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Accuracy and precision of triaxial orbit models I: SMBH mass, stellar mass, and dark-matter halo

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Rantala,  A.
Galaxy Formation, Cosmology, MPI for Astrophysics, Max Planck Society;

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Citation

Neureiter, B., de Nicola, S., Thomas, J., Saglia, R., Bender, R., & Rantala, A. (2023). Accuracy and precision of triaxial orbit models I: SMBH mass, stellar mass, and dark-matter halo. Monthly Notices of the Royal Astronomical Society, 519(2), 2004-2016. doi:10.1093/mnras/stac3652.


Cite as: https://hdl.handle.net/21.11116/0000-000D-1AC2-B
Abstract
We investigate the accuracy and precision of triaxial dynamical orbit models by fitting 2D mock observations of a realistic N-body merger simulation resembling a massive early-type galaxy with a supermassive black hole. We show that we can reproduce the triaxial N-body merger remnant’s correct black hole mass, stellar mass-to-light ratio and total enclosed mass (inside the half-light radius) for several different tested orientations with an unprecedented accuracy of 5–10 per cent. Our dynamical models use the entire non-parametric line-of-sight velocity distribution (LOSVD) rather than parametric LOSVDs or velocity moments as constraints. Our results strongly suggest that state-of-the-art integral-field projected kinematic data contain only minor degeneracies with respect to the mass and anisotropy recovery. Moreover, this also demonstrates the strength of the Schwarzschild method in general. We achieve the proven high recovery accuracy and precision with our newly developed modelling machinery by combining several advancements: (i) our new semiparametric deprojection code probes degeneracies and allows us to constrain the viewing angles of a triaxial galaxy; (ii) our new orbit modelling code SMART uses a 5-dim orbital starting space to representatively sample in particular near-Keplerian orbits in galaxy centres; (iii) we use a generalized information criterion AICp to optimize the smoothing and to compare different mass models to avoid biases that occur in χ2-based models with varying model flexibilities.