Investigating the nature of Peculiar Supernovae and Fast Radio Bursts via their multi-wavelength properties and environments
Bibliographic record
Abstract
Transient astronomical sources offer new ways to explore phenomena such as the origins of black holes, fast radio bursts, the nature of supernova explosions, the mechanisms driving gamma-ray bursts, and the role of mergers in producing gravitational waves. Two peculiar classes of transients, fast radio bursts (FRBs) and superluminous supernovae (SLSNe), have uncertain origins, prompting ongoing research into their multi-wavelength properties and environments. FRBs are powerful, millisecond-long bursts of radio waves from outside the Milky Way Galaxy. At the same time, some supernovae—particularly SLSNe—are much brighter than standard models predict, and their power sources remain poorly understood. This thesis seeks to advance our understanding of the nature of these two phenomena. Using the Canadian Hydrogen Intensity Mapping Experiment for Fast Radio Burst telescope data and complementary data from imaging telescopes, I identified likely host galaxies for three FRBs, revealing star-forming galaxies in early-type and transitional evolutionary stages as common FRB hosts. Additionally, a search for persistent radio sources (PRS) associated with repeating FRBs found that most repeating FRBs lack detectable PRSs. However, two new candidates' PRSs were identified and may align with magnetar or hypernebula models.For SLSNe, I analyzed multi-wavelength data from a luminous interacting supernova, PS1-11aop, discovered by the Pan-STARRS1 Medium Deep Survey. I then performed a detailed study of PS1-11aop, unveiling a dense shell surrounding the progenitor star and hinting at a significant mass loss before its explosion. Overall, this work advances our understanding of FRBs and SLSNe, exploring possible formation channels and progenitors while shedding light on the mass-loss mechanisms of supernova progenitors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".