Extremely Large Telescope

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.