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Record W4410302350 · doi:10.1093/mnras/staf748

A simple optimization for the MeerKAT pulsar timing array

2025· article· en· W4410302350 on OpenAlexfundno aff
H. Middleton, R. M. Shannon, M. Bailes, A D Cameron, A. Corongiu, M. Geyer, M. H. Jones, M. Kramer, Matthew T. Miles, A. Parthasarathy, Andrea Possenti, Daniel J. Reardon

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRadio Astronomy Observations and Technology
Canadian institutionsnot available
FundersAustralian Research CouncilInstitut sur la Nutrition et les Aliments FonctionnelsUK Space AgencyMichelson Prize and Grants
KeywordsPhysicsPulsarSimple (philosophy)AstronomyAstrophysics

Abstract

fetched live from OpenAlex

ABSTRACT The goal of the MeerKAT radio telescope’s pulsar timing array programme (MPTA) is the detection of gravitational waves (GWs) of nanohertz frequencies. Evidence for such a signal was recently announced by the MPTA and several other pulsar timing array (PTA) consortia. Given an array of pulsars and an observation strategy, we consider whether small adjustments to the observing schedule can provide gains in signal-to-noise ratio (S/N) for a stochastic GW background signal produced by a population of massive black hole binaries. Our approach uses a greedy algorithm to reallocate available integration time between pulsars in the array. The overall time dedicated to MPTA observing is kept constant so that there is only minimal disruption to the current observation strategy. We assume a GW signal consistent with those reported. For the sake of demonstrating our method, we also make several simplifying assumptions about the noise properties of the pulsars in the MPTA. Given these assumptions, we find that small adjustments to the observing schedule can provide an increased S/N by ${\approx} 20{{\ \rm per\, cent}}$ for a $10\, {\rm yr}$ PTA lifespan.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.213
Threshold uncertainty score0.397

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.228
Teacher spread0.218 · 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 teacher head, 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
Published2025
Admission routes1
Has abstractyes

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