Two novel methods of measuring cosmic distances in the Universe
Bibliographic record
Abstract
We present two novel methods of distance measurement using photometric techniques. We compare the methods to each other and independently created methods to measure photometric redshifts.The first method we present in this thesis is based on SFR of galaxies which are typically either star forming, quenched, or in transition between the two. This causes the SFR measurements to group up into two distinct and well defined groups in SFR-M? space. We measure how these groups evolve with redshift and see a distinct non degenerate evolutionary path which makes it possible to use it for distance measurements. Since this method requires measurements of several different galaxies we apply this method to several galaxy clusters to test it and see how well it works.The second method uses BL Lac objects to measure distance. While using these objects is not by itself a novel concept, we do extract the host galaxy magnitudes needed to measure the distance in a way that has not before been done on this large an amount of data. We also present several thousand new BL Lac candidates in the SDSS BOSS catalogue which has not previously undergone a systematic and dedicated search for BL Lacs. By doing this we also find many more radio quiet BL Lac like objects which have previously not been detected in a high enough number to properly analyse through the use of statistics.Finally we compare the two methods to each other as well with an independent photometric redshift method as well as measure the Hubble constant to be able to compare the methods to the distance ladder as a whole. Here the SFR method does not match up well to the independent methods giving worse results, but the BL Lac method give results with similar precision to the independent methods.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".