Submillimeter Number Counts From Statistical Analysis of BLAST Maps

TL;DR

This paper introduces a statistical P(D) method to estimate submillimeter galaxy counts from BLAST maps, providing unbiased results across a wide flux range, including below the confusion limit.

Contribution

It demonstrates the application of a maximum likelihood P(D) approach to derive galaxy counts from confusion-limited submillimeter data, even for sources with high signal-to-noise ratios.

Findings

Counts follow a steep slope of about -3.7 to -4.5 at 250-500 microns.

Break in the slope occurs below approximately 0.015 Jy.

Method provides unbiased estimates and can be used for future Herschel data.

Abstract

We describe the application of a statistical method to estimate submillimeter galaxy number counts from confusion limited observations by the Balloon-borne Large Aperture Submillimeter Telescope (BLAST). Our method is based on a maximum likelihood fit to the pixel histogram, sometimes called 'P(D)', an approach which has been used before to probe faint counts, the difference being that here we advocate its use even for sources with relatively high signal-to-noise ratios. This method has an advantage over standard techniques of source extraction in providing an unbiased estimate of the counts from the bright end down to flux densities well below the confusion limit. We specifically analyse BLAST observations of a roughly 10 sq. deg. map centered on the Great Observatories Origins Deep Survey South (GOODS-S) field. We provide estimates of number counts at the three BLAST wavelengths, 250, 350, and 500 microns; instead of counting sources in flux bins we estimate the counts at several flux density nodes connected with power-laws. We observe a generally very steep slope for the counts of about -3.7 at 250 microns and -4.5 at 350 and 500 microns, over the range ~0.02-0.5 Jy, breaking to a shallower slope below about 0.015 Jy at all three wavelengths. We also describe how to estimate the uncertainties and correlations in this method so that the results can be used for model-fitting. This method should be well-suited for analysis of data from the Herschel satellite.