2MASS Re-processing I: The Search for Faint Objects

TL;DR

This work develops an automated DAOFind-based pipeline to reprocess 2MASS Atlas J-band images with the goal of recovering faint point sources missing from the PSC. By optimizing detection parameters and applying a central sharpness-based screening, the method increases faint-source detections while keeping false positives low, achieving an apparent magnitude limit improvement from $16.20$ to $16.60$ mag and a growth rate of around $21\%$ with a false-positive rate near $4.8\%$ in the tested regions. Validation against the PSC and VHS catalogs demonstrates that many new faint sources are real, broadening our view of time-domain variability, Galactic structure, and cool, low-luminosity objects. The approach provides a valuable supplement to existing 2MASS catalogs and roadmap for building a refined near-infrared catalog from all-sky Atlas data, with future work including photometric calibration, Gaia-based astrometric re-calibration, and expansion to additional bands and surveys.

Abstract

We present an automated, DAOFind-based pipeline developed to reprocess J-band Atlas All Sky Release Survey Images from the Two Micron All Sky Survey (2MASS). By optimizing the detection parameters and implementing a screening procedure that jointly evaluates the signal-to-noise ratio and central sharpness, the pipeline effectively identifies faint point sources that were previously undetected. Applying this method to eight representative sky regions improves the 2MASS detection limit from 16.20 to 16.60 mag and increases the number of detected point sources by approximately 21.36% relative to the 2MASS Point Source Catalog, with a false-positive rate of only 4.80%. These results demonstrate that the proposed reprocessing pipeline can substantially enhance the scientific yield of archival 2MASS data, providing valuable faint-source supplements for studies of time-domain variability, Galactic structure, and cold, low-luminosity objects.

Figures (10)

• Figure 1: The figure shows the variation trends in the number of newly added point sources with a SNR greater than 3 and the false positive rate for eight Atlas images under different detection parameters. The horizontal axis represents the value of the conversion coefficient $\mathrm{n}_{\mathrm{1}}$ for $\mathrm{FWHM}_{\mathrm{det}}$, and the vertical axis represents the value of the conversion coefficient $\mathrm{n}_{\mathrm{2}}$ for the detection threshold. In the figure, variations in the number of new confirmed point sources are represented by different colors and data point sizes, while changes in the false alarm rate are indicated by color gradients and contour lines.
• Figure 2: (a) Schematic side view of a faint source showing the peak flux and its surrounding region; (b) Top view of the faint source. Panels (a) and (b) facilitate the interpretation of Equations 7 and 8, highlighting the peak flux $f_{\mathrm{center}}$ (blue) and the flux surface density $\sigma$ (green). (c) Spatial relationship between the $1\times1$ pixel region used for peak flux calculation (red) and other pixels of the detected source.
• Figure 3: $\mathrm{SNR} \leq 10$ ($h \in (0,1]$ with a step size of 0.1), while the y-axis quantifies detected sources by type.
• Figure 4: Variation in the retention counts of new confirmed point sources (left) and false positives (right) with h under different SNR filtering ranges for Atlas images in dense stellar fields. The images are from Sky Regions 7 and 8. The horizontal axis represents h, while the vertical axis shows the normalized count of detected sources (relative to the maximum count). Curves in different colors correspond to different upper SNR limits. The subplot titles indicate the maximum number of detected sources.
• Figure 5: Variation in the retention counts of new confirmed point sources (left) and false positives (right) with h under different SNR filtering ranges for Atlas images in sparse stellar fields. The images are from Sky Regions 1 and 3. Other specifications follow those of Figure \ref{['fig:4']}.