetary Access providing a variety of geospatial data types: vector data and spectral cube rasters, as well as Open Geospatial Consortium Web Map Service of planetary maps.

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Virtual observatory publishing with DaCHS

Cited by 21 publications

References 14 publications

Active deep learning method for the discovery of objects of interest in large spectroscopic surveys

Active deep learning method for the discovery of objects of interest in large spectroscopic surveys

Virtual European Solar & Planetary Access (VESPA): A Planetary Science Virtual Observatory Cornerstone

Bridging the Gap Between Geographical Information Systems and Planetary Virtual Observatory

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