On the importance of background subtraction in the analysis of coronal loops observed with TRACE

TL;DR

This paper compares two background subtraction methods in TRACE coronal loop analysis, finding that pixel-to-pixel subtraction yields more reliable diagnostics of loop cooling, highlighting the importance of background removal techniques.

Contribution

It demonstrates that pixel-to-pixel background subtraction provides more consistent diagnostics than interpolation methods in TRACE loop analysis.

Findings

Pixel-to-pixel subtraction yields clearer temperature profiles.

Interpolation-based subtraction introduces systematic errors.

Background subtraction method significantly affects loop diagnostics.

Abstract

In the framework of TRACE coronal observations, we compare the analysis and diagnostics of a loop after subtracting the background with two different and independent methods. The dataset includes sequences of images in the 171 A, 195 A filter bands of TRACE. One background subtraction method consists in taking as background values those obtained from interpolation between concentric strips around the analyzed loop. The other method is a pixel-to-pixel subtraction of the final image when the loop had completely faded out, already used by Reale & Ciaravella 2006. We compare the emission distributions along the loop obtained with the two methods and find that they are considerably different. We find differences as well in the related derive filter ratio and temperature profiles. In particular, the pixel-to-pixel subtraction leads to coherent diagnostics of a cooling loop. With the other subtraction the diagnostics are much less clear. The background subtraction is a delicate issue in the analysis of a loop. The pixel-to-pixel subtraction appears to be more reliable, but its application is not always possible. Subtraction from interpolation between surrounding regions can produce higher systematic errors, because of intersecting structures and of the large amount of subtracted emission in TRACE observations.