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In order to understand how galaxies form and evolve, the measurement of the parameters related to their morphologies and also to the way they interact is one of the most relevant requirements. Due to the huge amount of data that is generated by surveys, the morphological and interaction analysis of galaxies can no longer rely on visual inspection. For dealing with such issue, new approaches based on machine learning techniques have been proposed in the last years with the aim of automating the classification process. We tested Deep Learning using images of galaxies obtained from CANDELS to study the accuracy achieved by this tool considering two different frameworks. In the first, galaxies were classified in terms of their shapes considering five morphological categories, while in the second, the way in which galaxies interact was employed for defining other five categories. The results achieved in both cases are compared and discussed.
We present the results of observations of distant galaxies (z ~ 0.8) at high spatial resolution (~0.1"). We observed 7 fields of 1' × 1' with the NACO Adaptive Optics system (VLT) in Ks (2.2μm) band with typical V ~ 14 guide stars and 3h integration time per field. Observed fields are selected within the COSMOS survey area. We analyze the morphologies by means of B/D (Bulge/Disk) decomposition with GIM2D and CAS (Concentration-Asymmetry) estimators for 79 galaxies with magnitudes between Ks = 17 − 23 and classify them in three main morphological types (Late Type, Early Type and Irregulars). We obtain for the first time an estimate of the distribution of galaxy types at redshift z ~ 1 as measured from the near infrared at high spatial resolution.
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