New kinematic map of the Milky Way bulge

Context. The kinematics of the Milky Way bulge is known to be complex, reflecting the presence of multiple stellar components with distinct chemical and spatial properties. In particular, the bulge hosts a bar structure exhibiting cylindrical rotation, and a central velocity dispersion peak extending vertically along the Galactic latitude. However, due to severe extinction and crowding, observational constraints near the Galactic plane are sparse, underscoring the need for additional data to improve the completeness and accuracy of existing kinematic maps, and enabling robust comparison with dynamical models. Aims. This work aimed to refine the existing analytical models of the Galactic bulge kinematics by improving constraints in the innermost regions. We present updated maps of the mean velocity and velocity dispersion by incorporating new data near the Galactic plane. Methods. We combined radial velocity measurements from the GIBS and APOGEE surveys with both previously published and newly acquired MUSE observations. A custom-developed Python-based tool, <ns0:monospace>PHOTfun</ns0:monospace>, was used to extract spectra from MUSE datacubes using PSF photometry based on DAOPHOT-II, with an integrated GUI for usability. The method included a dedicated extension, PHOTcube, optimized for IFU datacubes. We applied Markov Chain Monte Carlo techniques to identify and correct for foreground contamination and to derive new analytical fits for the velocity and velocity dispersion distributions. Our analysis included nine new MUSE fields located close to the Galactic plane, bringing the total number of mapped fields to 57 including 23 000 individual RV measured. Results. The updated kinematic maps confirm the cylindrical rotation of the bulge and reveal a more boxy morphology in the velocity dispersion distribution, while preserving a well-defined central peak. The <ns0:monospace>PHOTfun</ns0:monospace> software, designed for flexible PSF photometry and spectral extraction from IFU data, is publicly available via pip for the community.