Feedback likely plays a crucial role in resolving discrepancies between observations and theoretical predictions of dwarf galaxy properties. Stellar feedback was once believed to be sufficient to explain these discrepancies, but it has thus far failed to fully reconcile theory and observations. The recent discovery of energetic galaxy-wide outflows in dwarf galaxies hosting active galactic nuclei (AGNs) suggests that AGN feedback may have a larger role in the evolution of dwarf galaxies than previously suspected. In order to assess the relative importance of stellar versus AGN feedback in these galaxies, we perform a detailed Keck/KCWI optical integral field spectroscopic study of a sample of low-redshift star-forming (SF) dwarf galaxies that show outflows in ionized gas in their Sloan Digital Sky Survey spectra. We characterize the outflows and compare them to observations of AGN-driven outflows in dwarfs. We find that SF dwarfs have outflow components that have comparable widths (
The inverse problem of extracting the stellar population content of galaxy spectra is analysed here from a basic standpoint based on information theory. By interpreting spectra as probability distribution functions, we find that galaxy spectra have high entropy, thus leading to a rather low effective information content. The highest variation in entropy is unsurprisingly found in regions that have been well studied for decades with the conventional approach. We target a set of six spectral regions that show the highest variation in entropy – the 4000 Å break being the most informative one. As a test case with real data, we measure the entropy of a set of high-quality spectra from the Sloan Digital Sky Survey, and contrast entropy-based results with the traditional method based on line strengths. The data are classified into star-forming (SF), quiescent (Q), and active galactic nucleus (AGN) galaxies, and show – independently of any physical model – that AGN spectra can be interpreted as a transition between SF and Q galaxies, with SF galaxies featuring a more diverse variation in entropy. The high level of entanglement complicates the determination of population parameters in a robust, unbiased way, and affects traditional methods that compare models with observations, as well as machine learning (especially deep learning) algorithms that rely on the statistical properties of the data to assess the variations among spectra. Entropy provides a new avenue to improve population synthesis models so that they give a more faithful representation of real galaxy spectra.
more » « less- NSF-PAR ID:
- 10398362
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- RAS Techniques and Instruments
- Volume:
- 2
- Issue:
- 1
- ISSN:
- 2752-8200
- Page Range / eLocation ID:
- p. 78-90
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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