Fast and accurate estimation for astrophysical problems in large databases
Bibliographic record
Abstract
In this proposed thesis, I will develop efficient non-parametric methods for parameter estimation in large databases of high-dimensional, noisy data. Specifically, I plan to continue exploring the efficacy of the diffusion map method of data transformation, used in conjunction with the Nyström extension, in uncovering underlying structure in complicated, high-dimensional data sets. I will explore models that effectively exploit this structure to accurately estimate scientific parameters of interest. Additionally, I will formulate multi-scale methods that generate data-driven bases within local partitions to allow for the modeling of more complicated data. The proposed methods will be tested on three research problems in astrophysics: estimation of star formation history (SFH) in galaxies using spectra from the Sloan Digital Sky Survey (SDSS), photometric redshift estimation using data from the SDSS Photometric Survey and the Canada-France-Hawaii Telescope Legacy Survey, and detection of classes of quasar and outliers using SDSS spectra. Preliminary work has already shown promise in the proposed methods for both SFH estimation (Richards et al. 2009) and photometric redshift estimation (Freeman et al. 2009). 1 Astronomical survey data Technological advancements in observational astronomy have caused a recent flood of astronomical surveys that collect data from billions of objects. 1 Modern surveys amass data for different types of astronomical object (e.g. stars, galaxies, quasars) across the entire electromagnetic spectrum,
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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 | 0.013 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".