CLOVER (Convnet Line-fitting Of Velocities in Emission-line Regions) is a convolutional neural network (ConvNet) trained to identify spectra with two velocity components along the line of sight and predict their kinematics. It works with Gaussian emission lines (e.g., CO) and lines with hyperfine structure (e.g., NH3). CLOVER has two prediction steps, classification and parameter prediction. For the first step, CLOVER segments the pixels in an input data cube into one of three classes: noise (i.e., no emission), one-component (emission line with single velocity component), and two-component (emission line with two velocity components). For the pixels identified as two-components in the first step, a second regression ConvNet is used to predict centroid velocity, velocity dispersion, and peak intensity for each velocity component.

Capivara implements a spectral-based segmentation method for Integral Field Unit (IFU) data cubes. The code uses hierarchical clustering in the spectral domain, grouping similar spectra to improve the signal-to-noise ratio without compromising astrophysical similarity among regions, and leverages advanced matrix operations via torch for GPU acceleration.