Py2DJPAS: Automated Spatial Galaxy Analysis

• Py2DJPAS is a Python-based pipeline designed for automated, spatially resolved analysis of galaxies from miniJPAS narrow-band imaging, enabling consistent multi-band photometry.
• The pipeline automates PSF homogenization, aperture definition, masking, background estimation, and Bayesian SED fitting, yielding precise measurements of galaxy properties.
• Using an ANN for emission line equivalent width estimation, Py2DJPAS achieves reliable IFU-like analysis validated against MaNGA spectroscopic benchmarks.

Py2DJPAS is a Python-based pipeline designed for the automated, spatially resolved analysis of galaxies observed by the miniJPAS survey, which acts as a precursor to the J-PAS survey and employs the same narrow-band filter system, telescope, and Pathfinder camera. Py2DJPAS orchestrates the complete workflow: from image and catalog acquisition, point spread function (PSF) homogenization, masking and background estimation, through aperture definition, spectral energy distribution (SED) fitting, to the inference of optical emission line equivalent widths via artificial neural networks (ANNs). The system facilitates consistent, high-precision multi-band photometry and enables Integral Field Unit (IFU)-like exploitation of the J-PAS data, validated on both photometric and spectroscopic benchmarks, including direct comparison to MaNGA IFU survey results (Rodríguez-Martín et al., 15 Sep 2025).

1. Survey Context and Data Preparation

Py2DJPAS is tailored for the miniJPAS survey, comprising a 1 deg² region observed with an array of 56 (J-) narrow-band filters. The data include scientific images, photometric catalogs, photometric zero-points, and PSF measurements, all of which can be automatically retrieved and prepared by the pipeline. The data architecture is designed to facilitate batch processing and to accommodate the unique challenges of multi-band, spatially resolved photometry across large-format imaging datasets.

A fundamental requirement for spatially consistent analysis is control over the PSF variations that arise between different filters due to atmospheric and instrumental effects. Py2DJPAS systematically homogenizes the PSF across all bands, enabling fluxes measured in identical apertures in different images to be directly comparable. For an input band with PSF (Gaussian FWHM) $\sigma_i$ and a target (worst) PSF of $\sigma_w$, the pipeline convolves the input band with a Gaussian kernel of width $\sqrt{\sigma_w^2 - \sigma_i^2}$, guaranteeing that every band shares the same effective resolution (Rodríguez-Martín et al., 15 Sep 2025).

2. Aperture Definition, Masking, and Background Estimation

Upon preprocessing, Py2DJPAS defines apertures, which can be both circular and elliptical to match the morphology of each galaxy under paper. The system generates masks to exclude contaminating sources—such as overlapping objects or imaging artifacts—based on automated detection. Local background estimation is performed for each aperture, using pixels in the immediate vicinity but outside the signal aperture to estimate and subtract the local sky or background, a step especially critical for low-surface-brightness regions and faint outer apertures.

Validation against the miniJPAS photometric catalog demonstrates that Py2DJPAS photometry in these custom apertures agrees at the 3% (or $\sim$0.1 mag) level with the catalog magnitudes, well within the catalog’s photometric zero-point uncertainties (typically $\leq$0.04 mag), indicating robust quality control throughout the image treatment phase (Rodríguez-Martín et al., 15 Sep 2025).

3. Consistent Multi-Band Photometry and SED Fitting

Following homogenization, masking, and background correction, the pipeline extracts aperture-integrated fluxes for each filter, assembling a contiguous “J-spectrum” for each spatial region (aperture) of the galaxy. Py2DJPAS outputs these measurements for use in SED fitting—specifically designed to be fed to the Bayesian SED fitting code BaySeAGal, which implements a Markov Chain Monte Carlo (MCMC) search over parameterized stellar population grids. Typical model outputs include spatially resolved stellar mass, mass-weighted age, dust attenuation, and star formation history.

