AVOCADO: A Virtual Observatory Census to Address Dwarfs Origins

Dwarf galaxies are by far the most abundant of all galaxy types, yet their properties are still poorly understood -especially due to the observational challenge that their intrinsic faintness represents. AVOCADO aims at establishing firm conclusions on their formation and evolution by constructing a homogeneous, multiwavelength dataset for a statistically significant sample of several thousand nearby dwarfs (-18 < Mi < -14). Using public data and Virtual Observatory tools, we have built GALEX+SDSS+2MASS spectral energy distributions that are fitted by a library of single stellar population models. Star formation rates, stellar masses, ages and metallicities are further complemented with structural parameters that can be used to classify them morphologically. This unique dataset, coupled with a detailed characterization of each dwar's environment, allows for a fully comprehensive investigation of their origins and to track the (potential) evolutionary paths between the different dwarf types.Comment: 4 pages, 1 figure. To appear in the proceedings of IAU Symposium 277, "Tracing the Ancestry of Galaxies on the Land of our Ancestors", Carignan, Freeman, and Combes, ed

The importance of major mergers in the build up of stellar mass in brightest cluster galaxies at z=1

Recent independent results from numerical simulations and observations have shown that brightest cluster galaxies (BCGs) have increased their stellar mass by a factor of almost two between z~0.9 and z~0.2. The numerical simulations further suggest that more than half this mass is accreted through major mergers. Using a sample of 18 distant galaxy clusters with over 600 spectroscopically confirmed cluster members between them, we search for observational evidence that major mergers do play a significant role. We find a major merger rate of 0.38 +/- 0.14 mergers per Gyr at z~1. While the uncertainties, which stem from the small size of our sample, are relatively large, our rate is consistent with the results that are derived from numerical simulations. If we assume that this rate continues to the present day and that half of the mass of the companion is accreted onto the BCG during these mergers, then we find that this rate can explain the growth in the stellar mass of the BCGs that is observed and predicted by simulations. Major mergers therefore appear to be playing an important role, perhaps even the dominant one, in the build up of stellar mass in these extraordinary galaxies.Comment: 15 pages, 6 figures, accepted for publication in MNRAS. Reduced data will be made available through the ESO archiv

Transfer learning for galaxy morphology from one survey to another

© 2018 The Author(s). Published by Oxford University Press on behalf of the Royal Astronomical Society.Deep Learning (DL) algorithms for morphological classification of galaxies have proven very successful, mimicking (or even improving) visual classifications. However, these algorithms rely on large training samples of labelled galaxies (typically thousands of them). A key question for using DL classifications in future Big Data surveys is how much of the knowledge acquired from an existing survey can be exported to a new dataset, i.e. if the features learned by the machines are meaningful for different data. We test the performance of DL models, trained with Sloan Digital Sky Survey (SDSS) data, on Dark Energy survey (DES) using images for a sample of $\sim$5000 galaxies with a similar redshift distribution to SDSS. Applying the models directly to DES data provides a reasonable global accuracy ($\sim$ 90%), but small completeness and purity values. A fast domain adaptation step, consisting in a further training with a small DES sample of galaxies ($\sim$500-300), is enough for obtaining an accuracy > 95% and a significant improvement in the completeness and purity values. This demonstrates that, once trained with a particular dataset, machines can quickly adapt to new instrument characteristics (e.g., PSF, seeing, depth), reducing by almost one order of magnitude the necessary training sample for morphological classification. Redshift evolution effects or significant depth differences are not taken into account in this study.Peer reviewedFinal Accepted Versio

Polynucleotide phosphorylase exonuclease and polymerase activities on single-stranded DNA ends are modulated by RecN, SsbA and RecA proteins

Bacillus subtilis pnpA gene product, polynucleotide phosphorylase (PNPase), is involved in double-strand break (DSB) repair via homologous recombination (HR) or non-homologous end-joining (NHEJ). RecN is among the first responders to localize at the DNA DSBs, with PNPase facilitating the formation of a discrete RecN focus per nucleoid. PNPase, which co-purifies with RecA and RecN, was able to degrade single-stranded (ss) DNA with a 3′ → 5′ polarity in the presence of Mn2+ and low inorganic phosphate (Pi) concentration, or to extend a 3′-OH end in the presence dNDP·Mn2+. Both PNPase activities were observed in evolutionarily distant bacteria (B. subtilis and Escherichia coli), suggesting conserved functions. The activity of PNPase was directed toward ssDNA degradation or polymerization by manipulating the Pi/dNDPs concentrations or the availability of RecA or RecN. In its dATP-bound form, RecN stimulates PNPase-mediated polymerization. ssDNA phosphorolysis catalyzed by PNPase is stimulated by RecA, but inhibited by SsbA. Our findings suggest that (i) the PNPase degradative and polymerizing activities might play a critical role in the transition from DSB sensing to end resection via HR and (ii) by blunting a 3′-tailed duplex DNA, in the absence of HR, B. subtilis PNPase might also contribute to repair via NHEJ