Keck Integral-field Spectroscopy of M87 Reveals an Intrinsically Triaxial Galaxy and a Revised Black Hole Mass

Abstract

The three-dimensional intrinsic shape of a galaxy and the mass of the central supermassive black hole provide key insight into the galaxy’s growth history over cosmic time. Standard assumptions of a spherical or axisymmetric shape can be simplistic and can bias the black hole mass inferred from the motions of stars within a galaxy. Here, we present spatially resolved stellar kinematics of M87 over a two-dimensional 250″ × 300″ contiguous field covering a radial range of 50 pc–12 kpc from integral-field spectroscopic observations at the Keck II Telescope. From about 5 kpc and outward, we detect a prominent 25 km s−1 rotational pattern, in which the kinematic axis (connecting the maximal receding and approaching velocities) is 40° misaligned with the photometric major axis of M87. The rotational amplitude and misalignment angle both decrease in the inner 5 kpc. Such misaligned and twisted velocity fields are a hallmark of triaxiality, indicating that M87 is not an axisymmetrically shaped galaxy. Triaxial Schwarzschild orbit modeling with more than 4000 observational constraints enabled us to determine simultaneously the shape and mass parameters. The models incorporate a radially declining profile for the stellar mass-to-light ratio suggested by stellar population studies. We find that M87 is strongly triaxial, with ratios of p = 0.845 for the middle-to-long principal axes and q = 0.722 for the short-to-long principal axes, and determine the black hole mass to be ( 5.37 − 0.25 + 0.37 ± 0.22 ) × 10 9 M ⊙ , where the second error indicates the systematic uncertainty associated with the distance to M87.

Dynamical Stellar Mass-to-light Ratio Gradients: Evidence for Very Centrally Concentrated IMF Variations in ETGs?

“…From our own work, we include axisymmetric Schwarzschild modeling results for the massive ETGs NGC 1600 Thomas et al (2016) and Holm 15A Mehrgan et al (2019), which were both noted for their particularly massive SMBHs, as well as NGC 5419, which was modeled with our new triaxial modeling code SMART (Neureiter et al 2021). Moreover, we add results from triaxial Schwarzschild modeling of NGC 1453 from Quenneville et al (2022), using the σ e value from Veale et al (2018), as well as triaxial models for M87 from Liepold et al (2023). Finally, we add seven more axisymmetric Schwarzschild measurements for low-mass fast-rotating ETGs from Thater et al (2019) and Thater et al (2022).…”

Formation of Limb-brightened Radio Jets by Angle-dependent Energy Extraction from Rapidly Rotating Black Holes

“…As an example, we apply the present method to the M87 jet and adopt a BH mass 6.4 × 10 9 M e (Gebhardt et al 2011;Event Horizon Telescope Collaboration et al 2019;Liepold et al 2023) and the luminosity distance 16.7 Mpc. An angular scale of 1 mas corresponds to a spatial scale 140R S , where R S ≡ 2GMc −2 = 2M denotes the Schwarzschild radius.…”

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

“…For each of the five realizations for a given galaxy model in Table 1, we treat the mock galaxy's projected kinematics as simulated data and perform a 6D parameter space search following the grid-free procedure we have developed for recent analyses of real data (e.g., Pilawa et al 2022;Quenneville et al 2022;Liepold et al 2023). In this procedure, a grid-free Latin hypercube scheme is used to choose sampling points in the galaxy model parameter space (about 3000 models in this work).…”

“…To mimic the procedure used in orbit modeling of real data, we do not use regularization in this step (see discussion in Section 2.2). The metric, hereafter referred to as the log-likelihood, is given by Adopting the parameter inference procedure used in our prior work (Pilawa et al 2022;Quenneville et al 2022;Liepold et al 2023), we first construct an interpolated log-likelihood surface from the discrete set of evaluated models using Gaussian process regression (Rasmussen & Williams 2005) with a Matérn covariance kernel with ν = 3/2. The resulting Gaussian process mean function is used as a smooth surrogate function for the true log-likelihood surface.…”