Bibliographic record
Abstract
Precision pulsar timing can be used to study many different astrophysically interesting phenomena, from the emission mechanism of pulsars to the detection of nanohertz gravitational waves. These analyses span topics such as studying the single pulses of pulsars and analyzing years of pulsar timing data from pulsar timing arrays (PTAs). Single-pulse studies allow us to glean information on the emission physics of pulsars on their shortest timescales, while PTA observations of millisecond pulsars (MSPs) allow us to not only study the pulsars themselves, but also probe the interstellar medium (ISM) and constrain the noise in the data for precision pulsar timing experiments. To study the emission properties of pulsars, we compiled a large population of single pulses from three rotating radio transients (RRATs), from which we detect sporadic, but periodic pulsations and whose emission mechanisms remain largely unknown. Our study found that the average spectral indices of these RRATs is flatter than most pulsars, with a power-law index of α = −0.9, but that the distribution of single-pulse spectral indices is large. We also find that the single-pulse flux distributions of these three RRATs generally follow a log-normal distribution, suggesting the detected radio emission is not due to giant pulses. Further we find that single-pulse flux is not correlated with the wait-time between pulses, and thus is not produced by mechanisms such as the storage and release of energy in the pulsar magnetosphere. We then analyze multi-hour continuous observations of seven MSPs in the North American Nanohertz Observatory for Gravitational Wave (NANOGrav) PTA to characterize the ISM along their lines-of-sight. These unique observa- tions allowed us to place some of the best limits on the scintillation bandwidth and timescale for these MSPs, some for the first time, and show that the root mean square (rms) noise due to scattering in these MSPs is < 50 ns. We further showed that, as expected, the dispersion measure (DM) of these MSPs does not vary on ∼ hour long timescales, and that the timing precision of these MSPs does not decrease if the observations are not contiguous, as expected for observations by telescopes like the Canadian Hydrogen Intensity Mapping Experiment. Finally, we explore the covariances between various radio frequency-dependent parameters in pulsar timing models and how they contribute to the overall noise budget of the PTA. To do this, we developed the Pulsar Signal Simulator Python package and use it to generate simulated
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".