The ALHAMBRA survey: 2-D analysis of the stellar populations in massive early-type galaxies at z < 0.3
I. San Roman, A. J. Cenarro, L. A. D\'iaz-Garc\'ia, C., L\'opez-Sanjuan, J. Varela, R. M. Gonz\'alez Delgado, P., S\'anchez-Bl\'azquez, E. J. Alfaro, B. Ascaso, S. Bonoli, A. Borlaff, F. J., Castander, M. Cervi\~no, A. Fern\'andez-Soto, I. M\'arquez, J. Masegosa, D., Muniesa

TL;DR
This paper introduces a cost-effective photometric technique to analyze stellar population gradients in early-type galaxies, producing detailed 2D maps and radial profiles that align with previous spectroscopic findings.
Contribution
The authors develop a novel multi-filter photometric method for spatially resolved stellar population analysis, validated on ALHAMBRA survey data, enabling large sample studies beyond traditional spectroscopy.
Findings
Gradients of age are generally flat in early-type galaxies.
Metallicity gradients are negative, indicating decreasing metallicity outward.
Extinction gradients are mostly flat with significant variation.
Abstract
We present a technique that permits the analysis of stellar population gradients in a relatively low cost way compared to IFU surveys analyzing a vastly larger samples as well as out to larger radii. We developed a technique to analyze unresolved stellar populations of spatially resolved galaxies based on photometric multi-filter surveys. We derived spatially resolved stellar population properties and radial gradients by applying a Centroidal Voronoi Tesselation and performing a multi-color photometry SED fitting. This technique has been applied to a sample of 29 massive (M > 10 M), early-type galaxies at < 0.3 from the ALHAMBRA survey. We produced detailed 2D maps of stellar population properties (age, metallicity and extinction). Radial structures have been studied and luminosity-weighted and mass-weighted gradients have been derived out to 2 - 3.5…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
