TriOS Schwarzschild Orbit Modeling: Robustness of Parameter Inference for Masses and Shapes of Triaxial Galaxies with Supermassive Black Holes

Abstract: Evidence for the majority of the supermassive black holes in the local universe has been obtained dynamically from stellar motions with the Schwarzschild orbit superposition method. However, there have been only a handful of studies using simulated data to examine the ability of this method to reliably recover known input black hole masses $M_{BH}$ and other galaxy parameters. Here we conduct a comprehensive assessment of the reliability of the triaxial Schwarzschild method at $\textit{simultaneously}$ determining $M_{BH}$, stellar mass-to-light ratio $M^{*}/L$, dark matter mass, and three intrinsic triaxial shape parameters of simulated galaxies. For each of 25 rounds of mock observations using simulated stellar kinematics and the $\texttt{TriOS}$ code, we derive best-fitting parameters and confidence intervals after a full search in the 6D parameter space with our likelihood-based model inference scheme. The two key mass parameters, $M_{BH}$ and $M^{*}/L$, are recovered within the 68% confidence interval, and other parameters are recovered between 68% and 95% confidence intervals. The spatially varying velocity anisotropy of the stellar orbits is also well recovered. We explore whether the goodness-of-fit measure used for galaxy model selection in our pipeline is biased by variable complexity across the 6D parameter space. In our tests, adding a penalty term to the likelihood measure either makes little difference, or worsens the recovery in some cases.