Comparing the Photometric Calibration of DESI Imaging and Gaia Synthetic Photometry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The relative photometric calibration errors in the DESI Legacy Imaging Surveys (LS), which are used for DESI target selection, can leave imprints on the DESI target densities and bias the resulting cosmological measurements. We characterize the LS calibration systematics by comparing the LS stellar photometry with Gaia DR3 synthetic photometry. We find the stellar photometry of LS DR9 and Gaia has an rms difference of 4.7, 3.7, 4.4 mmag in DECam grz bands, respectively, when averaged over an angular scale of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mn>27</mml:mn> <mml:mo>′</mml:mo> </mml:math> . There are distinct spatial patterns in the photometric offset resembling the Gaia scan patterns (most notably in the synthesized g -band) which indicate systematics in the Gaia spectrophotometry, as well as honeycomb patterns due to LS calibration systematics. We also find large and smoothly varying photometric offsets at decl. < −29.°25 in LS DR9 which are fixed in DR10.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it