Bibliographic record
Abstract
The SDSS-III Baryon Oscillation Spectroscopic Survey (BOSS), a five-year spectroscopic survey of 10,000 square-degrees, achieved first light in late 2009. One of the key goals of BOSS is to measure the signature of baryon acoustic oscillations (BAO) in the distribution of Ly-a absorption from the spectra of a sample of ~150,000 z > 2.2 quasars in conjunction with measuring the redshifts of 1.6 million luminous red galaxies with high completeness to i ~ 19.9 at z ~ 0.7. One of the biggest challenges in achieving this goal is an efficient target selection algorithm for quasars in the redshift range 2.2 z g l 22.0 or r l 21.85.In this thesis I present a new method for quasar target selection using photometric fluxes and a Bayesian probabilistic approach. For our purposes I target quasars using Sloan Digital Sky Survey (SDSS) photometry to a magnitude limit of g = 22. The efficiency and completeness of this technique is measured using the Baryon Oscillation Spectroscopic Survey (BOSS) data, taken in 2010. This llikelihoodr technique was used for the uniformly selected (CORE) sample of targets in BOSS year one spectroscopy to be realized in the 9th SDSS data release. When targeting at a density of 40 objects per square-degree (the BOSS quasar targeting density) the efficiency of this technique in recovering z > 2.2 quasars is 40%. The completeness compared to all quasars identified in BOSS data is 65%. An extension of the llikelihoodr technique is also described. This SDSS-XDQSO technique builds models of the distributions of stars and quasars in flux space down to the flux limit by applying the extreme-deconvolution method to estimate the underlying density. I convolve this density with the flux uncertainties when evaluating the probability that an object is a quasar. This approach results in a targeting algorithm that is more principled, more efficient, and faster than other similar methods. With BOSS's new catalog of quasar and galaxy data, exciting new science can be done. Whether luminous quasars reside in dark matter halos of the same mass and accrete at different rates, or live in halos of different masses and accretion is near the Eddington limit, is still an open question. Here, I present measurements of the luminosity-dependence of quasar clustering, using QSO data from the Sloan Digital Sky Survey (SDSS) Data Release 7, 2dF-SDSS LRG and QSO Survey (2SLAQ), and SDSS-III: Baryon Oscillation Spectroscopic Survey (BOSS).In my quasar sample I have 3100 spectroscopically confirmed quasarswith a redshift range of (0.5 z i -band 22.14. In my galaxy sample I have 5.23 million photometric galaxies brighter than z -band = 23.50, selected from the CFHT (Canada-France-Hawaii Telescope) Survey of Stripe-82 (CS82). The cross-correlation is well described by a power law with slope 1.77 p 0.1 and r0 = 5.05 p 0.14 h-1 Mpc, which is consistent with previous findings. I determine a large-scale quasar bias, bQSO = 1.46 p 0.18, at redshift z =0.7. When I divide the quasar sample into low/high luminosity samples I find luminosity depended quasar clustering at a 4.56 s significance level.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".