Day 1093

Tuesday.  Wednesday.  Thursday.

1605.03182
Effects of local environment and stellar mass on galaxy quenching out to z~3
Darvish et al

Study the effects of local environment and stellar mass on galaxy properties using a mass complete sample of quiescent and SF systems in the COSMOS field at z<~3.  Show that at z<~1, the median SFR and sSFR of all galaxies depend on environment, and but they become independent of environment at z>~1.  However, find that only for star-forming galaxies, the median SFR and sSFR are similar in different environments, regardless of redshift and stellar mass.  Find that the quiescent fraction depends on environment at z<~1, and on stellar mass out to z~3.  Show that at z<1, galaxies become quiescent faster in denser environments and that the overall environmental quenching efficiency increases with cosmic time.  Environmental and mass quenching processes depend on each other.  At z<1, denser environments more efficiently quench galaxies with higher masses (log(M/Msun)>10.7), possibly due to a higher merger rate of massive galaxies in denser environments, and that mass quenching is more efficient in denser regions.  Show that the overall mass quenching efficiency for more massive galaxies (log(M/Msun)>10.2) rises with cosmic time until z~1 and flattens out since then.  However, for less massive galaxies, the rise in mass quenching efficiency continues to the present time.  The results suggest that environmental quenching is only relevant at z<1, likely a fast process, whereas mass quenching is the dominant mechanism at z>1, with a possible stellar feedback physics.


1605.03201
Stellar classification from single-band imaging using machine learning
Kuntzer, Tewes, Courbin

Information on the spectral types of stars is of great interest in view of the exploitation of space-based imaging surveys.  In this article, investigate the classification of stars into spectral types using only the shape of their diffraction pattern in a single broadband image.  Propose a supervised machine learning approach to this endeavor, based on PCA for dimensionality reduction, followed by ANNs estimating the spectral type.  The analysis is performed with imagine simulations mimicking the HST ACS in the F606W and F814W bands, as well as the Euclid VIS imager.  First demonstrate this classification in a simple context, assuming perfect knowledge of the PSF model and the possibility of accurately generating mock training data for the machine learning.  Then analyse its performance in a fully data-driven situation, in which the training would be performed with a limited subset of bright stars from a survey, and an unknown PSF with spatial variations across the detector.  Use simulations of MS stars with flat distributions in spectral type and in S/N ratio, and classify these stars into 13 spectral subclasses, from O5 to M5.  Under these conditions, the algorithm achieves a high success rate both for Euclid and HST images, with typical errors of half a spectral class.  Although more detailed simulations would be needed to assess the performance of the algorithm on a specific survey, this shows that stellar classification from single-band images is well possible.