DWARF GALAXY CLUSTERING AND MISSING SATELLITES
Bibliographic record
Abstract
At redshifts around 0.1 the Canada–France–Hawaii Telescope Legacy Survey Deep fields contain some 6 × 10 4 galaxies spanning the mass range from 10 5 to 10 12 M ☉ . We measure the stellar mass dependence of the two-point correlation using angular measurements to largely bypass the errors, approximately 0.02 in the median, of the photometric redshifts. Inverting the power-law fits with Limber's equation we find that the autocorrelation length increases from a very low 0.4 h −1 Mpc at 10 5.5 M ☉ to the conventional 4.5 h −1 Mpc at 10 10.5 M ☉ . The power-law fit to the correlation function has a slope which increases from γ ≃ 1.6 at high mass to γ ≃ 2.3 at low mass. The spatial cross-correlation of dwarf galaxies with more massive galaxies shows fairly similar trends, with a steeper radial dependence at low mass than predicted in numerical simulations of subhalos within galaxy halos. To examine the issue of "missing satellites" we combine the cross-correlation measurements with our estimates of the low-mass galaxy number density. We find on the average there are 60 ± 20 dwarfs in subhalos with M (total)>10 7 M ☉ for a typical Local Group M (total)/ M (stars) = 30, corresponding to M / L V ≃ 100 for a galaxy with no recent star formation. The number of dwarfs per galaxy is about a factor of 2 larger than currently found for the Milky Way. Nevertheless, the average dwarf counts are about a factor of 30 below lambda cold dark matter (LCDM) simulation results. The divergence from LCDM predictions is one of the slope of the relation, approximately dN / d ln M ≃ −0.5 rather than the predicted −0.9, not sudden onset at some characteristic scale. The dwarf galaxy star formation rates span the range from passive to bursting, which suggests that there are few completely dark halos.
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.
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 | 0.000 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".