The Outer Stellar Mass of Massive Galaxies: A SimpleTracer of Halo Mass with Scatter Comparable to Richness and Reduced Projection Effects
Bibliographic record
Abstract
These are the data for reproducing the results of the publication titled "The Outer Stellar Mass of Massive Galaxies: A Simple Tracer of Halo Mass with Scatter Comparable to Richness and Reduced Projection Effects" by Song Huang et al. Please see the Python scripts and Jupyter notebooks provided in the jianbing repo for examples about how to use these data files. And please contact dr.guangtou@gmail.com if you have any questions about these data. ------------------------------------------------------------------------------------------------- Here is a brief description of all the files: Data from N-body simulation: mdpl2_halos_0.7333_reduced_logmvir_13.npy Basic information about the dark matter halos from MDPL2 simulation For scale factor = 0.7333 (or z~0.4). Only for halos with logMvir > 13.0. mdpl2_particles_0.7333_72m.npy Particle catalog of the a=0.7333 snapshot from MDPL2 This is a down-sampled version with 72 million particles. topn_theory_demo.pkl These are the data used to create the theoretical demo of the TopN test. It is used for making the figures in this notebook. Catalogs of Galaxies or Galaxy Clusters: camira_s16a_cluster_use_bsm.fits The HSC S16A CAMIRA cluster catalog. redmapper_hsc_s16a_cluster_bsm.fits The HSC S16A redMaPPer cluster catalog. redmapper_sdss_cluster_bsm.fits The SDSS DR8 redMaPPer clusters in the HSC S16A footprint. s16a_massive_logm_11.2.fits 0.2 Galaxy-Galaxy Lensing Data: s16a_weak_lensing_medium.hdf5 A compilation of the weak lensing data to calculate the DeltaSigma profiles. This includes the weak lensing source catalog, photometric redshift calibration file, and the random catalog. "medium" here means we applied the medium criteria for selecting source galaxies. Please refer to Speagle et al. (2019) for the exact meaning of these criteria. We also have a "basic" and "strict" version. Please send your request if you need them. topn_public_s16a_medium_precompute.hdf5 A compilation of pre-computed lensing profiles for each individual object in a different galaxy or cluster samples for the TopN test. These are the data used to create the stacked DeltaSigma profiles. We also provide the "strict" and the "basic" versions if you want to test the robustness of the TopN tests against the different selections of source galaxies in weak lensing measurements. You just need these files to generate the stacked DeltaSigma profiles.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.053 | 0.023 |
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".