<tt>fftvis</tt> : a non-uniform Fast Fourier Transform based interferometric visibility simulator
Why this work is in the frame
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Bibliographic record
Abstract
ABSTRACT The detection and characterization of the 21 cm signal from the Epoch of Reionization (EoR) demands extraordinary precision in radio interferometric observations and analysis. For modern low-frequency arrays, achieving the dynamic range necessary to detect this signal requires simulation frameworks to validate analysis techniques and characterize systematic effects. However, the computational expense of direct visibility calculations grows rapidly with sky model complexity and array size, posing a potential bottleneck for scalable forward modelling. In this paper, we present fftvis, a high-performance visibility simulator built on the Flatiron Non-Uniform Fast-Fourier Transform (finufft) algorithm. We show that fftvis matches the well-validated matvis simulator to near numerical precision while delivering substantial runtime reductions, up to two orders of magnitude for dense, many-element arrays. We provide a detailed description of the fftvis algorithm and benchmark its computational performance, memory footprint, and numerical accuracy against matvis, including a validation study against analytic solutions for diffuse sky models. We further assess the utility of fftvis in validating 21 cm analysis pipelines through a study of the dynamic range in simulated delay and fringe-rate spectra. Our results establish fftvis as a fast, precise, and scalable simulation tool for 21 cm cosmology experiments, enabling end-to-end validation of analysis pipelines.
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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 | 0.001 |
| 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 it