Bayesian Statistics and Estimating Stellar Masses in the Blind Cosmology Challenge and the Dark Energy Survey
Bibliographic record
Abstract
The purpose of this investigation was to apply the techniques and strategies developed on simulated data over the course of the summer on actual data from the Dark Energy Survey. Bayesian techniques were developed to measure the stellar masses of galaxy clusters through the use simulated data from the Blind Cosmology Challenge (BCC) using models derived from Simha et al (2014) and Conroy and Gunn’s FSPS. Our analysis of data from the BCC mirrors the results of the Canada France Legacy Survey for high mass galaxy clusters (clusters of at least 10^13 stellar masses). There is discrepancy at lower masses because the BCC underestimates the size of central galaxies in lower mass galaxies. We are currently awaiting results on our analysis of galaxies cataloged by the Redmapper galaxy catalogue and these results will be presented at IMSAloquium. We can conclude that Bayesian methods of analysis can provide accurate results when applied to live data. Additionally, since this Bayesian technique provides not the best fit value, but rather, the most likely value, our estimates for stellar mass could be useful as a proxy for the galaxy richness, a key variable when analyzing and modeling the cosmology of a cluster.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".