Measuring galaxy segregation with the mark connection function

Author(s): Martinez, V. J.; Arnalte-Mur, P.; Stoyan, D.

Source: ASTRONOMY & ASTROPHYSICS  Volume: 513     Article Number: A22   DOI: 10.1051/0004-6361/200912922   Published: APR 2010

Context. The clustering properties of galaxies belonging to different luminosity ranges or having different morphological types are different. These characteristics or “marks” permit us to understand the galaxy catalogs that carry all this information as realizations of marked point processes. Many attempts have been presented to quantify the dependence of the clustering of galaxies on their inner properties.

Aims. The present paper summarizes methods on spatial marked statistics used in cosmology to disentangle luminosity, color or morphological segregation and introduces a new one in this context, the mark connection function.

Methods. The methods used here are the partial correlation functions, including the cross-correlation function, the normalized mark correlation function, the mark variogram and the mark connection function. All these methods are applied to a volume-limited sample drawn from the 2dFGRS, using the spectral type eta as the mark.

Results. We show the virtues of each method to provide information about the clustering properties of each population, the dependence of the clustering on the marks, the similarity of the marks as a function of the pair distances, and the way to characterize the spatial correlation between the marks. We demonstrate by means of these statistics that passive galaxies exhibit a stronger spatial correlation than active galaxies at small scales (r less than or similar to 20 h(-1) Mpc), and that the price for galaxies to be close together is in the smaller values of the assigned marks, which means in our case that they are more passive. Through the mark connection function we quantify the relative positioning of different types of galaxies within the overall clustering pattern.

Conclusions. The different marked statistics provide different information about the clustering properties of each population. Different aspects of the segregation are encapsulated by each measure, which makes the new one introduced here – the mark connection function – particularly useful for understanding the spatial correlation between the marks.