  We present a robust, efficient, and user-friendly algorithm for detecting
faint emission-line sources in large integral-field spectroscopic datacubes
together with the public release of the software package LSDCat (Line Source
Detection and Cataloguing). LSDCat uses a 3-dimensional matched filter
approach, combined with thresholding in signal-to-noise, to build a catalogue
of individual line detections. In a second pass, the detected lines are grouped
into distinct objects, and positions, spatial extents, and fluxes of the
detected lines are determined. LSDCat requires only a small number of input
parameters, and we provide guidelines for choosing appropriate values. The
software is coded in Python and capable to process very large datacubes in a
short time. We verify the implementation with a source insertion and recovery
experiment utilising a real datacube taken with the MUSE instrument at the ESO
Very Large Telescope.
