---
title: DESI Legacy Imaging Surveys Overview
url: https://www.emergentmind.com/topics/desi-legacy-imaging-surveys-desi-ls
type: topic
---

# DESI Legacy Imaging Surveys Overview

The DESI Legacy Imaging Surveys (DESI-LS) provide a quantitative framework for estimating stellar masses of galaxies based on deep, wide-field optical imaging in the $g$, $r$, and $z$ bands. Leveraging calibration against robust mid-infrared stellar mass measurements from the S$^4$G survey, a simple, linear prescription has been derived for rapid and automated stellar mass estimates from DESI-LS data. This methodology supports multiple scientific applications—from statistical galaxy evolution studies to construction of large, homogeneous galaxy catalogs—while emphasizing scalability and reproducibility suitable for modern extragalactic research.

## 1. Formula Derivation and Calibration

The core of the method is a two-parameter model that links the DESI-LS $g$ and $r$ absolute magnitudes to stellar mass. Integrated optical magnitudes are obtained by fitting DESI-LS images with GALFIT, producing extinction-corrected, distance-calibrated photometry. Comparison against the high-fidelity S$^4$G stellar masses (derived from Spitzer 3.6–4.5 μm imaging) yields the regression:

\[
\log(M_*/\mathrm{M}_\odot) = 0.673\,M_g - 1.108\,M_r + 0.996
\]

where $M_g$ and $M_r$ are the absolute magnitudes in the respective DESI-LS bands. This statistically optimal form achieves a scatter of approximately 25% (in logarithmic units) against the S$^4$G reference. Systematic offsets due to alternative S$^4$G mass calibrations (e.g., mass-to-light ratios, assumed IMF) are typically of order 0.07–0.09 dex, underscoring intrinsic uncertainties in any photometrically derived stellar mass.

## 2. Role and Contribution of Photometric Bands

DESI-LS provides imaging in at least three optical bands: $g$, $r$, and $z$. Comprehensive testing demonstrates that $g$ and $r$ alone capture the bulk of the diagnostic power for stellar mass estimation. The $r$ band is the single most informative photometric channel. Adding $z$ band, although redder and closer to S$^4$G wavelengths, confers negligible improvement in the scatter; the $z$ band’s lower SNR and subtle systematics likely limit its utility in this context. Neither higher-order polynomial functions nor incorporates extrinsic structural parameters (e.g., Sèrsic index, ellipticity) materially reduce the uncertainty below the $\sim$25% floor set by $g$ and $r$ magnitudes alone.

## 3. Tools, Automation, and Data Pipeline

The mass estimation workflow is encapsulated in the Python package **photomass_ls.py**, engineered for reproducibility and high throughput. Key components include:

- **Automated data retrieval:** FITS images in $g$, $r$, (optionally $z$) bands are programmatically downloaded from the Legacy Surveys DR10 archive.
- **Foreground and contaminant masking:** Using SEP (SExtractor in Python), the script auto-generates object masks to exclude stars and neighbors from the GALFIT modeling.
- **Input parameter estimation:** The pipeline computes initial guesses for crucial fit parameters—effective radius, axis ratio, position angle—based on the segmentation output, embedding these into the GALFIT configuration.
- **Photometric modeling and extinction correction:** GALFIT extracts integrated model magnitudes, which are then corrected for Galactic foreground extinction (e.g., via NED).
- **Absolute magnitude and mass calculation:** With supplied redshifts or distances, the code converts apparent to absolute magnitudes, applies the calibrated mass formula, and outputs $\log(M_*)$ estimates.

Optional features include the application of K-corrections for higher-redshift systems and selection among alternative S$^4$G mass calibrations.

## 4. Alternative Calibrations and Generality

In addition to the default formula based on direct GALFIT measurements, an alternative calibration using photometry from the Siena Galaxy Atlas 2020 (SGA-2020) is provided. The corresponding scatter is $\sim$29%, reflecting differences in aperture definition, photometric depth, and data quality. Analyses demonstrate that even with different extraction methodologies or input data, the general $g$/$r$-based approach remains effective and robust, although caution should be exercised when comparing mass catalogs constructed from disparate pipelines. Furthermore, coefficients are available for several S$^4$G-based calibrations (e.g., after Meidt et al. 2014, Querejeta et al. 2015), allowing users to match assumptions as needed for specific scientific contexts.

## 5. Applications and Implications

The DESI-LS stellar mass estimator serves as a practical backbone for large-scale studies of galaxy populations:

- **Statistical extragalactic studies:** The ability to derive homogeneous stellar masses for millions of galaxies enables refined measurements of the stellar mass function, mass–metallicity relation, and stellar-to-halo mass scaling across cosmic environments.
- **Comparison with dynamical and lensing masses:** Automated mass catalogs facilitate cross-matching with dynamical and lensing studies, permitting critical comparisons of stellar and total mass measurement techniques.
- **Target selection and ancillary science:** Rapid mass estimation benefits legacy survey target selection for follow-up campaigns, including deep spectroscopy or high-resolution imaging.
- **Systematic uncertainties:** The accuracy of individual mass estimates is limited by the S$^4$G calibration and the universality of the underlying stellar population assumptions. The method performs optimally for isolated galaxies with high-quality DESI-LS imaging and limited morphological complexity. Complex objects—such as those with tidal features or significant non-stellar contributions—may require domain-specific caution or future methodological refinements.

## 6. Limitations and Prospects for Improvement

Limiting factors include: (1) a systematic calibration floor ($\gtrsim$0.1 dex) from both S$^4$G reference uncertainties and propagation of errors in distance and extinction; (2) relatively diminished utility for galaxies with atypical morphologies or strong nebular emission; and (3) the lack of improvement when additional photometric bands or structural parameters are introduced. Ongoing and future wide-area surveys may explore the integration of advanced machine learning techniques, multi-wavelength SED fitting, or more sophisticated modeling of stellar population varaibility to further reduce the intrinsic scatter and tailor mass estimates for diverse populations.

## Conclusion

The calibrated mass estimator for DESI Legacy Imaging Surveys, built upon rigorous regression against S$^4$G stellar masses and automated with an open-source Python workflow, provides a practical bridge between large-area optical imaging and robust extragalactic stellar mass inference. Its demonstrated precision, simplicity, and scalability ensure wide applicability within both standalone and cross-survey galaxy evolution analyses [2510.02257].

Source: https://www.emergentmind.com/topics/desi-legacy-imaging-surveys-desi-ls