Doppler bias: impact of peculiar velocities on color selection and the large scale structure of galaxy surveys
Bibliographic record
Abstract
Abstract Lightcone selection effects on cosmic observables must be precisely accounted for in the next generation of surveys, including the Dark Energy Spectroscopic Instrument (DESI) survey. This will allow us to correctly model the data and extract subtle shifts from general-relativistic effects. We examine the effects of peculiar velocities on color selection in spectroscopic galaxy surveys, with a focus on their implications for the galaxy clustering dipole P 1(k). Using DESI Emission Line Galaxy (ELG) targets, we show that peculiar velocities can shift spectral emission features into or out of filter bands, modifying galaxy colors and thereby changing galaxy selection. This phenomenon mimics the effect of evolution bias, and we refer to it as the Doppler bias, bD . The Doppler bias is of comparable size to the evolution bias at 0.8 < z < 1, where it is largest. This enhances the ELG-LRG (Luminous Red Galaxy) cross-correlation dipole by 25–50%. This could be detectable at the ∼ 6σ level for the full DESI survey. Additionally, we found that our bD estimate is impacted by the incompleteness of the parent ELG sample. Therefore, this work highlights the essential need for careful consideration of spectral-dependent biases caused by peculiar velocities during the selection phase of galaxy surveys, to enable unbiased analyses.
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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 | 0.006 | 0.042 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".