Bibliographic record
Abstract
The interstellar medium is the principal ingredient for star formation and hence, it is necessary to study the properties of the interstellar medium. Radio sources in our Galaxy and beyond can be used as a probe of the intervening medium. In this dissertation, I present an attempt to use radio transients like pulsars and fast radio bursts and their interactions with the environment around them to study interstellar medium. We show that radio emission from pulsars is absorbed by dense ionized gas in their surroundings, causing a turnover in their flux density spectrum that can be used to reveal information about the absorbing medium. We carried out a multi-wavelength observation campaign of PSR B0611+22. The pulsar shows peculiar emission variability that is broadband in nature. Moreover, we show that the flux density spectrum of B0611+22 is unusual which can be attributed to the environment it lies in. We also present predictions of fast radio burst detections from upcoming low frequency surveys. We show that future surveys with the Canadian Hydrogen Intensity Mapping Experiment (CHIME) will be able to detect~1 radio burst per hour even if the radio burst undergoes significant absorption and scattering. Finally, we present our results of pulsar population synthesis to understand the pulsar population in the Galactic Centre (GC) and place conservative upper limits on the GC pulsar population. We obtain an upper limit of 52 CPs and 10,000 MSPs in the GC. The dense, ionized environment of the GC gives us the opportunity to predict the probability of detection by considering scattering and absorption as the principle sources of flux mitigation. Our results suggest that the optimal frequency range for a pulsar survey in the GC is 9--14~GHz. A larger sample of absorbed spectrum pulsars and fast radio bursts will be beneficial not only for the study of emission processes but also for discerning the properties of the material permeating through space.
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 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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".