A Post-correlation Beamformer for Time-domain Studies of Pulsars and Transients
Bibliographic record
Abstract
Abstract We present a detailed analysis of post-correlation (PC) beamforming (i.e., beamforming which involves only phased sums of the correlation of the voltages of different antennas in an array), and compare it with the traditionally used incoherent and phased beamforming techniques. Using data from the GMRT we show that PC beam formation results in a manyfold increase in the signal-to-noise for periodic signals from pulsars and reductions, of several orders of magnitude, in the number of false triggers from single-pulse events like fast radio bursts (FRBs). This difference arises primarily because the PC beam contains less red noise, as well as less radio frequency interference. The PC beam can also be more easily calibrated than the incoherent or phased array beams. We also discuss two different modes of PC beam formation: (1) by subtracting the incoherent beam from the coherent beam and (2) by phased addition of the visibilities. The computational costs for both these beam formation techniques, as well as their suitability for studies of pulsars and FRBs, are discussed. The techniques discussed here should be of interest for all upcoming surveys with interferometric arrays. Finally, we describe a time-domain survey with the GMRT using the PC beam formation as a case study. We find that PC beamforming will improve the current GMRT time-domain survey sensitivity by ∼2 times for pulsars with periods of few hundreds of milliseconds and by many-folds for even slower pulsars, making it one of the most sensitive surveys for pulsars and FRBs at low and mid radio frequencies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".