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Record W3132253527 · doi:10.1093/mnras/stab564

Constraints on large-scale magnetic fields in the intergalactic medium using cross-correlation methods

2021· article· en· W3132253527 on OpenAlexaff
A. D. Amaral, Tessa Vernstrom, B. M. Gaensler

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRadio Astronomy Observations and Technology
Canadian institutionsCanadian Institute for Theoretical AstrophysicsUniversity of Toronto
FundersNational Aeronautics and Space Administration
KeywordsPhysicsIntergalactic travelFaraday effectAstrophysicsGalaxyMagnetic fieldIntergalactic mediumUpper and lower boundsPolarization (electrochemistry)Coherence (philosophical gambling strategy)RedshiftQuantum mechanics

Abstract

fetched live from OpenAlex

ABSTRACT Large-scale coherent magnetic fields in the intergalactic medium (IGM) are presumed to play a key role in the formation and evolution of the cosmic web, and in large-scale feedback mechanisms. However, they are theorized to be extremely weak, in the nano-Gauss regime. To search for a statistical signature of these weak magnetic fields, we perform a cross-correlation between the Faraday rotation measures (RMs) of 1742 radio galaxies at z > 0.5 and large-scale structure at 0.1 < z < 0.5, as traced by 18 million optical and infrared foreground galaxies. No significant correlation signal was detected within the uncertainty limits. We are able to determine model-dependent 3σ upper limits on the parallel component of the mean magnetic field strength of filaments in the IGM of ∼30 nG for coherence scales between 1 and 2.5 Mpc, corresponding to a mean upper bound RM enhancement of ∼3.8 rad m−2 due to filaments along all probed sightlines. These upper bounds are consistent with upper bounds found previously using other techniques. Our method can be used to further constrain intergalactic magnetic fields with upcoming future radio polarization surveys.

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 score0.012
Threshold uncertainty score0.601

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.280
Teacher spread0.266 · 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

Citations38
Published2021
Admission routes1
Has abstractyes

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