The Taurus Molecular Cloud: Multiwavelength Surveys with XMM-Newton, the Spitzer Space Telescope, and the Canada-France-Hawaii Telescope
Bibliographic record
Abstract
The Taurus Molecular Cloud (TMC) ranks among the nearest and best-studied low-mass star-formation regions. It contains numerous prototypical examples of deeply embedded protostars with massive disks and outflows, classical and weak-lined T Tauri stars, jets and HerbigHaro objects, and a growing number of confirmed brown dwarfs. Star formation is ongoing, and the cloud covers all stages of pre-main-sequence stellar evolution. We have initiated comprehensive surveys of the TMC, including in particular (1) a deep X-ray survey of about 5 deg2 with XMM-Newton; (2) a nearto mid-infrared photometric survey of ≈30 deg2 with the Spitzer Space Telescope, mapping the entire cloud in all available photometric bands; and (3) a deep optical survey using the Canada-France-Hawaii Telescope. Each wavelength regime contributes to the understanding of different aspects of young stellar systems. XMM-Newton and Spitzer mapping of the central TMC is a real breakthrough in disk characterization, offering the most detailed studies of correlations between disk properties and high-energy magnetic processes in any low-mass star-forming region, extending also to brown dwarfs in which disk physics is largely unexplored. The optical data critically complements the other two surveys by allowing clear source identification with 0.8 resolution, identifying substellar candidates, and, when combined with NIR data, providing the wavelength baseline to probe NIR excess emission. We report results and correlation studies from these surveys. In particular, we address the physical interpretation of our new X-ray data, discuss the entire young stellar population from embedded protostars to weak-lined T Tau stars and their environment, and present new results on the low-mass population of the TMC, including young brown dwarfs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".