Machine learning techniques to select Be star candidates. An application in the OGLE-IV Gaia south ecliptic pole field

Statistical pattern recognition methods have provided competitive solutions for variable star classification at a relatively low computational cost. In order to perform supervised classification, a set of features is proposed and used to train an automatic classification system. Quantities related to the magnitude density of the light curves and their Fourier coefficients have been chosen as features in previous studies. However, some of these features are not robust to the presence of outliers and the calculation of Fourier coefficients is computationally expensive for large data sets. We propose and evaluate the performance of a new robust set of features using supervised classifiers in order to look for new Be star candidates in the OGLE-IV Gaia south ecliptic pole field. We calculated the proposed set of features on six types of variable stars and on a set of Be star candidates reported in the literature. We evaluated the performance of these features using classification trees and random forests along with K-nearest neighbours, support vector machines, and gradient boosted trees methods. We tuned the classifiers with a 10-fold cross-validation and grid search. We validated the performance of the best classifier on a set of OGLE-IV light curves and applied this to find new Be star candidates. The random forest classifier outperformed the others. By using the random forest classifier and colour criteria we found 50 Be star candidates in the direction of the Gaia south ecliptic pole field, four of which have infrared colours consistent with Herbig Ae/Be stars. Supervised methods are very useful in order to obtain preliminary samples of variable stars extracted from large databases. As usual, the stars classified as Be stars candidates must be checked for the colours and spectroscopic characteristics expected for them

PHYLOGENETIC RELATIONSHIPS OF PALAEACANTHOCEPHALA (ACANTHOCEPHALA) INFERRED FROM SSU AND LSU rDNA GENE SEQUENCES

The Palaeacanthocephala is traditionally represented by 2 orders, Echinorhynchida and Polymorphida, with 10 and 3 families, respectively. To test the monophyly of the class, these 2 orders, and certain families, phylogenies were inferred using nuclear small-subunit (SSU) and large-subunit (LSU) ribosomal DNA sequences obtained for 29 species representing 10 families, 2 other classes of acanthocephalans, and 3 rotifer outgroups. Phylogenetic relationships were inferred by analyzing combined SSU and LSU sequences using maximum parsimony (MP) and maximum likelihood (ML) methods. Parsimony and ML trees inferred from combined analysis of these rDNA data strongly supported monophyly of Palaeacanthocephala and provided good resolution among species. Neither Polymorphida nor Echinorhynchida was monophyletic. Gorgorhynchoides bullocki (Echinorhynchida) was nested within the 6 species representing Polymorphida, and this clade was nested within species representing Echinorhynchida. Three of 4 palaeacanthocephalan families that could be evaluated were not monophyletic, and this finding was strongly supported. These results indicate that the family level classification of palaeacanthocephalans, which is mainly based on combinations of shared characters (not shared derived characters), needs to be reevaluated with respect to comprehensively sampled phylogenetic hypotheses

Extracting Hα\alpha flux from photometric data in the J-PLUS survey

We present the main steps that will be taken to extract H$\alpha$ emission flux from Javalambre Photometric Local Universe Survey (J-PLUS) photometric data. For galaxies with $z\lesssim0.015$, the H$\alpha$+[NII] emission is covered by the J-PLUS narrow-band filter $F660$. We explore three different methods to extract the H$\alpha$ + [NII] flux from J-PLUS photometric data: a combination of a broad-band and a narrow-band filter ($r'$ and $F660$), two broad-band and a narrow-band one ($r'$, $i'$ and $F660$), and a SED-fitting based method using 8 photometric points. To test these methodologies, we simulated J-PLUS data from a sample of 7511 SDSS spectra with measured H$\alpha$ flux. Based on the same sample, we derive two empirical relations to correct the derived H$\alpha$+[NII] flux from dust extinction and [NII] contamination. We find that the only unbiased method is the SED fitting based one. The combination of two filters underestimates the measurements of the H$\alpha$ + [NII] flux by a 28%, while the three filters method by a 9%. We study the error budget of the SED-fitting based method and find that, in addition to the photometric error, our measurements have a systematic uncertainty of a 4.3%. Several sources contribute to this uncertainty: differences between our measurement procedure and the one used to derive the spectroscopic values, the use of simple stellar populations as templates, and the intrinsic errors of the spectra, which were not taken into account. Apart from that, the empirical corrections for dust extinction and [NII] contamination add an extra uncertainty of 14%. Given the J-PLUS photometric system, the best methodology to extract H$\alpha$ + [NII] flux is the SED-fitting based one. Using this method, we are able to recover reliable H$\alpha$ fluxes for thousands of nearby galaxies in a robust and homogeneous way.Comment: 11 pages, 14 figures. Minor changes to match the published versio

