The Euclid Mission
Bibliographic record
Abstract
Euclid is an ESA-led medium class space mission selected in October 2011, with launch planned for 2022. The Euclid mission aims at understanding why the expansion of the Universe is accelerating and what is the nature of the source - commonly called dark energy - responsible for this acceleration. Dark energy represents around 75% of the energy content of the Universe today, and together with dark matter it dominates the Universe's matter-energy content. Understanding dark energy is one of the key goals of physics over the next decade. The imprints of dark energy and gravity will be tracked by Euclid using two complementary cosmological probes to capture signatures of the expansion rate of the Universe and the growth of cosmic structures: weak gravitational lensing; and galaxy clustering (both through baryonic acoustic oscillations and redshift-space distortions). Although low-redshift cosmology is the primary driver of the mission, a wide range of science will be possible with the Euclid data. The Euclid Mission aims to survey over 15,000 deg^2 of the extragalactic sky with imaging in a wide visible (riz) band at 0.1" resolution, near-infrared photometry (Y, J, and H) and near-infrared spectroscopy. As a result, the Euclid Mission will generate a vast data set for legacy science, including broadband visible images and near-infrared photometry of roughly 1.5 billion galaxies and near-infrared spectroscopy of roughly 25 million galaxies. Such a large data set will touch on many aspects of astrophysics, on many different scales, from the formation and evolution of galaxies down to the detection of brown dwarfs. In 2016, Canada joined the Euclid Consortium when CFHT approved the Canada-France Imaging Survey (CFIS) as a Large Program. CFIS, along with other ground-based surveys, will be used by Euclid to measure photometric redshifts in the northern sky. 27 faculty-level astronomers in Canada are members of the Euclid Consortium. In this white paper, we present a status update for Euclid, and a request that the committee make a strong recommendation that funds be allocated to support the exploitation of the Euclid data by Canadian researchers.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.046 | 0.086 |
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".