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eSASS task: erbackmap



This document provides a description of the algorithm and instructions for the correct usage of the ERBACKMAP task, which is part of eSASS (the eROSITA Science Analysis Software System).


Create smooth background maps by masking source regions in input image and smoothing either by 2D spline fit or an adaptive smoothing algorithm. The task accepts source lists created by ERBOX orERMLDET as input.


The task erbackmap creates the background images for the eROSITA source detection pipeline. The input data are one science image, an input source list (typically created by task erbox in local mode), an exposure map (from task expmap) and detection mask (from task ermask). In a first step circular regions around the input sources are calculated, where the surface brightness of the input sources exceed the threshold set by parameter scut . The radii of these circles depend on the source counts, the PSF, and the source extent parameter. A mask image where this regions are set to 0 can optionally (cheesemask_flag="Y") be written to the file specified by parameter cheesemask . The image data outside the masked out source regions are used to create a smoothed background map which is also interpolated to the positions of the input sources. The user can choose between a 2D spline fit (fitmethod="spline") and an adaptive smoothing algorithm (fitmethod="smooth") to perform the smoothing/interpolation.

The option "smooth" is recommended for all types of observations, the "spline" can be considered as obsolete. The spline fit can be controlled with the parameters nsplinenodes (number of spline nodes per dimension), degree , smoothflag , smoothval . If smoothflag =Y, a smoothing spline with smoothing factor smoothval is used. If smoothflag =N, a weighted least-squares spline is used. For the setting (fitmethod="smooth" an adaptive smoothing algorithm is used to create the background map. If an exposure map is read in (expima_flag="Y"), a count rate map is calculated by dividing the input science image with the exposure map. Source regions are masked out by multiplying the image with the source mask. The adaptive smoothing algorithm (fitmethod="smooth") then convolves the masked count image or count rate image with a set of 8 Gaussian kernels and calculates a signal-to-noise map for each kernel size. The size σ1 of the smallest kernel is set by parameter smoothval in units of image pixels. The sizes of the i=1..8 kernels are σi = σ1 * (√2)i. Also the source masks are convolved with the same kernels and each smoothed image is divided by the smoothed mask in order to account for the masked out regions. Smoothed count rate images are multiplied with the exposure map. The background map is then calculated for each image pixel by finding the two kernel sizes σi, σi+1, whose signal-to-noise maps bracket the required SNR defined by parameter snr. The background value of the pixel is the interpolated from the 2 smoothed images i, i+1.

The task ERBACKMAP can also be used to adaptively smooth an image without removing any source regions.


Input files:

Output files: