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Record W2519446426 · doi:10.33915/etd.6754

Pulsar population synthesis using palfa detections and pulsar search collaboratory discoveries including a wide DNS system and a nearby MSP

2015· dissertation· en· W2519446426 on OpenAlexfundno aff
Joseph K. Swiggum

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicPulsars and Gravitational Waves Research
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaInternational Max Planck Research School for Advanced Methods in Process and Systems EngineeringNederlandse Organisatie voor Wetenschappelijk OnderzoekUniversities Space Research AssociationAssociated UniversitiesHigh Point UniversityCanadian Institute for Advanced ResearchNational Radio Astronomy ObservatoryNational Science Foundation
KeywordsPulsarPhysicsMillisecond pulsarGalactic planeAstrophysicsPulsar planetPopulationAstronomyX-ray pulsarGreen Bank TelescopeNeutron starBinary pulsarGalaxy

Abstract

fetched live from OpenAlex

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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.029
GPT teacher head0.359
Teacher spread0.329 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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