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Record W4406267769 · doi:10.1051/0004-6361/202452221

Rotation Measure study of FRB 20180916B with the uGMRT

2025· article· en· W4406267769 on OpenAlexaff
S. Bethapudi, L. G. Spitler, Dongzi Li, V. R. Marthi, Marlon Luis Bause, Robert Main, R. S. Wharton

Bibliographic record

VenueAstronomy and Astrophysics · 2025
Typearticle
Languageen
FieldEngineering
TopicAstronomical Observations and Instrumentation
Canadian institutionsMcGill University
FundersJet Propulsion LaboratoryMax-Planck-GesellschaftTata Institute of Fundamental ResearchDepartment of Atomic Energy, Government of IndiaCalifornia Institute of TechnologyNational Aeronautics and Space Administration
KeywordsPhysicsAstrophysicsMeasure (data warehouse)Rotation (mathematics)AstronomyGeometry

Abstract

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Context. Fast Radio Burst 20180916B is a repeating FRB whose activity window has a 16.34-day periodicity that also shifts and varies in duration with the observing frequency. Recent observations report that the FRB has started to show an increasing trend in secular Rotation Measure (RM) after only showing stochastic variability around a constant value of −114.6 rad m −2 since its discovery. RM studies let us directly probe the magnetic field structure in the local environment of the FRB. The trend of the variability can be used to constrain progenitor models of the FRB. Hence, further study of the RM variability forms the basis of this work. Aims. We studied the local environment of FRB 20180916B. We did so by focusing on polarization properties, namely RM, and studied how it varies with time. The data comes from the ongoing campaigns of FRB 20180916B using the upgraded Giant Metrewave Radio Telescope (uGMRT). The majority of the observations are in Band 4, which is centered at 650 MHz with 200 MHz bandwidth. Additionally, we used a few observations where we had simultaneous coverage in Band 4 and Band 5 (centered at 1100 MHz). Methods. We applied a standard single-pulse search pipeline to search for bursts. In total, we detected 116 bursts with ∼36 hours of on-source time spanning 1200 days from December 2020 to February 2024, with two bursts detected during simultaneous frequency coverage observations. We developed and applied a polarization calibration strategy suited for our dataset. On the calibrated bursts, we used QU-fitting to measure RM. We verified the veracity of calibration solution and RM measurement by performing RM measurements on single pulses of PSR J0139+5814. We also measured various other properties such as rate, linear polarization fraction, and fluence distribution. Results. Of the 116 detected bursts, we could calibrate 79 of them. We observed in our early observations that the RM continued to follow a secular linear trend, as already seen in past observations. However, our later observations suggest that the source switched from the linear trend to stochastic variations around a constant value of −58.75 rad m −2 . It has ceased any secular variability and is only showing stochastic variability. Using the predicted Milky Way RM contribution, we report a tentative detection of a sign flip in the RM in the host galaxy host-frame. We also studied a cumulative rate against fluence and note that the rate at higher fluences (1.2 Jy ms) scales as γ = −1.09(7), whereas that at lower fluences (between 0.2 and 1.2 Jy ms) only scales as γ = −0.51(1), meaning the rate at the higher fluence regime is steeper than at the lower fluence regime. Finally, we qualitatively assess the two extremely large bandwidth bursts that we detected in our simultaneous multi-band observations. Conclusions. Future measurements of RM variations would help place stronger constraints on the local environment. Moreover, any periodic behavior in the RM measurements would directly test progenitor models. Therefore, we motivate such endeavors.

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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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.005
GPT teacher head0.180
Teacher spread0.175 · 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 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".

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Citations4
Published2025
Admission routes1
Has abstractyes

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