The Grism lens-amplified survey from space (GLASS). VII. The diversity of the distribution of star formation in cluster and field galaxies at 0.3<z<0.7

Benedetta Vulcani, Tommaso Treu, Kasper B. Schmidt, Takahiro Morishita, Alan Dressler, Bianca M. Poggianti, Louis Abramson, Marusa Bradač, Gabriel B. Brammer, Austin Hoag, Matthew Malkan, Laura Pentericci, Michele Trenti

Published 2016-10-14Version 1

Exploiting the slitless spectroscopy taken as part of the Grism Lens-Amplified Survey from Space (GLASS), we present an extended analysis of the spatial distribution of star formation in 76 galaxies in 10 clusters at 0.3< z <0.7. We use 85 foreground and background galaxies in the same redshift range as a field sample. The samples are well matched in stellar mass (10^8-10^11 M_sun) and star formation rate (0.5-50 M_sun/yr). We visually classify galaxies in terms of broad-band morphology, Halpha morphology and likely physical process acting on the galaxy. Most Halpha emitters have a spiral morphology (41+/-8% in clusters, 51+/-8% in the field), followed by mergers/interactions (28+/-8%, 31+/-7%, respectively) and early-type galaxies (remarkably as high as 29+/-8% in clusters and 15+/-6% in the field). A diversity of Halpha morphologies is detected, suggesting a diversity of physical processes. In clusters, 30+/-8% of the galaxies present a regular morphology, mostly consistent with star formation diffused uniformly across the stellar population (mostly in the disk component, when present). The second most common morphology (28+/-8%) is asymmetric/jellyfish, consistent with ram pressure stripping or other non-gravitational processes in 18+/-8% of the cases. Ram pressure stripping appears significantly less prominent in the field (2+/-2%), where the most common morphology/mechanism appears to be consistent with minor gas rich mergers or clump accretion. This work demonstrates that while environment specific mechanisms affect galaxy evolution at this redshift, they are diverse and their effects subtle. A full understanding of this complexity requires larger samples and detailed and spatially resolved physical models.