Science with the Large Synoptic Survey Telescope
Bibliographic record
Abstract
The Large Synoptic Survey Telescope (LSST) is set to begin commissioning its instruments in 2021, with science verification commencing in 2022 and science operations in 2023. With its large field of view and 3.2 Gigapixel camera, it will scan 18,000 square degrees every few nights from 320-1050 nm. This cadence will not only build a deep co-added map of the sky to ~25th magnitude in its wide field and ~28th magnitude in its deep fields, but will enable transient science on the largest scale. It will produce millions of ‘alerts’ for bright new celestial objects each night. By the end of the nominal 11th data release, it will deliver 18 billion objects in 5.5 million images. LSST will revolutionize photometric survey science.There are a wide range of scientific collaborations under the LSST umbrella: the Stars, Milky Way and the Local Volume Collaboration, the Transients and Variable Stars Collaboration, the Dark Energy Science Collaboration, The Solar System Science Collaboration, The Active Galactic Nuclei Collaboration, the Strong Lensing Collaboration and the Informatics and Statistics Science Collaboration. We highlight the LSST science cases relevant to the Canadian community, and describe how LSST data will complement and transform science in these areas. In a separate white paper, we discuss the varied Canadian data access needs and LSST data products. We will describe how coordination with current and planned facilities and programs with Canadian participation and leadership, namely CFHT, CFIS/UNIONS, MSE, Gemini, and Euclid will leverage these science efforts even further. We discuss the proposed model for participation in LSST and motivate why a coordinated national strategy for Canadian data access is essential to ensuring both the continued Canadian scientific leadership in LSST, and how investing in the infrastructure and training necessary for LSST partnership would therefore be strategic for Canadian astronomy.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.042 | 0.027 |
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".