A simulation framework for the <i>LiteBIRD</i> instruments
Bibliographic record
Abstract
Abstract LiteBIRD , the Lite (Light) satellite for the study of B-mode polarization and Inflation from cosmic background Radiation Detection, is a space mission focused on primordial cosmology and fundamental physics. In this paper, we present the LiteBIRD Simulation Framework (LBS), a Python package designed for the implementation of pipelines that model the outputs of the data acquisition process from the three instruments on the LiteBIRD spacecraft: LFT (Low-Frequency Telescope), MFT (Mid-Frequency Telescope), and HFT (High-Frequency Telescope). LBS provides several modules to simulate the scanning strategy of the telescopes, the measurement of realistic polarized radiation coming from the sky (including the Cosmic Microwave Background itself, the Solar and Kinematic dipole, and the diffuse foregrounds emitted by the Galaxy), the generation of instrumental noise and the effect of systematic errors, like pointing wobbling, non-idealities in the Half-Wave Plate, et cetera . Additionally, we present the implementation of a simple but complete pipeline that showcases the main features of LBS. We also discuss how we ensured that LBS lets people develop pipelines whose results are accurate and reproducible. A full end-to-end pipeline has been developed using LBS to characterize the scientific performance of the LiteBIRD experiment. This pipeline and the results of the first simulation run are presented in Puglisi et al. (2025).
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| 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) | 0.000 | 0.000 |
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 teacher head, 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".