arXiv subjects

David A. Simon

Publications and source records attributed to David A. Simon.

4 recordsLinked to original sources

arXiv2026

The Impact of Non-Gaussian Line Spread Functions on Stellar Kinematic Recovery: Consequences for Dynamical Models

The line spread function (LSF) of a spectrograph encodes the inherent broadening of a single spectral line. It is typically reported as a single number, the resolving power $R = \lambda/\Delta\lambda$ with $\Delta \lambda$ the FWHM of the LSF. In standard pipelines for extracting stellar kinematics the LSF is assumed to be a wavelength dependent Gaussian. However, detailed LSF measurements from real integral field spectrographs reveal a variety of shapes, some close to Gaussian, others with large wings or that appear boxy. I have studied the impact that these non-Gaussian LSF profiles have on the recovery of the stellar kinematics of a mock spectrum based on MUSE ($\sigma_{\rm inst} = 51 $ km s$^{-1}$) and find that even in the high dispersion case of 300 km s$^{-1}$, there is up to a 7 percent bias in the dispersion due to non-Gaussian LSF profiles. Additionally, higher order Gauss-Hermite moments $h_3$ and $h_4$ can be biased by up to $\pm$0.1. To resolve this bias, I developed a method to match the LSF of the template spectra to the LSF of a target spectrum when the LSF of either one or both is non-Gaussian and show that it can reduce bias in the dispersion to less than a percent down to the instrumental resolution. A Python implementation of this method has been made publicly available.

astro-ph.GA↗

arXiv2026

Dynamical Modelling of Galactic Kinematics using Neural Networks

The advent of integral field data has revolutionised the study of galaxy evolution. A key component of this is dynamical modelling methods which have allowed for crucial insights to be made from kinematic data. Despite this importance, most dynamical models make a number of key assumptions which do not hold for real galaxies. These include assumptions about the geometry (axisymmetry or triaxiality), the shape of the velocity ellipsoid, and the shape of the underlying stellar distribution. At the same time, machine learning methods are becoming increasingly powerful, with many applications appearing in astronomy. As a first step towards building new dynamical modelling methods with machine learning, it is important to understand the types of machine learning architectures that are best fit for dynamical modelling. To investigate this, we construct a training set of dynamical models of early-type galaxies using Jeans Anisotropic Modelling (JAM). We then train a neural network on this data using the parameters of JAM and mock photometry as the input. We are able to accurately model JAM galaxies with relatively simple machine learning architectures, leading to a significant speed increase over traditional JAM modelling.

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arXiv2025

MaNGA DynPop. VII. A Unified Bulge-Disk-Halo Model for Explaining Diversity in Circular Velocity Curves of 6000 Spiral and Early-Type Galaxies

We derive circular velocity curves (CVCs) from stellar dynamical models for $\sim6000$ nearby galaxies in the final data release of the Sloan Digital Sky Survey-IV MaNGA survey with integral-field spectroscopy, exploring connections between the inner gravitational potential (traced by CVC amplitude/shape) and galaxy properties. The maximum circular velocity ($V_{\rm circ}^{\rm max}$) and circular velocity at the half-light radius ($V_{\rm circ}(R_{\rm e}^{\rm maj})$) both scale linearly with the stellar second velocity moment $\sigma_{\rm e}^2\equiv\langle V^2+\sigma^2\rangle$ within the half-light isophote, following $V_{\rm circ}^{\rm max} \approx 1.72\sigma_{\rm e}$ (7$\%$ error) and $V_{\rm circ}(R_{\rm e}^{\rm maj}) \approx 1.62\sigma_{\rm e}$ (7$\%$ error). CVC shapes (rising, flat, declining) correlate strongly with structural and stellar population properties: declining curves dominate in massive, early-type, bulge-dominated galaxies with old, metal-rich stars and early quenching, while rising CVCs prevail in disk-dominated systems with younger stellar populations and ongoing star formation. Using a unified bulge-disk-halo model, we predict CVC shapes with minimal bias, identifying three governing parameters: bulge-to-total mass ratio ($B/T$), dark matter fraction within $R_{\rm e}$, and bulge Sersic index. The distribution of CVC shapes across the mass-size plane reflects evolutionary pathways driven by (i) in situ star formation (spurring bulge growth) and (ii) dry mergers. This establishes CVC morphology as a diagnostic for galaxy evolution, linking dynamical signatures to structural and stellar population histories.

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arXiv2023

Supermassive black hole mass in the massive elliptical galaxy M87 from integral-field stellar dynamics using OASIS and MUSE with adaptive optics: assessing systematic uncertainties

The massive elliptical galaxy M87 has been the subject of several supermassive black hole mass measurements from stellar dynamics, gas dynamics, and recently the black hole shadow by the Event Horizon Telescope (EHT). This uniquely positions M87 as a benchmark for alternative black hole mass determination methods. Here we use stellar kinematics extracted from integral-field spectroscopy observations with Adaptive Optics (AO) using MUSE and OASIS. We exploit our high-resolution integral field spectroscopy to spectrally decompose the central AGN from the stars. We derive an accurate inner stellar-density profile and find it is flatter than previously assumed. We also use the spectrally-extracted AGN as a reference to accurately determine the observed MUSE and OASIS AO PSF. We then perform Jeans Anisotropic Modelling (JAM), with a new flexible spatially-variable anisotropy, and measure the anisotropy profile, stellar mass-to-light variations, inner dark matter fraction, and black hole mass. Our preferred black hole mass is $M_{\rm BH}=(8.7\pm1.2 [\text{random}] \pm1.3 [\text{systematic}]) \times 10^9 \ M_\odot $. However, using the inner stellar density from previous studies, we find a preferred black hole mass of $M_{\rm BH} = (5.5^{+0.5}_{-0.3}) \times 10^9 \ M_\odot $, consistent with previous work. We find that this is the primary cause of the difference between our results and previous work, in addition to smaller contributions due to kinematics and modelling method. We conduct numerous systematic tests of the kinematics and model assumptions and conclude that uncertainties in the black hole mass of M87 from previous determinations may have been underestimated and further analyses are needed.

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