Modelling the deep-chlorophyll maximum: A coupled physical-biological approach

The Deep Chlorophyll Maximum (DCM) is simulated in two oligotrophic regions (SW Sargasso Sea and NW Mediterranean) using a physical/biological model that couples an upper ocean turbulent model to a nutrient/phytoplankton model. The biological model considers two types of primary producers, heterotrophs and atmospheric in addition to internal nitrate inputs. Model results appear to adequately reproduce the DCM structure in those regions. The DCM depth and magnitude is mainly determined by the vertical eddy diffusion and light extinction. The grazing parameters mainly affect the intensity of the DCM. This suggest the DCM is primarily the result of a balance between upward nutrient flux and light field characteristics. Consequently, the regenerated production only plays a secondary role

Analyzing navigation logs in MOOC: A case study

Continued use of various technological devices has massively increased the generation of digital data, which are recorded as an opportunity for research. In the educational case, it is common to analyze data generated in Learning Management Systems which allows better understand the learning process of the participants and make informed decisions for better e-learning processes and situations in which develop. This paper analyzes participants’ navigation logs in a MOOC hosted on the Coursera platform, for which a visual e-learning analytics process was performed. The results confirm that the videos of experts are an essential educational resource for learning in a MOOC, similarly, the discussion forums are an important resource which are recurrent social spaces in different navigation paths complementing other activities

Plasticidad del crecimiento larvario entre atún rojo y melva modulado por sus interacciones tróficas.

ECOlogía trófica comparativa de LArvas de aTUN rojo atlántico (Thunnus thynnus) de las áreas de puesta del Medterraneo-NO y el Golfo de México.ECOLATU

The GALANTE Photometric System

This paper describes the characterization of the GALANTE photometric system, a seven intermediate- and narrow-band filter system with a wavelength coverage from 3000 $\r{A}$ to 9000 $\r{A}$ . We describe the photometric system presenting the full sensitivity curve as a product of the filter sensitivity, CCD, telescope mirror, and atmospheric transmission curves, as well as some first- and second-order moments of this sensitivity function. The GALANTE photometric system is composed of four filters from the J-PLUS photometric system, a twelve broad-to-narrow filter system, and three exclusive filters, specifically designed to measure the physical parameters of stars such as effective temperature $T_{\rm eff}$, $\log(g)$, metallicity, colour excess $E(4405-5495)$, and extinction type $R_{5495}$. Two libraries, the Next Generation Spectral Library (NGSL) and the one presented in Ma\'iz Apell\'aniz & Weiler (2018), have been used to determine the transformation equations between the Sloan Digital Sky Survey ($\textit{SDSS}$) $\textit{ugriz}$ photometry and the GALANTE photometric system. We will use this transformation to calibrate the zero points of GALANTE images. To this end, a preliminary photometric calibration of GALANTE has been made based on two different $\textit{griz}$ libraries ($\textit{SDSS}$ DR12 and ATLAS All-Sky Stellar Reference Catalog, hereinafter $\textit{RefCat2}$). A comparison between both zero points is performed leading us to the choice of $\textit{RefCat2}$ as the base catalogue for this calibration, and applied to a field in the Cyg OB2 association.Comment: Accepted in MNRA