Update on the indication of a mass-dependent anisotropy above ${10}^{18.7}$eV in the hybrid data of the Pierre Auger Observatory

We test for an anisotropy in the mass of arriving cosmic-ray primaries associated with the galactic plane. The sensitivity to primary mass is obtained through the depth of shower maximum, $X_{\mathrm{m}\mathrm{a}\mathrm{x}}$, extracted from hybrid events measured over a 14-year period at the Pierre Auger Observatory. The sky is split into distinct on- and off-plane regions using the galactic latitude of each arriving cosmic ray to form two distributions of $X_{\mathrm{m}\mathrm{a}\mathrm{x}}$, which are compared using an Anderson-Darling 2-samples test. A scan over roughly half of the data is used to select a lower threshold energy of ${10}^{18.7}$eV and a galactic latitude splitting at $\mathrm{\mid }b\mathrm{\mid }={30}^{\circ }$, which are set as a prescription for the remaining data. With these thresholds, the distribution of $X_{\mathrm{m}\mathrm{a}\mathrm{x}}$ from the on-plane region is found to have a $9.1\pm 1.6_{-2.2}^{+2.1}$g$$cm${}^{-}2$ shallower mean and a $5.9\pm 2.1_{-2.5}^{+3.5}$g$$cm${}^{-}2$ narrower width than that of the off-plane region and is observed in all telescope sites independently. These differences indicate that the mean mass of primary particles arriving from the on-plane region is greater than that of those from the off-plane region. Monte Carlo studies yield a $5.9\times {10}^{-6}$ random chance probability for the result in the independent data, lowering to a $6.0\times {10}^{-7}$ post-penalization random chance probability when the scanned data is included. Accounting for systematic uncertainties leads to an indication for anisotropy in mass composition above ${10}^{18.7}$eV with a $3.3\sigma$ significance. Furthermore, the result has been newly tested using additional FD data recovered from the selection process. This test independently disfavors the on- and off-plane regions being uniform in composition at the $2.2\sigma$ level, which is in good agreement with the expected sensitivity of the dataset used for this test.