Phenomenological power spectrum models for H α emission line galaxies from the Nancy Grace Roman Space Telescope
Bibliographic record
Abstract
ABSTRACT The High Latitude Spectroscopic Survey (HLSS) is the reference baseline spectroscopic survey for NASA’s Nancy Grace Roman Space Telescope, measuring redshifts of ∼10M H α emission line galaxies over a 2000 deg2 footprint at z = 1–2. In this work, we use a realistic Roman galaxy mock catalogue to explore optimal phenomenological modelling of the measured power spectrum. We consider two methods for modelling the redshift-space distortions (Kaiser squashing and another with a window function on β that selects out the coherent radial infall pairwise velocities, $\mathcal {M}_A$ and $\mathcal {M}_B$, respectively), two models for the non-linear impact of baryons that smear the baryon acoustic oscillation signal (a fixed ratio between the smearing scales in the perpendicular and parallel dimensions and another where these smearing scales are kept as free parameters, Pdw(k|k*) and Pdw(k|Σ⊥, Σ∥), respectively), and two analytical emulations of non-linear growth (one employing the halo model and another formulated from simulated galaxy clustering of a semi-analytical model, $\mathcal {F}_{HM}$ and $\mathcal {F}_{\it SAM}$, respectively). We find that the best model combination employing $\mathcal {F}_{HM}$ is $P_{dw}(k|k_*)*\mathcal {F}_{HM}*\mathcal {M}_B$, while the best combination employing $\mathcal {F}_{\it SAM}$ is $P_{dw}(k|k_*)*\mathcal {F}_{\it SAM}*\mathcal {M}_B$, which leads to unbiased measurements of cosmological parameters. We compare these to the Effective Field Theory of Large-Scale Structure perturbation theory model PEFT(k|Θ), and find that our simple phenomenological models are comparable across the entire redshift range for kmax = 0.25 and 0.3 h Mpc−1. We expect the tools that we have developed to be useful in probing dark energy and testing gravity using Roman in an accurate and robust manner.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".