Mitigating Radio Frequency Interference in CHIME/FRB Real-time Intensity Data
Bibliographic record
Abstract
Abstract Extragalactic fast radio bursts (FRBs) are a new class of astrophysical transient with unknown origins that have become a main focus of radio observatories worldwide. FRBs are highly energetic (∼1036–1042 erg) flashes that last for about a millisecond. Thanks to its broad bandwidth (400–800 MHz), large field of view (∼200 sq. deg.), and massive data rate (1500 TB of coherently beamformed data per day), the Canadian Hydrogen Intensity Mapping Experiment / Fast Radio Burst (CHIME/FRB) project has increased the total number of discovered FRBs by over a factor 10 in 3 yr of operation. CHIME/FRB observations are hampered by the constant exposure to radio frequency interference (RFI) from artificial devices (e.g., cellular phones, aircraft), resulting in ∼20% loss of bandwidth. In this work, we describe our novel technique for mitigating RFI in CHIME/FRB real-time intensity data. We mitigate RFI through a sequence of iterative operations, which mask out statistical outliers from frequency-channelized intensity data that have been effectively high-pass filtered. Keeping false-positive and false-negative rates at very low levels, our approach is useful for any high-performance surveys of radio transients in the future.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".