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From global to spatially resolved in low-redshift galaxies

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arxiv 2009.00424 v1 pith:EPA7RXLW submitted 2020-09-01 astro-ph.GA

classification astro-ph.GA
keywords galaxiesparticularresolvedspatiallyevolutiongalaxynatureproperties
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Our understanding of the structure, composition and evolution of galaxies has strongly improved in the last decades, mostly due to new results based on large spectroscopic and imaging surveys. In particular, the nature of ionized gas, its ionization mechanisms, its relation with the stellar properties and chemical composition, the existence of scaling relations that describe the cycle between stars and gas, and the corresponding evolution patterns have been widely explored and described. More recently, the introduction of additional techniques, in particular Integral Field Spectroscopy, and their use in large galaxy surveys, have forced us to re-interpret most of those recent results from a spatially resolved perspective. This review is aimed to complement recent efforts to compile and summarize this change of paradigm in the interpretation of galaxy evolution. In particular we cover three particular aspects not fully covered in detail in recent reviews: (i) the spatially resolved nature of the ionization properties in galaxies and the confusion introduced by considering just integrated quantities; (ii) the nature of the global scaling relations and their relations with the spatially resolved ones; and (iii) the dependence of the radial gradients and characteristic properties of the stellar populations and ionized gas on stellar mass and galaxy morphology. To this end we replicate published results, and present novel ones, based on the largest compilation of IFS data of galaxies in the nearby universe to date.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. The PyKOALA python library: a multi-instrument package for IFS data reduction

    astro-ph.IM 2025-07 conditional novelty 5.0 of 10

    PyKOALA is a modular, instrument-agnostic Python framework for IFS data reduction, currently applied to KOALA+AAOmega, but lacking quantitative performance validation in this paper.

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