The accuracy of SED fitting is quantified: for annular galaxy apertures with $\mathrm{S/N}>5$, the residuals between the observed photometry and the best-fit SED models are below 10%, with no significant systematic bias as a function of wavelength. This high level of consistency enables reliable recovery of physical galaxy properties out to large galactocentric radii (Rodríguez-Martín et al., 15 Sep 2025).

4. Emission Line Equivalent Width Estimation via Artificial Neural Networks

Beyond stellar continuum analysis, Py2DJPAS employs an ANN to estimate the equivalent widths (EWs) of key optical emission lines (e.g., H$\alpha$, H$\beta$, [O III], [N II]). The neural network is trained on spaxel-resolved data from integral field unit (IFU) spectroscopic surveys such as CALIFA and MaNGA, leveraging the rich mapping from SED features and multi-band photometry to emission line strengths. The ANN operates directly on the SED-fitted photometry and output parameters, systematically providing EW estimates without requiring full spectroscopic analysis from each object, thus offering a highly efficient solution for mining emission line diagnostics at scale (Rodríguez-Martín et al., 15 Sep 2025).

5. Validation and IFU-Like Analysis: Comparison with MaNGA

A key benchmark for the pipeline is the cross-validation against MaNGA IFU survey data. In the case of galaxy 2470-10239 (redshift $z \approx 0.08$), Py2DJPAS was used to extract spatially resolved photometry via elliptical annuli, which—after SED fitting—yielded stellar mass surface density profiles. These profiles show excellent agreement with those derived from MaNGA spectroscopic mapping within 1 half-light radius (HLR). MaNGA’s mapping typically covers up to $\sim$1–1.5 HLR, while the miniJPAS data analyzed with Py2DJPAS, due to its robust background subtraction and aperture definition, reliably extends to $\sim$4 HLR at $\mathrm{S/N}\sim5$. This suggests that narrow-band imaging surveys processed with Py2DJPAS can probe galaxy outskirts and low surface brightness regimes inaccessible to traditional IFU spectroscopy (Rodríguez-Martín et al., 15 Sep 2025).

Survey	Annuli Coverage	SED Residuals
MaNGA	$\sim$1–1.5 HLR	— (spectroscopic)
miniJPAS + Py2DJPAS	up to 4 HLR	$<$10% (S/N$>$5)

6. Scientific Implications and Applications

Py2DJPAS enables research across several key areas in extragalactic astronomy:

• Consistent, spatially resolved photometry across 56 narrow bands facilitates pseudo-IFU analysis, allowing the paper of stellar population gradients, star formation histories, and dust attenuation profiles at radii much larger than those accessible with current IFU instruments.
• Efficient, automated EW estimation via the ANN supports emission line diagnostics across large galaxy samples, enabling statistical studies of nebular properties and star formation indicators.
• The system’s extension to faint, low surface brightness regimes—enabled by precise background subtraction and aperture-based extraction—allows the investigation of galaxy assembly and evolution well into the outskirts (several HLR).
• Validation against both photometric catalogs and spectroscopic data supports the reliability and accuracy of the workflow, ensuring its utility for future J-PAS datasets and comparable multi-filter surveys.

A plausible implication is that pipelines following the Py2DJPAS methodology can significantly leverage wide-field narrow-band imaging to emulate some of the most valuable aspects of IFU spectroscopic surveys—namely, spatially resolved stellar population and emission line mapping—across larger sky areas.

7. Summary

Py2DJPAS is a fully automated toolchain for the spatially resolved analysis of galaxies using miniJPAS imaging data. Its methodological core includes rigorous PSF homogenization, aperture-based multi-band photometry with masking and local background subtraction, Bayesian SED fitting, and emission line EW estimation using ANNs trained on IFU data. Validation exercises demonstrate that Py2DJPAS achieves photometric and physical property accuracy consistent with both miniJPAS catalogs and MaNGA IFU spectroscopy for resolved galaxies, including excellent performance in galaxy outskirts. This framework positions Py2DJPAS as a foundational tool for exploiting J-PAS and similar surveys in the quantitative paper of galaxy evolution (Rodríguez-Martín et al., 15 Sep 2025).