activity
20062010
most citedStar formation and nuclear activity in close pairs of early-type galaxies

46 citations · 146 across the 6 of their papers we have counts for

collaborators

6 papers

astro-ph.CO2010

Does environment affect the star formation histories of early-type galaxies?

Ignacio Ferreras, Anna Pasquali, Ben Rogers

Differences in the stellar populations of galaxies can be used to quantify the effect of environment on the star formation history. We target a sample of early-type galaxies from t…

astro-ph.CO2010★ 28 cited

The Role of Environment on the Formation of Early-Type Galaxies

Ben Rogers, Ignacio Ferreras, Anna Pasquali +3

(Abridged) We present a detailed study of the stellar populations of a volume-limited sample of early-type galaxies from SDSS, across a range of environments -- defined as the mass…

astro-ph.CO2009★ 46 cited

Star formation and nuclear activity in close pairs of early-type galaxies

Ben Rogers, Ignacio Ferreras, Sugata Kaviraj +2

We extract from the Sloan Digital Sky Survey a sample of 347 systems involving early type galaxies separated by less than 30 kpc, in projection, and 500 km/s in radial velocity. Th…

astro-ph2008★ 46 cited

Exploring the Star Formation History of Elliptical Galaxies: Beyond Simple Stellar Populations with a New Estimator of Line Strengths

Ben Rogers, Ignacio Ferreras, Reynier F. Peletier +1

(Abridged) We study the stellar populations of 14 elliptical galaxies in the Virgo cluster. We propose an alternative approach to the standard side-band method to measure equivalen…

astro-ph2007★ 26 cited

Decoding the spectra of SDSS early-type galaxies: New indicators of age and recent star formation

Ben Rogers, Ignacio Ferreras, Ofer Lahav +3

(Abridged) We apply Principal Component Analysis (PCA) to a sample of early-type galaxies from the Sloan Digital Sky Survey (SDSS) in order to infer differences in their star forma…

astro-ph2006

Principal Component Analysis as a tool to explore star formation histories

I. Ferreras, B. Rogers, O. Lahav +1

Principal Component Analysis (PCA) is a well-known multivariate technique used to decorrelate a set of vectors. PCA has been extensively applied in the past to the classification o…