Simulation of Iron Core Proton Discrimination Capability in the HADAR Experiment
Bibliographic record
Abstract
The research detailed in this paper focuses on an extensive simulation of the HADAR experiment's detection capabilities concerning iron cores and protons. Leveraging a sophisticated software package built on the CORSIKA simulation program, the study meticulously examined the distribution characteristics of secondary particles emitted by iron cores and protons. Discrimination between these primary cosmic rays was accomplished through the utilization of Hillas parameters and MRSW (Modified Hillas parameters - Mean Reduced Scaled Width) parameters. Additionally, a quantitative assessment was introduced in the form of the Q-factor to gauge the effectiveness of both discrimination methods. The obtained results showcase the HADAR experiment's remarkable proficiency in not only detecting but also distinguishing between iron cores and protons. Notably, the MRSW method emerged as highly effective, demonstrating significantly superior discrimination capabilities compared to the Hillas parameters. This advancement is pivotal for the HADAR experiment, providing researchers with a more robust and accurate tool for characterizing cosmic ray events. The successful discrimination achieved in this study contributes valuable insights to the broader field of astroparticle physics. The refined capabilities of the HADAR experiment open new avenues for investigating the intricate properties of cosmic rays, thereby advancing our understanding of high-energy astrophysical phenomena. These findings, presented in this paper, lay the groundwork for future research endeavors and underscore the HADAR experiment's significance in unraveling the mysteries of the cosmos.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".