Investigating the galaxy–halo connection of DESI emission-line galaxies with SHAMe-SF
Bibliographic record
Abstract
Context. The Dark Energy Spectroscopic Instrument (DESI) survey is mapping the large-scale distribution of millions of emission line galaxies (ELGs) over vast cosmic volumes to measure the growth history of the Universe. However, compared to luminous red galaxies, it is more complex to model the connection of ELGs with the underlying matter field. Aims. We employed a novel theoretical model, SHAMe-SF, to infer the connection between ELGs and their host dark matter haloes and subhaloes. SHAMe-SF is a version of subhalo abundance matching that incorporates prescriptions for multiple processes, including star formation, tidal stripping, environmental correlations, and quenching. Methods. We analysed public measurements of the projected and redshift-space ELG correlation functions at z = 1.0 and z = 1.3 from the DESI One Percent data release (from the Early Data Release), which we fitted over a broad range of scales, r ∈ [0.1, 30]/h−1 Mpc, to within the statistical uncertainties of the data. We also validated the inference pipeline using two mock DESI-ELG catalogues built from hydrodynamic (TNG300) and semi-analytic galaxy formation models (L-Galaxies). Results. SHAMe-SF is able to reproduce the clustering of DESI ELGs and the mock DESI samples within statistical uncertainties. We infer that DESI ELGs typically reside in haloes of ∼ 1011.8 h−1 M⊙ when they are centrals and ∼ 1012.5 h−1 M⊙ when they are satellites, which occurs in ∼30% of cases. In addition, compared to the distribution of dark matter within haloes, satellite ELGs preferentially reside both in the outskirts and inside haloes, and have a net infall velocity towards the centre. Finally, our results show evidence of assembly bias and conformity. All these findings are in qualitative agreement with the mock DESI catalogues. Conclusions. These results pave the way for a cosmological interpretation of DESI ELG measurements on small scales using SHAMe-SF.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".