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Record W2886992169 · doi:10.1017/pasa.2018.30

<i>In situ</i> measurement of MWA primary beam variation using <i>ORBCOMM</i>

2018· article· en· W2886992169 on OpenAlexaff
J. Line, B. McKinley, J. Rasti, Manisha Bhardwaj, R. B. Wayth, R. L. Webster, Daniel Ung, D. Emrich, L. Horsley, Adam P. Beardsley, B. Crosse, T. M. O. Franzen, B. M. Gaensler, M. Johnston‐Hollitt, D. L. Kaplan, D. Kenney, M. F. Morales, D. Pallot, Kim Steele, S. J. Tingay, Cathryn M. Trott, M. Walker, A. Williams, Chen Wu

Bibliographic record

VenuePublications of the Astronomical Society of Australia · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRadio Astronomy Observations and Technology
Canadian institutionsCanadian Institute for Theoretical AstrophysicsUniversity of Toronto
FundersAustralian Research CouncilARC Centre of Excellence for All-Sky AstrophysicsCommonwealth Scientific and Industrial Research Organisation
KeywordsIn situVariation (astronomy)Primary (astronomy)Beam (structure)GeologyOpticsGeographyPhysicsAstrophysics

Abstract

fetched live from OpenAlex

Abstract We provide the first in situ measurements of antenna element beam shapes of the Murchison Widefield Array. Most current processing pipelines use an assumed beam shape, which can cause absolute and relative flux density errors and polarisation ‘leakage’. Understanding the primary beam is then of paramount importance, especially for sensitive experiments such as a measurement of the 21-cm line from the epoch of reionisation, where the calibration requirements are so extreme that tile to tile beam variations may affect our ability to make a detection. Measuring the primary beam shape from visibilities is challenging, as multiple instrumental, atmospheric, and astrophysical factors contribute to uncertainties in the data. Building on the methods of Neben et al. [Radio Sci., 50, 614], we tap directly into the receiving elements of the telescope before any digitisation or correlation of the signal. Using ORBCOMM satellite passes we are able to produce all-sky maps for four separate tiles in the XX polarisation. We find good agreement with the beam model of Sokolowski et al. [2017, PASA, 34, e062], and clearly observe the effects of a missing dipole from a tile in one of our beam maps. We end by motivating and outlining additional on-site experiments.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.115
Threshold uncertainty score0.447

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 head0.042
GPT teacher head0.266
Teacher spread0.224 · 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 designObservational
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

Citations39
Published2018
Admission routes1
Has abstractyes

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