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In this work, we present a methodology and a corresponding code-base for constructing mock integral field spectrograph (IFS) observations of simulated galaxies in a consistent and reproducible way. Such methods are necessary to improve the collaboration and comparison of observation and theory results, and accelerate our understanding of how the kinematics of galaxies evolve over time. This code, SimSpin, is an open-source package written in R, but also with an API interface such that the code can be interacted with in any coding language. Documentation and individual examples can be found at the open-source website connected to the online repository. SimSpin is already being utilised by international IFS collaborations, including SAMI and MAGPI, for generating comparable data sets from a diverse suite of cosmological hydrodynamical simulations., Comment: 20 pages, 15 figures, 2 tables. Accepted for publication in PASA. 30/08/23

In this paper we introduce the software package ProPane, written for the R data analysis language. ProPane combines the full range of wcslib projections with the C++ image manipulation routines provided by the CImg library. ProPane offers routines for image warping and combining (including stacking), and various related tasks such as image alignment tweaking and pixel masking. It can stack an effectively unlimited number of target frames using multiple parallel cores, and offers threading for many lower level routines. It has been used for a number of current and upcoming large surveys, and we present a range of its capabilities and features. ProPane is already available under a permissive open-source LGPL-3 license at github.com/asgr/ProPane (DOI: 10.5281/zenodo.10057053)., Comment: 19 pages, 17 figures, 5 tables, accepted to MNRAS

We present ProFit, a new code for Bayesian two-dimensional photometric galaxy profile modelling. ProFit consists of a low-level C++ library (libprofit), accessible via a command-line interface and documented API, along with high-level R (ProFit) and Python (PyProFit) interfaces (available at github.com/ICRAR/ libprofit, github.com/ICRAR/ProFit, and github.com/ICRAR/pyprofit respectively). R ProFit is also available pre-built from CRAN, however this version will be slightly behind the latest GitHub version. libprofit offers fast and accurate two- dimensional integration for a useful number of profiles, including Sersic, Core-Sersic, broken-exponential, Ferrer, Moffat, empirical King, point-source and sky, with a simple mechanism for adding new profiles. We show detailed comparisons between libprofit and GALFIT. libprofit is both faster and more accurate than GALFIT at integrating the ubiquitous Serrsic profile for the most common values of the Serrsic index n (0.5 < n < 8). The high-level fitting code ProFit is tested on a sample of galaxies with both SDSS and deeper KiDS imaging. We find good agreement in the fit parameters, with larger scatter in best-fit parameters from fitting images from different sources (SDSS vs KiDS) than from using different codes (ProFit vs GALFIT). A large suite of Monte Carlo-simulated images are used to assess prospects for automated bulge-disc decomposition with ProFit on SDSS, KiDS and future LSST imaging. We find that the biggest increases in fit quality come from moving from SDSS- to KiDS-quality data, with less significant gains moving from KiDS to LSST., Comment: 32 pages, 34 Figures, accepted for publication in MNRAS

We present ProFit, a new code for Bayesian two-dimensional photometric galaxy profile modelling. ProFit consists of a low-level C++ library (libprofit), accessible via a command-line interface and documented API, along with high-level R (ProFit) and Python (PyProFit) interfaces (available at github.com/ICRAR/ libprofit, github.com/ICRAR/ProFit, and github.com/ICRAR/pyprofit respectively). R ProFit is also available pre-built from CRAN, however this version will be slightly behind the latest GitHub version. libprofit offers fast and accurate two- dimensional integration for a useful number of profiles, including Sersic, Core-Sersic, broken-exponential, Ferrer, Moffat, empirical King, point-source and sky, with a simple mechanism for adding new profiles. We show detailed comparisons between libprofit and GALFIT. libprofit is both faster and more accurate than GALFIT at integrating the ubiquitous Serrsic profile for the most common values of the Serrsic index n (0.5 < n < 8). The high-level fitting code ProFit is tested on a sample of galaxies with both SDSS and deeper KiDS imaging. We find good agreement in the fit parameters, with larger scatter in best-fit parameters from fitting images from different sources (SDSS vs KiDS) than from using different codes (ProFit vs GALFIT). A large suite of Monte Carlo-simulated images are used to assess prospects for automated bulge-disc decomposition with ProFit on SDSS, KiDS and future LSST imaging. We find that the biggest increases in fit quality come from moving from SDSS- to KiDS-quality data, with less significant gains moving from KiDS to LSST., Comment: 32 pages, 34 Figures, accepted for publication in MNRAS

We investigate the large scale metallicity distribution in the Galactic bulge, using a large spatial coverage, in order to constrain the bulge formation scenario. We use the VISTA variables in the Via Lactea (VVV) survey data and 2MASS photometry, covering 320 sqdeg of the Galactic bulge, to derive photometric metallicities by interpolating of the (J-Ks)0 colors of individual Red Giant Branch stars based on a set of globular cluster ridge lines. We then use this information to construct the first global metallicity map of the bulge with a resolution of 30'x45'. The metallicity map of the bulge revealed a clear vertical metallicity gradient of ~0.04 dex/deg (~0.28 dex/kpc), with metal-rich stars ([Fe/H]~0) dominating the inner bulge in regions closer to the galactic plane (|b|<5). At larger scale heights, the mean metallicity of the bulge population becomes significantly more metal-poor. This fits in the scenario of a boxy-bulge originated from the vertical inestability of the Galactic bar, formed early via secular evolution of a two component stellar disk. Older, metal-poor stars dominate at higher scale heights due to the non-mixed orbits from the originally hotter thick disk stars., Comment: Accepted for publication in A&A

We investigate the large scale metallicity distribution in the Galactic bulge, using a large spatial coverage, in order to constrain the bulge formation scenario. We use the VISTA variables in the Via Lactea (VVV) survey data and 2MASS photometry, covering 320 sqdeg of the Galactic bulge, to derive photometric metallicities by interpolating of the (J-Ks)0 colors of individual Red Giant Branch stars based on a set of globular cluster ridge lines. We then use this information to construct the first global metallicity map of the bulge with a resolution of 30'x45'. The metallicity map of the bulge revealed a clear vertical metallicity gradient of ~0.04 dex/deg (~0.28 dex/kpc), with metal-rich stars ([Fe/H]~0) dominating the inner bulge in regions closer to the galactic plane (|b|<5). At larger scale heights, the mean metallicity of the bulge population becomes significantly more metal-poor. This fits in the scenario of a boxy-bulge originated from the vertical inestability of the Galactic bar, formed early via secular evolution of a two component stellar disk. Older, metal-poor stars dominate at higher scale heights due to the non-mixed orbits from the originally hotter thick disk stars., Comment: Accepted for publication in A&A

We use the Vista Variables in the Via Lactea (VVV) ESO public survey data to measure extinction values in the complete area of the Galactic bulge covered by the survey at high resolution. We derive reddening values using the method described in Paper I. This is based on measuring the mean (J-Ks) color of red clump giants in small subfields of 2' to 6' in the following bulge area: -10.3

We use the Vista Variables in the Via Lactea (VVV) ESO public survey data to measure extinction values in the complete area of the Galactic bulge covered by the survey at high resolution. We derive reddening values using the method described in Paper I. This is based on measuring the mean (J-Ks) color of red clump giants in small subfields of 2' to 6' in the following bulge area: -10.3