SPACE: the spectroscopic all-sky cosmic explorer
Why this work is in the frame
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Bibliographic record
Abstract
We describe the scientific motivations, the mission concept and the instrumentation of SPACE , a class-M mission proposed for concept study at the first call of the ESA Cosmic-Vision 2015–2025 planning cycle. SPACE aims to produce the largest three-dimensional evolutionary map of the Universe over the past 10 billion years by taking near-IR spectra and measuring redshifts for more than half a billion galaxies at 0 < z < 2 down to AB ~23 over 3 π sr of the sky. In addition, SPACE will also target a smaller sky field, performing a deep spectroscopic survey of millions of galaxies to AB ~26 and at 2 < z < 10 +. These goals are unreachable with ground-based observations due to the ≈500 times higher sky background (see e.g. Aldering, LBNL report number LBNL-51157, 2001 ). To achieve the main science objectives, SPACE will use a 1.5 m diameter Ritchey-Chretien telescope equipped with a set of arrays of Digital Micro-mirror Devices covering a total field of view of 0.4 deg 2 , and will perform large-multiplexing multi-object spectroscopy (e.g. ≈6000 targets per pointing) at a spectral resolution of R~400 as well as diffraction-limited imaging with continuous coverage from 0.8 to 1.8 μm. Owing to the depth, redshift range, volume coverage and quality of its spectra, SPACE will reveal with unique sensitivity most of the fundamental cosmological signatures, including the power spectrum of density fluctuations and its turnover. SPACE will also place high accuracy constraints on the dark energy equation of state parameter and its evolution by measuring the baryonic acoustic oscillations imprinted when matter and radiation decoupled, the distance-luminosity relation of cosmological supernovae, the evolution of the cosmic expansion rate, the growth rate of cosmic large-scale structure, and high- z galaxy clusters. The datasets from the SPACE mission will represent a long lasting legacy for the whole astronomical community whose data will be mined for many years to come.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it