Speaker
Description
We present a novel algorithm designed to detect and correct cosmic ray (CR) hits in astronomical images obtained with the MEGARA integral field spectrograph at the Gran Telescopio Canarias (GTC). Traditional approaches in the MEGARA Data Reduction Pipeline (DRP) rely on median stacking of multiple exposures to mitigate CR contamination. However, this method becomes less effective for long exposures, where the same pixel may be struck by CRs in consecutive frames. Moreover, acquiring more than three consecutive long exposures is often not feasible, as it becomes increasingly difficult to ensure that all exposures are equivalent and maintain a consistent flux level, an essential condition for reliable cosmic ray rejection through median stacking.
Our algorithm, implemented within the numina package, introduces an automated strategy that identifies anomalous pixel values using a diagnostic diagram based on the median and minimum signal levels across three or more equivalent exposures. The method generates CR masks that can be applied using different strategies, each offering different trade-offs between noise suppression and CR removal efficiency.
This approach significantly reduces the need for manual intervention and improves the quality of the final reduced data, particularly for science exposures with long integration times. While still under development and subject to further validation, the algorithm represents a substantial step forward in the automated processing of MEGARA data and may be adaptable to other astronomical instruments facing similar challenges.
The algorithm can also be run independently of the MEGARA Data Reduction Pipeline by using a Python script included in the numina package.
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