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Abstract:

The stellar velocity dispersion ( σ ) of massive elliptical galaxies is a key ingredient in breaking the mass-sheet degeneracy and obtaining precise and accurate cosmography from gravitational time delays. The relative uncertainty on the Hubble constant H 0 is double the relative error on σ . Therefore, time-delay cosmography imposes much more demanding requirements on the precision and accuracy of σ than galaxy studies. While precision can be achieved with an adequate signal-to-noise ratio (S/N), the accuracy critically depends on key factors such as the elemental abundance and temperature of stellar templates, flux calibration, and wavelength ranges. We carried out a detailed study of the problem using multiple sets of galaxy spectra of massive elliptical galaxies with S/N ∼ 30–160 Å −1 , along with state-of-the-art empirical and semi-empirical stellar libraries and stellar population synthesis templates. We show that the choice of stellar library is generally the dominant source of residual systematic errors. We propose a general recipe for mitigating and accounting for residual uncertainties. We show that a sub-percent level of accuracy can be achieved on individual spectra with our data quality, which we subsequently validated with simulated mock datasets. The covariance between velocity dispersions measured for a sample of spectra can also be reduced to sub-percent levels. We recommend this recipe for all applications that require high precision and accurate stellar kinematics. Thus, we have made all the software publicly available to facilitate its implementation. This recipe will also be used in future TDCOSMO collaboration papers.

Abstract:

We present combined James Webb Space Telescope (JWST) NIRSpec and MIRI/MRS integral field spectroscopy data of the nuclear and circumnuclear regions of the highly dust obscured Seyfert 2 galaxy NGC 7582, which is part of the sample of active galactic nucleaus (AGN) in the Galaxy Activity, Torus and Outflow Survey (GATOS). Spatially resolved analysis of the pure rotational H lines (S(1)–S(7)) reveals a characteristic power-law temperature distribution in different apertures, with the two prominent southern star-forming regions exhibiting unexpectedly high molecular gas temperatures, comparable to those in the AGN powered nuclear region. We investigate potential heating mechanisms including direct AGN photoionization, UV fluorescent excitation from young star clusters, and shock excitation. We find that shock heating gives the most plausible explanation, consistent with multiple near- and mid-IR tracers and diagnostics. Using photoionization models from the PhotoDissociation Region Toolbox, we quantify the ISM conditions in the different regions, determining that the southern star-forming regions have a high density ( cm) and are irradiated by a moderate UV radiation field ( Habing). Fitting a suite of Paris-Durham shock models to the rotational H lines, as well as rovibrational 1-0 S(1), 1-0 S(2), and 2-1 S(1) H emission lines, we find that a slow ( km s−1) C-type shock is likely responsible for the elevated temperatures. Our analysis loosely favours local starburst activity as the driver of the shocks and circumnuclear gas dynamics in NGC 7582, though the possibility of an AGN jet contribution cannot be excluded.

Abstract:

Adaptive binning is a crucial step in the analysis of large astronomical data sets, such as those from integral-field spectroscopy, to ensure a sufficient signal-to-noise ratio () for reliable model fitting. However, the widely used Voronoi-binning method and its variants suffer from two key limitations: they scale poorly with data size, often as , creating a computational bottleneck for modern surveys, and they can produce undesirable non-convex or disconnected bins. I introduce PowerBin, a new algorithm that overcomes these issues. I frame the binning problem within the theory of optimal transport, for which the solution is a Centroidal Power Diagram (CPD), guaranteeing convex bins. Instead of formal CPD solvers, which are unstable with real data, I develop a fast and robust heuristic based on a physical analogy of packed soap bubbles. This method reliably enforces capacity constraints even for non-additive measures like with correlated noise. I also present a new bin-accretion algorithm with complexity, removing the previous bottleneck. The combined PowerBin algorithm scales as , making it about two orders of magnitude faster than previous methods on million-pixel data sets. I demonstrate its performance on a range of simulated and real data, showing it produces high-quality, convex tessellations with excellent uniformity. The public python implementation provides a fast, robust, and scalable tool for the analysis of modern astronomical data.