An Automated System to Classify Stellar Spectra I

Analyses of stellar spectra often begin with the determination of a number of parameters that define a model atmosphere. This work presents a prototype for an automated spectral classification system that uses a 15 nm-wide region around Hbeta, and applies to stars of spectral types A to K with normal (scaled solar) chemical composition. The new tool exploits synthetic spectra based on plane-parallel flux-constant model atmospheres. The input data are high signal-to-noise spectra with a resolution greater than about 0.1 nm. The output parameters are forced to agree with an external scale of effective temperatures based on the Infrared Flux Method. The system is fast -- a spectrum is classified in a few seconds-- and well-suited for implementation on a web server. We estimate upper limits to the 1-sigma random error in the retrieved effective temperatures, surface gravities, and metallicities as 100 K, 0.3 dex, and 0.1 dex, respectively.Comment: 8 pages, 5 figures; to appear in MNRA

The Footprint Database and Web Services of the Herschel Space Observatory

Data from the Herschel Space Observatory is freely available to the public but no uniformly processed catalogue of the observations has been published so far. To date, the Herschel Science Archive does not contain the exact sky coverage (footprint) of individual observations and supports search for measurements based on bounding circles only. Drawing on previous experience in implementing footprint databases, we built the Herschel Footprint Database and Web Services for the Herschel Space Observatory to provide efficient search capabilities for typical astronomical queries. The database was designed with the following main goals in mind: (a) provide a unified data model for meta-data of all instruments and observational modes, (b) quickly find observations covering a selected object and its neighbourhood, (c) quickly find every observation in a larger area of the sky, (d) allow for finding solar system objects crossing observation fields. As a first step, we developed a unified data model of observations of all three Herschel instruments for all pointing and instrument modes. Then, using telescope pointing information and observational meta-data, we compiled a database of footprints. As opposed to methods using pixellation of the sphere, we represent sky coverage in an exact geometric form allowing for precise area calculations. For easier handling of Herschel observation footprints with rather complex shapes, two algorithms were implemented to reduce the outline. Furthermore, a new visualisation tool to plot footprints with various spherical projections was developed. Indexing of the footprints using Hierarchical Triangular Mesh makes it possible to quickly find observations based on sky coverage, time and meta-data. The database is accessible via a web site (http://herschel.vo.elte.hu) and also as a set of REST web service functions.Comment: Accepted for publication in Experimental Astronom

Grids and the Virtual Observatory

We consider several projects from astronomy that benefit from the Grid paradigm and associated technology, many of which involve either massive datasets or the federation of multiple datasets. We cover image computation (mosaicking, multi-wavelength images, and synoptic surveys); database computation (representation through XML, data mining, and visualization); and semantic interoperability (publishing, ontologies, directories, and service descriptions)

A Sub-Neptune-sized Planet Transiting the M2.5 Dwarf G 9-40: Validation with the Habitable-zone Planet Finder

We validate the discovery of a 2-Earth-radii sub-Neptune-sized planet around the nearby high-proper-motion M2.5 dwarf G 9-40 (EPIC 212048748), using high-precision, near-infrared (NIR) radial velocity (RV) observations with the Habitable-zone Planet Finder (HPF), precision diffuser-assisted ground-based photometry with a custom narrowband photometric filter, and adaptive optics imaging. At a distance of d = 27.9 pc, G 9-40b is the second-closest transiting planet discovered by K2 to date. The planet's large transit depth (~3500 ppm), combined with the proximity and brightness of the host star at NIR wavelengths (J = 10, K = 9.2), makes G 9-40b one of the most favorable sub-Neptune-sized planets orbiting an M dwarf for transmission spectroscopy with James Webb Space Telescope, ARIEL, and the upcoming Extremely Large Telescopes. The star is relatively inactive with a rotation period of ~29 days determined from the K2 photometry. To estimate spectroscopic stellar parameters, we describe our implementation of an empirical spectral-matching algorithm using the high-resolution NIR HPF spectra. Using this algorithm, we obtain an effective temperature of T_(eff) = 3404±73K, and metallicity of [Fe/H] = −0.08±0.13. Our RVs, when coupled with the orbital parameters derived from the transit photometry, exclude planet masses above 11.7M⊕ with 99.7% confidence assuming a circular orbit. From its radius, we predict a mass of M = 5.0^(+3.8)_(−1.9) M⊕ and an RV semiamplitude of K = 4.1^(+3.1)_(−1.6) ms⁻¹, making its mass measurable with current RV facilities. We urge further RV follow-up observations to precisely measure its mass, to enable precise transmission spectroscopic measurements in the future

Using ORB, BoW and SVM to identify and track tagged Norway lobster Nephrops norvegicus (L.)

Sustainable capture policies of many species strongly depend on the understanding of their social behaviour. Nevertheless, the analysis of emergent behaviour in marine species poses several challenges. Usually animals are captured and observed in tanks, and their behaviour is inferred from their dynamics and interactions. Therefore, researchers must deal with thousands of hours of video data. Without loss of generality, this paper proposes a computer vision approach to identify and track specific species, the Norway lobster, Nephrops norvegicus. We propose an identification scheme were animals are marked using black and white tags with a geometric shape in the center (holed triangle, filled triangle, holed circle and filled circle). Using a massive labelled dataset; we extract local features based on the ORB descriptor. These features are a posteriori clustered, and we construct a Bag of Visual Words feature vector per animal. This approximation yields us invariance to rotation and translation. A SVM classifier achieves generalization results above 99%. In a second contribution, we will make the code and training data publically available.Peer Reviewe