Measuring subhalo mass in redMaPPer clusters with CFHT Stripe 82 Survey
Bibliographic record
Abstract
We use the shear catalogue from the CFHT Stripe-82 Survey to measure the subhalo masses of satellite galaxies in redMaPPer clusters. Assuming a Chabrier initial mass function and a truncated NFW model for the subhalo mass distribution, we find that the subhalo mass to galaxy stellar mass ratio increases as a function of projected halo-centric radius rp, from |$M_{\rm sub}/M_{\rm star}=4.43^{+ 6.63}_{- 2.23}$| at rp ∈ [0.1, 0.3] h−1 Mpc to |$M_{\rm sub}/M_{\rm star}=75.40^{+ 19.73}_{- 19.09}$| at rp ∈ [0.6, 0.9] h−1 Mpc. We also investigate the dependence of subhalo masses on stellar mass by splitting satellite galaxies into two stellar mass bins: 10 < log (Mstar/h−1 M⊙) < 10.5 and 11 < log (Mstar/h−1 M⊙) < 12. The best-fitting subhalo mass of the more massive satellite galaxy bin is larger than that of the less massive satellites: |$\log (M_{\rm sub}/{h^{-1}\,\mathrm{M}_{{\odot }}})=11.14 ^{+ 0.66 }_{- 0.73}$| (|$M_{\rm sub}/M_{\rm star}=19.5^{+19.8}_{-17.9}$|) versus |$\log (M_{\rm sub}/{h^{-1}\,\mathrm{M}_{{\odot }}})=12.38 ^{+ 0.16 }_{- 0.16}$| (|$M_{\rm sub}/M_{\rm star}=21.1^{+7.4}_{-7.7}$|).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".