Connor Stone

This paper presents a study of the impact of supermassive black hole (SMBH) feedback on dark matter (DM) haloes in numerical NIHAO (Numerical Investigation of a Hundred Astrophysical Object) simulations of galaxies. In particular, the amount of DM displaced via active galactic nuclei (AGNs) feedback and the physical scale over which AGN feedback affects the DM halo are quantified by comparing NIHAO simulations with and without AGN feedback. NIHAO galaxies with log (M*/M⊙) ≥ 10.0 show a growing central DM suppression of 0.2 dex (∼40 per cent) from z = 1.5 to the present relative to no AGN feedback simulations. The growth of the DM suppression is related to the mass evolution of the SMBH and the gas mass in the central regions. For the most massive NIHAO galaxies with log (M*/M⊙) > 10.5, partially affected by numerical resolution, the central DM suppression peaks at z = 0.5 after which halo contraction overpowers AGN feedback due a shortage of gas and thus, SMBH growth. The spatial scale, or ‘sphere of influence’, over which AGN feedback affects the DM distribution decreases as a function of time for Milky Way-mass galaxies (from ∼16 kpc at z = 1.5 to ∼7.8 kpc at z = 0) as a result of halo contraction due to stellar growth. For the most massive NIHAO galaxies, the size of the sphere of influence remains constant (∼16 kpc) for z > 0.5 owing to the balance between AGN feedback and halo contraction.

We present astrophot, a fast, powerful, and user-friendly python based astronomical image photometry solver. astrophot incorporates automatic differentiation and graphics processing unit (GPU), or parallel central processing unit (CPU), acceleration, powered by the machine learning library pytorch. Everything: astrophot can fit models for sky, stars, galaxies, point spread functions (PSFs), and more in a principled χ2 forward optimization, recovering Bayesian posterior information and covariance of all parameters. Everywhere: astrophot can optimize forward models on CPU or GPU; across images that are large, multiband, multi-epoch, rotated, dithered, and more. All at once: The models are optimized together, thus handling overlapping objects and including the covariance between parameters (including PSF and galaxy parameters). A number of optimization algorithms are available including Levenberg–Marquardt, Gradient descent, and No-U-Turn Markov chain Monte Carlo sampling. With an object-oriented user interface, astrophot makes it easy to quickly extract detailed information from complex astronomical data for individual images or large survey programs. This paper outlines novel features of the astrophot code and compares it to other popular astronomical image modelling software. astrophot is open-source, fully python based, and freely accessible at https://github.com/Autostronomy/AstroPhot .