A Geometric Approach to Estimate Background in Astronomical Images
Estimating the true background in an astronomical image is fundamental to detecting faint sources. In a typical low-photon-count astronomical image, such as in the far- and near-ultraviolet wavelength ranges, conventional methods relying on 3 σ clipping and median or mode estimation often fail to ca...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2025-01-01
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| Series: | The Astrophysical Journal Supplement Series |
| Subjects: | |
| Online Access: | https://doi.org/10.3847/1538-4365/ad9906 |
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| Summary: | Estimating the true background in an astronomical image is fundamental to detecting faint sources. In a typical low-photon-count astronomical image, such as in the far- and near-ultraviolet wavelength ranges, conventional methods relying on 3 σ clipping and median or mode estimation often fail to capture the true background level accurately. As a consequence, differentiating true sources from noise peaks remains a challenging task. Additionally, in such images, effectively identifying and excluding faint sources during the background estimation process remains crucial, as undetected faint sources could contaminate the background. This results in overestimating the true background and obscuring the detection of very faint sources. To tackle this problem, we introduce a geometric approach based on the method of steepest descent to identify local minima in an astronomical image. The proposed algorithm, based on the minima statistics, effectively reduces the confusion between sources and background in the image, thereby ensuring a better background estimation and enhancing the reliability of faint-source detection. Our algorithm performs well compared to conventional methods in estimating the background even in crowded field images. In low-photon-count, less crowded images, our algorithm recovers the background within 10%, while traditional methods drastically underestimate it by a few orders of magnitude. In crowded fields, the conventional methods overestimate the background by ∼200% whereas our algorithm recovers the true background within ∼14%. We provide a simple prescription to create a background map using our algorithm and discuss its application in large astronomical surveys. |
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| ISSN: | 0067-0049 |