activity
20022026
most citedThe Astropy Project: Building an inclusive, open-science project and status of the v2.0 core package

7.7k citations · 26.1k across the 145 of their papers we have counts for

collaborators
Showing 2009Show all

11 papers · 1 filter

astro-ph.CO2009★ 153 cited

Comparing the relation between star formation and galaxy mass in different environments

Benedetta Vulcani, Bianca M. Poggianti, Rose A. Finn +3

Analyzing 24 mu m MIPS/Spitzer data and the [O II]3727 line of a sample of galaxies at 0.4 < z < 0.8 from the ESO Distant Cluster Survey (EDisCS), we investigate the ongoing star f…

astro-ph.CO2009★ 395 cited

GAMA: towards a physical understanding of galaxy formation

Simon P. Driver, Peder Norberg, Ivan K. Baldry +46

The Galaxy And Mass Assembly (GAMA) project is the latest in a tradition of large galaxy redshift surveys, and is now underway on the 3.9m Anglo-Australian Telescope at Siding Spri…

astro-ph.CO2009★ 137 cited

Galaxy and Mass Assembly (GAMA): Optimal Tiling of Dense Surveys with a Multi-Object Spectrograph

Aaron Robotham, S. P. Driver, P. Norberg +23

A heuristic greedy algorithm is developed for efficiently tiling spatially dense redshift surveys. In its first application to the Galaxy and Mass Assembly (GAMA) redshift survey w…

astro-ph.CO2009★ 130 cited

Galaxy And Mass Assembly (GAMA): The input catalogue and star-galaxy separation

I. K. Baldry, A. S. G. Robotham, D. T. Hill +24

We describe the spectroscopic target selection for the Galaxy And Mass Assembly (GAMA) survey. The input catalogue is drawn from the Sloan Digital Sky Survey (SDSS) and UKIRT Infra…

astro-ph.CO2009★ 256 cited

Galaxy Zoo: Passive Red Spirals

Karen L. Masters, Moein Mosleh, A. Kathy Romer +14

We study the spectroscopic properties and environments of red spiral galaxies found by the Galaxy Zoo project. By carefully selecting face-on, disk dominated spirals we construct a…

astro-ph.CO2009★ 199 cited

Galaxy Zoo: Reproducing Galaxy Morphologies Via Machine Learning

Manda Banerji, Ofer Lahav, Chris J. Lintott +10

We present morphological classifications obtained using machine learning for objects in SDSS DR6 that have been classified by Galaxy Zoo into three classes, namely early types, spi…