Bibliographic record
Abstract
BLAST is a balloon-borne large-aperture, submillimetre telescope, which makes large area (1-200 square degree) surveys of Galactic and extragalactic targets. Since BLAST observes in the stratosphere, it is able to make broad-band observations between 200um and 550um which are difficult or impossible to perform from the ground. BLAST has been designed to probe star formation both in the local Galaxy and in the high redshift (z=1-4) universe.\n\nBecause BLAST is flown on an unmanned stratospheric balloon platform, it has been designed to be able to operate autonomously, without needing operator intervention to perform its scientific goals. This thesis includes an overview of the design of the BLAST platform, with emphasis on the command and control systems used to operate the telescope.\n\nBLAST has been flown on two long-duration balloon flights. The first of these, from Esrange, Sweden in June of 2005, acquired ~70 hours of primarily Galactic data. During the second flight, from Willy Field, Antarctica in December of 2006, BLAST acquired ~225 hours of both Galactic and extragalactic data. Operational performance of the platform during these two flights is reviewed, with the goal of providing insight on how future flights can be improved.\n\nReduction of the data acquired by these large-format bolometer arrays is a challenging procedure, and techniques developed for BLAST data reduction are reviewed. The ultimate goal of this reduction is the generation of high quality astronomical maps which can be used for subsequent portions of data analysis.\n\nThis thesis treats, in detail, the iterative, maximum likelihood map maker developed for BLAST. Results of simulations performed on the map maker to characterise its ability to reconstruct astronomical signals are presented. Finally, astronomical maps produced by this map maker using real data acquired by BLAST are presented, with a discussion on non-physical map pathologies resulting from the data reduction pipeline and map making procedures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.006 |
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".