Pulsar population synthesis using palfa detections and pulsar search collaboratory discoveries including a wide DNS system and a nearby MSP
Bibliographic record
Abstract
Using the ensemble of detections from pulsar surveys, we can learn about the sizes and characteristics of underlying populations. In this thesis, I analyze results from the Pulsar Arecibo L-band Feed Array (PALFA) precursor and Green Bank Telescope 350 MHz Drift Scan surveys; I examine survey sensitivity to see how detections can inform pulsar population models, I look at new ways of including young scientists -- high school students -- in the discovery process and I present timing solutions for students' discoveries (including a nearby millisecond pulsar and a pulsar in a wide-orbit double neutron star system).;The PALFA survey is on-going and uses the ALFA 7-beam receiver at 1400 MHz to search both inner and outer Galactic sectors visible from Arecibo (32° ?£? 77° and 168° ?£? 214°) close to the Galactic plane (|b| ? 5°) for pulsars. The PALFA precursor survey observed a subset of this region, (|b| ? 1°) and detected 45 pulsars, including one known millisecond pulsar (MSP) and 11 previously unknown, long-period (normal) pulsars. I assess the sensitivity of the PALFA precursor survey and use the number of normal pulsar and MSP detections to infer the size of each underlying Galactic population. Based on 44 normal pulsar detections and one MSP, we constrain each population size to 107,000+36,000-25,000 and 15,000 +85,000-6,000 respectively with 95% confidence. Based on these constraints, we predict yields for the full PALFA survey and find a deficiency in normal pulsar detections, possibly due to radio frequency interference and/or scintillation, neither of which are currently accounted for in population simulations.;The GBT 350 MHz Drift Scan survey collected data in the summer of 2007 while the GBT was stationary, undergoing track replacement. Results discussed here come from ~20% of the survey data, which were processed and donated to the Pulsar Search Collaboratory (PSC). The PSC is a joint outreach program between WVU and NRAO, involving high school students in the pulsar discovery process -- hands-on, cutting-edge research -- to foster their interest in pursuing Science, Technology, Engineering and Mathematics (STEM) related career paths. The PSC began in 2008; since then, over 100 teachers and 2,500 students from 18 states have participated and discovered seven pulsars. Of these seven, J1400--1431, a bright, nearby MSP shows promising characteristics for inclusion in pulsar timing arrays, which aim to detect gravitational waves by precisely timing an array of MSPs. Two others -- J1821+0155, a disrupted recycled pulsar and
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".