<i>Euclid</i>: Calibrating photometric redshifts with spectroscopic cross-correlations
Bibliographic record
Abstract
Cosmological constraints from key probes of theEuclidimaging survey rely critically on the accurate determination of the true redshift distributions,n(z), of tomographic redshift bins. We determine whether the mean redshift, ⟨z⟩, of tenEuclidtomographic redshift bins can be calibrated to theEuclidtarget uncertainties ofσ(⟨z⟩) < 0.002 (1 + z) via cross-correlation, with spectroscopic samples akin to those from the Baryon Oscillation Spectroscopic Survey (BOSS), Dark Energy Spectroscopic Instrument (DESI), andEuclid’s NISP spectroscopic survey. We construct mockEuclidand spectroscopic galaxy samples from the Flagship simulation and measure small-scale clustering redshifts up to redshiftz < 1.8 with an algorithm that performs well on current galaxy survey data. The clustering measurements are then fitted to twon(z) models: one is the truen(z) with a free mean; the other a Gaussian process modified to be restricted to non-negative values. We show that ⟨z⟩ is measured in each tomographic redshift bin to an accuracy of order 0.01 or better. By measuring the clustering redshifts on subsets of the full Flagship area, we construct scaling relations that allow us to extrapolate the method performance to larger sky areas than are currently available in the mock. For the full expectedEuclid, BOSS, and DESI overlap region of approximately 6000 deg2, the uncertainties attainable by clustering redshifts exceeds theEuclidrequirement by at least a factor of three for bothn(z) models considered, although systematic biases limit the accuracy. Clustering redshifts are an extremely effective method for redshift calibration forEuclidif the sources of systematic biases can be determined and removed, or calibrated out with sufficiently realistic simulations. We outline possible future work, in particular an extension to higher redshifts with quasar reference samples.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".