<i>Euclid</i> preparation
Bibliographic record
Abstract
Context. The European Space Agency’s Euclid mission is one of a raft of forthcoming large-scale cosmology surveys that will map the large-scale structure in the Universe with unprecedented precision. The mission will collect a vast amount of data that will be processed and analysed by Euclid ’s Science Ground Segment (SGS). The development and validation of the SGS pipeline requires state-of-the-art simulations with a high level of complexity and accuracy that include subtle instrumental features not accounted for previously as well as faster algorithms for the large-scale production of the expected Euclid data products. Aims. In this paper, we present the Euclid SGS simulation framework as it is applied in a large-scale end-to-end simulation exercise named Science Challenge 8. Our simulation pipeline enables the swift production of detailed image simulations for the construction and validation of the Euclid mission during its qualification phase and will serve as a reference throughout operations. Methods. Our end-to-end simulation framework started with the production of a large cosmological N-body simulation that we used to construct a realistic galaxy mock catalogue. We performed a selection of galaxies down to I E =26 and 28 mag, respectively, for a Euclid Wide Survey spanning 165 deg 2 and a 1 deg 2 Euclid Deep Survey. We built realistic stellar density catalogues containing Milky Way-like stars down to H < 26 from a combination of a stellar population synthesis model of the Galaxy and real bright stars. Using the latest instrumental models for both the Euclid instruments and spacecraft as well as Euclid -like observing sequences, we emulated with high fidelity Euclid satellite imaging throughout the mission’s lifetime. Results. We present the SC8 dataset, consisting of overlapping visible and near-infrared Euclid Wide Survey and Euclid Deep Survey imaging and low-resolution spectroscopy along with ground-based data in five optical bands. This extensive dataset enables end-to-end testing of the entire ground segment data reduction and science analysis pipeline as well as the Euclid mission infrastructure, paving the way for future scientific and technical developments and enhancements.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.196 | 0.145 |
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".