Study of X-ray spectral and timing properties of astronomical sources using low-cost stratospheric balloons
Bibliographic record
Abstract
ABSTRACT Astronomical objects, such as the Sun, accreting matter around black holes, neutron stars, white dwarfs, etc. are known to emit X-rays and gamma-rays. Some of these sources, such as highly magnetized neutron stars, show periodic pulsations. Even accreting matter around black holes and neutron stars shows timing properties including quasi-periodic oscillations. In this paper, we use the data of low-cost, lightweight, high-altitude (∼42 km) small, stratospheric balloon-borne missions of the Indian Centre for Space Physics. We measure the intensity of the secondary cosmic rays, the radiation fluxes, and the spectra of persistent X-ray sources or transient events such as solar flares and gamma-ray bursts, apart from routine weather parameters. In this study, we present the source detection method without an onboard pointing system and the temporal and spectral behaviour of the quiet Sun. We also analyse the data containing Crab in the field of view to find the pulsation of the Crab pulsar. During the analysis of the source detection and spectrum of the solar X-rays, we calculate the detector’s background radiation (mainly secondary cosmic rays) using physical assumptions and also take care of the atmospheric absorption effects. Finally, we show the source detection processes for the strong sources such as the Sun, Crab, and Cyg X-1, obtaining the quiet Sun’s spectrum. Among the interesting timing properties, we present the result for the Crab pulsar and find the well-known ∼33 Hz pulsations whenever our instrument pointed to the Crab.
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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".