The Spitzer-HETDEX Exploratory Large Area Survey. IV. Model-Based Multi-wavelength Photometric Catalog
Bibliographic record
Abstract
We present a 0.3--4.5 $μ$m 16-band photometric catalog for the Spitzer/HETDEX Exploratory Large-Area (SHELA) survey. SHELA covers a $\sim 27$ deg$^2$ field within the footprint of the Hobby-Eberly Telescope Dark Energy Experiment (HETDEX). Here we present new DECam imaging and a $rizK_s$-band-selected catalog of four million sources extracted using a fully model-based approach. We validate our photometry by comparing with the model-based DECam Legacy Survey. We analyze the differences between model-based and aperture photometry by comparing with the previous SHELA catalog, finding that our model-based photometry can measure point sources to fainter fluxes and better capture the full emission of resolved sources. The catalog is $80\%$ ($50\%$) complete at $riz \sim 24.7$ ($25.1$) AB mag, and the optical photometry reaches a $5σ$ depth of $\sim 25.5$ AB mag. We measure photometric redshifts and achieve $1σ$ scatter of $Δz/(1+z)$ of 0.04 with available spectroscopic redshifts at $0 \le z \le 1$. This large area, multi-wavelength photometric catalog, combined with spectroscopic information from HETDEX, will enable a wide range of extragalactic science investigations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.008 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".