PyCosmic: a robust method to detect cosmics in CALIFA and other fiber-fed integral-field spectroscopy datasets

Context. Detecting cosmic ray hits (cosmics) in fiber-fed integral-field spectroscopy (IFS) data of single exposures is a challenging task because of the complex signal recorded by IFS instruments. Existing detection algorithms are commonly found to be unreliable in the case of IFS data, and the optimal parameter settings are usually unknown a priori for a given dataset. Aims. The Calar Alto legacy integral field area (CALIFA) survey generates hundreds of IFS datasets for which a reliable and robust detection algorithm for cosmics is required as an important part of the fully automatic CALIFA data reduction pipeline. Such a new algorithm needs to be tested against the performance of the commonly used algorithms L. A. Cosmic and DCR. General recommendations for the usage and optimal parameter settings of each algorithm have not yet been systematically studied for fiber-fed IFS datasets to guide users in their choice. Methods. We developed a novel algorithm, PyCosmic, which combines the edge-detection algorithm of L. A. Cosmic with a point-spread function convolution scheme. We generated mock data to compute the efficiency of different algorithms for a wide range of characteristic fiber-fed IFS datasets using the Potsdam Multi-Aperture Spectrophotometer (PMAS) and the VIsible MultiObject Spectrograph (VIMOS) IFS instruments as representative cases. Results. PyCosmic is the only algorithm that achieves an acceptable detection performance for CALIFA data. We find that PyCosmic is the most robust tool with a detection rate of greater than or similar to 90% and a false detection rate less than or similar to 5% for any of the tested IFS data. It has one less free parameter than the L. A. Cosmic algorithm. Only for strongly undersampled IFS data does L. A. Cosmic exceed the performance of PyCosmic by a few per cent. DCR never reaches the efficiency of the other two algorithms and should only be used if computational speed is a concern. Thus, PyCosmic appears to be the most versatile cosmics detection algorithm for IFS data. It is implemented in the new CALIFA data reduction pipeline as well as in recent versions of the multi-instrument IFS pipeline P3D. Although PyCosmic has been optimized for IFS data, we have also successfully applied it to longslit data and anticipate that good results will be achieved with imaging data.