Mass composition anisotropy is predicted by a number of theories describing sources of ultra-high-energy cosmic rays. Event-by-event determination of a type of a primary cosmic-ray particle is impossible due to large shower-to-shower fluctuations, and the mass composition usually is obtained by averaging over some composition-sensitive observable determined independently for each extensive air shower (EAS) over a large number of events. In the present study we propose to employ the observable $\xi$ used in the TA mass composition analysis for the mass composition anisotropy analysis. The $\xi$ variable is determined with the use of Boosted Decision Trees (BDT) technique trained with the Monte-Carlo sets, and the $\xi$ value is assigned for each event, where $\xi=1$ corresponds to an event initiated by the primary iron nuclei and $\xi=-1$ corresponds to a proton event.

Use of $\xi$ distributions obtained for the Monte-Carlo sets allows us to separate proton and iron candidate events from a data set with some given accuracy and study its distributions over the observed part of the sky. Results for the TA SD 11-year data set mass composition anisotropy will be presented.