YCAA Seminar: “Star Formation Histories of Massive Quiescent Galaxies at z > 1”, Helena Domínguez Sánchez - Observatoire de Paris & Department of Physics and Astronomy, University of Pennsylvania

Event time: 
Wednesday, March 15, 2017 - 2:30pm
Hours of operation: 

coffee/tea and cookies will be served in the lounge of Steinbach Hall (52 Hillhouse) starting at 2 PM.

Watson (), A-53 See map
60 Sachem St
New Haven, CT 06511
Event description: 

Three billion years after the big bang (at redshift z = 2), half of the most massive galaxies
were already old, quiescent systems with little to no residual star formation. How were the
lives of these galaxies so they died so fast? In this talk, I will present recent results on the Star Formation Histories (SFHs) of a sample of ~ 100 quiescent massive (log M > 10 M☉)
galaxies at z=1.0 - 1.5, inferred from the analysis of spectro-photometric data from the
SHARDS and HST/WFC3 G102-G141 surveys of the GOODS-N field. The data are compared to stellar population models assuming different SFHs, with the goal of determining 4 basic physical properties of red quiescent galaxies at high-z: their age, star formation timescale ([55349][57045]), metallicity, and extinction. Thanks to the spectral resolution of the SHARDS plus G102 and G141 data, we are able to measure spectral features related to the age of the galaxies (MgUV and D4000), which allow us to break the typical age-[55349][57045], age-extinction degeneracies with great confidence. We find that the derived SFHs for our MQGs are consistent with the slope and the location of the Main Sequence of star-forming galaxies (MS) at z > 1.2, when these galaxies were 0.5–1.0~Gyr old. According to the derived SFH, all of the MQGs experienced a Luminous Infrared Galaxy (LIRG) phase during typically ~500~Myr and roughly half of them went through ULIRG phase for ~100 Myr. I will also briefly introduce other projects I am involved in, including stellar population gradients of local ETGs with the MANGA survey and morphological
classification of galaxies using Deep Learning.