New determination of abundances and stellar parameters for a set of weak G-band stars

Weak G-band (wGb) stars are very peculiar red giants almost devoided of carbon and often mildly enriched in lithium. Despite their very puzzling abundance patterns, very few detailed spectroscopic studies existed up to a few years ago, preventing any clear understanding of the wGb phenomenon. We recently proposed the first consistent analysis of published data for 28 wGb stars and identified them as descendants of early A-type to late B-type stars, without being able to conclude on their evolutionary status or the origin of their peculiar abundance pattern. We used newly obtained high-resolution and high SNR spectra for 19 wGb stars in the southern and northern hemisphere to homogeneously derive their fundamental parameters, metallicities, as well as the spectroscopic abundances for Li, C, N, O, Na, Sr, and Ba. We also computed dedicated stellar evolution models that we used to determine the masses and to investigate the evolutionary status and chemical history of the stars in our sample. We confirm that the wGb stars are stars in the mass range 3.2 to 4.2 M$_\odot$. We suggest that a large fraction could be mildly evolved stars on the SGB currently undergoing the 1st DUP, while a smaller number of stars are more probably in the core He burning phase at the clump. After analysing their abundance pattern, we confirm their strong N enrichment anti-correlated with large C depletion, characteristic of material fully processed through the CNO cycle to an extent not known in other evolved intermediate-mass stars. However, we demonstrate here that such a pattern is very unlikely due to self-enrichment. In the light of the current observational constraints, no solid self-consistent pollution scenario can be presented either, leaving the wGb puzzle largely unsolved.Comment: 19 pages , 14 figures, accepted for publication in Astronomy & Astrophysic

Deep learning approach for classifying, detecting and predicting photometric redshifts of quasars in the Sloan Digital Sky Survey stripe 82

We have applied a convolutional neural network (CNN) to classify and detect quasars in the Sloan Digital Sky Survey Stripe 82 and also to predict the photometric redshifts of quasars. The network takes the variability of objects into account by converting light curves into images. The width of the images, noted w, corresponds to the five magnitudes ugriz and the height of the images, noted h, represents the date of the observation. The CNN provides good results since its precision is 0.988 for a recall of 0.90, compared to a precision of 0.985 for the same recall with a random forest classifier. Moreover 175 new quasar candidates are found with the CNN considering a fixed recall of 0.97. The combination of probabilities given by the CNN and the random forest makes good performance even better with a precision of 0.99 for a recall of 0.90. For the redshift predictions, the CNN presents excellent results which are higher than those obtained with a feature extraction step and different classifiers (a K-nearest-neighbors, a support vector machine, a random forest and a Gaussian process classifier). Indeed, the accuracy of the CNN within |Δz| < 0.1 can reach 78.09%, within |Δz| < 0.2 reaches 86.15%, within |Δz| < 0.3 reaches 91.2% and the value of root mean square (rms) is 0.359. The performance of the KNN decreases for the three |Δz| regions, since within the accuracy of |Δz| < 0.1, |Δz| < 0.2, and |Δz| < 0.3 is 73.72%, 82.46%, and 90.09% respectively, and the value of rms amounts to 0.395. So the CNN successfully reduces the dispersion and the catastrophic redshifts of quasars. This new method is very promising for the future of big databases such as the Large Synoptic Survey Telescope