Panchromatic Photometry of Low-redshift, Massive Galaxies Selected from SDSS Stripe 82

Authors: Yang A. Li, Luis C. Ho, Jinyi Shangguan, Ming-Yang Zhuang, Ruancun Li

2023ApJS..267...17L
arXiv: 2307.13461v1 - DOI (astro-ph.GA)
36 pages, 24 figures
License: CC BY 4.0

Results of the summarizing process for the arXiv paper: 2307.13461v1

  • Comprehensive Summary
  • Key points
  • Layman's Summary
  • Blog article
This study presents a comprehensive catalog of panchromatic photometry for 2685 low-redshift, massive galaxies selected from the Stripe 82 region of the Sloan Digital Sky Survey. The authors cover 17 bands ranging from the far-ultraviolet to 500 µm in order to derive robust measurements for stellar mass, star formation rate (SFR), dust content and possible contribution from nonstellar sources. To obtain accurate photometry, they develop a hybrid approach that combines matched-aperture photometry with profile-fitting photometry. This approach takes advantage of deep optical coadded images and is optimized for resolving bright nearby galaxies. Challenges such as deblending interacting/merging galaxies and sources in crowded regions as well as contamination by foreground stars are addressed. The derived SEDs are used to estimate stellar mass, SFR and dust mass through spectral energy distribution fitting. Corrections for systematic uncertainties based on extensive mock tests are applied to account for flux upper limits for undetected photometric bands. Comparisons with published catalogs show good agreement for stellar masses but discrepancies arise when comparing SFR measurements for galaxies in and below the green valley. This study emphasizes the importance of incorporating accurate and inclusive photometry into SED analysis and highlights the need for caution when interpreting SFRs of galaxies with moderate to weak star formation activity. The presented catalog provides valuable data for future studies on galaxy evolution and fundamental parameters.
AI-generated keywords: SED photometry stellar mass star formation rate dust content

AI-generated Key Points

  • Comprehensive catalog of panchromatic photometry for 2685 low-redshift, massive galaxies
  • Covers 17 bands ranging from far-ultraviolet to 500 µm
  • Derives measurements for stellar mass, star formation rate (SFR), dust content, and nonstellar sources
  • Develops a hybrid approach combining matched-aperture photometry with profile-fitting photometry
  • Addresses challenges such as deblending interacting/merging galaxies and contamination by foreground stars
  • Uses spectral energy distribution fitting to estimate stellar mass, SFR, and dust mass
  • Applies corrections for systematic uncertainties based on extensive mock tests
  • Good agreement with published catalogs for stellar masses but discrepancies in SFR measurements below the green valley
  • Emphasizes the importance of accurate and inclusive photometry in SED analysis
  • Caution needed when interpreting SFRs of galaxies with moderate to weak star formation activity
  • Provides valuable data for future studies on galaxy evolution and fundamental parameters
Also access our AI generated: Comprehensive summary, Lay summary, Blog-like article; or ask questions about this paper to our AI assistant.

Abstract: The broadband spectral energy distribution of a galaxy encodes valuable information on its stellar mass, star formation rate (SFR), dust content, and possible fractional energy contribution from nonstellar sources. We present a comprehensive catalog of panchromatic photometry, covering 17 bands from the far-ultraviolet to 500 $\mu$m, for 2685 low-redshift (z=0.01-0.11), massive ($M_* > 10^{10}\,M_\odot$) galaxies selected from the Stripe 82 region of the Sloan Digital Sky Survey, one of the largest areas with relatively deep, uniform observations over a wide range of wavelengths. Taking advantage of the deep optical coadded images, we develop a hybrid approach for matched-aperture photometry of the multi-band data. We derive robust uncertainties and upper limits for undetected galaxies, deblend interacting/merging galaxies and sources in crowded regions, and treat contamination by foreground stars. We perform spectral energy distribution fitting to derive the stellar mass, SFR, and dust mass, critically assessing the influence of flux upper limits for undetected photometric bands and applying corrections for systematic uncertainties based on extensive mock tests. Comparison of our measurements with those of commonly used published catalogs reveals good agreement for the stellar masses. While the SFRs of galaxies on the star-forming main sequence show reasonable consistency, galaxies in and below the green valley show considerable disagreement between different sets of measurements. Our analysis suggests that one should incorporate the most accurate and inclusive photometry into the spectral energy distribution analysis, and that care should be exercised in interpreting the SFRs of galaxies with moderate to weak star formation activity.

Submitted to arXiv on 25 Jul. 2023

Tools for this paper

Explain it differently 3 free rewrites left today
Export BibTeX · RIS (Zotero, Mendeley, EndNote) · Markdown (Notion, Obsidian) · Premium
Citation alerts Premium
Journal club Slides to present it Premium
For journalists Press kit and hype check Premium

Ask questions about this paper to our AI assistant

You can also chat with multiple papers at once here.

Figures and plots

Ask about a figure by number or use its question button to request visual analysis. Up to two figures and four image panels per question. Values read from plots are approximate; check the original data.

AI assistant instructions?

Assess the quality of the AI-generated content by voting

Score: 0

Why do we need votes?

Votes are used to determine whether we need to re-run our summarizing tools. If the count reaches -10, our tools can be restarted.

The previous summary was created more than a year ago and can be re-run (if necessary) by clicking on the Run button below.

Similar papers summarized with our AI tools

Navigate through even more similar papers through a

tree representation

Disclaimer: The AI-based summarization tool and virtual assistant provided on this website may not always provide accurate and complete summaries or responses. We encourage you to carefully review and evaluate the generated content to ensure its quality and relevance to your needs.