http://arxiv.org/abs/2208.05489
This paper approaches the inverse problem of extracting the stellar population content of galaxy spectra from a basic standpoint based on information theory. By interpreting spectra as probability distribution functions, we find that galaxy spectra have high entropy, caused by the high correlatedness in wavelength space. The highest variation in entropy is unsurprisingly found in regions that have been well studied for decades with the conventional approach. Therefore, we target a set of six spectral regions that show the highest variation in entropy – the 4,000 Angstrom 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 AGN galaxies, and show – independently of any physical model – that AGN spectra represent 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 affect traditional methods that compare models with observations, as well as machine learning and deep learning algorithms that rely on the statistical properties of the data to assess the variations among spectra. Therefore, caution must be exercised when retrieving detailed population parameters or even star formation histories from galaxy spectra.
I. Ferreras, O. Lahav, R. Somerville, et. al.
Fri, 12 Aug 22
44/48
Comments: 11 pages, 12 figures. Comments welcome