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Record W7162005083 · doi:10.82308/24263

Deciphering the Origins of FRBs Using Local Universe CHIME/FRB Discoveries

2023· dissertation· en· W7162005083 on OpenAlexaboutno aff
Mohit Bhardwaj

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicPulsars and Gravitational Waves Research
Canadian institutionsnot available
Fundersnot available
KeywordsUniversePipeline (software)Angular resolution (graph drawing)Radio telescopeIdentification (biology)Field (mathematics)

Abstract

fetched live from OpenAlex

Fast radio bursts (FRBs) are one of astronomy’s greatest mysteries. These millisecond-duration radio pulses are powerful enough to be observed from distant galaxies. Althoughover a thousand FRBs have been discovered to date, their origin remains a hotly debatedtopic primarily due to the dearth of FRBs with known hosts. A promising method to narrowdown FRB origins is by identifying their hosts and/or multi-wavelength counterparts. Moreimportantly, with milliarcsecond localization precision, it is possible to study the FRBs’ lo-cal environment in the host, which is crucial to test if the FRB progenitors constitute an oldor young stellar population. However, due to the limited sensitivity of telescopes operat-ing in the optical and X-ray wavebands, multi-wavelength follow-up is most promising forlocal Universe sources (z < 0.1).The Canadian Hydrogen Intensity Mapping Experiment (CHIME) is a transit radiotelescope operating in the frequency range of 400-800 MHz. Due to its enormous field-of-view (∼220 sq.deg.), large collecting area (8000 sq.m.), broad frequency coverage (400-800 MHz), and highly sophisticated software back-end, CHIME has revolutionized theFRB field by discovering the majority of all known FRB sources to date. For some of theCHIME FRBs, we acquire raw voltage data that can facilitate localization to sub-arcminuteor a few arcminutes precision. This angular resolution can be sufficient to identify hostgalaxies of local Universe FRBs due to the low chance association probability.In this thesis, we discuss the pipeline that facilitates the identification of plausible hostgalaxies of the local Universe CHIME FRBs. Using this pipeline, we identified host galax-ies of the two closest extragalactic FRBs discovered to date, FRB 20200120E and FRB20181030A. FRB 20200120E has the dispersion measure of 87.82 pc cm−3 , which is the lowest recorded from an FRB to date. The FRB appears on the outskirts of M81 (projectedoffset ∼ 20 kpc), a spiral galaxy at a distance of 3.6 Mpc, but well inside its extended HIand thick disks. We search for prompt X-ray counterparts in Swift/BAT and Fermi/GBMdata, and for two of the FRB 20200120E bursts, we rule out coincident SGR 1806−20-likeX-ray bursts. For FRB 20181030A, we identify NGC 3252, a star-forming spiral galaxylocated at the distance of ≈ 20 Mpc, as its most likely host. With the discovery of thissecond-closest extragalactic FRB, we argue that a population of young millisecond mag-netars alone cannot explain the observed volumetric rate of repeating FRBs.In addition to these two closest extragalactic FRBs, we perform follow-up studies oftwo nearby repeating CHIME FRBs, FRB 20180814A and 20190303A. For FRB 20180814A,the second repeating FRB discovered in 2018, we find an early-type lenticular galaxy at thespectroscopic redshift of 0.068 as its plausible host. If this galaxy is not the FRB host, weargue that the host of FRB 20180814A will be the faintest host known to date. For FRB20190303A, we identify a merging pair of star-forming spiral galaxies at the spectroscopicredshift of 0.064 as its most likely host. These two vastly different host associations clearlyhighlight the complex nature of FRB host and progenitor populations

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)0.001
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 score0.027
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.020
GPT teacher head0.357
Teacher spread0.336 · 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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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