EstrellaNueva: An Open-source Software to Study the Interactions and Detection of Neutrinos Emitted by Supernovae
Bibliographic record
Abstract
Abstract Supernovae emit large fluxes of neutrinos, which can be detected by detectors on Earth. Future multi-kiloton scale detectors will be sensitive to several neutrino interaction channels, with thousands of events expected if a supernova emerges in the galaxy neighborhood. There are a limited number of tools to study the interaction rates of supernova neutrinos, although a plethora of available supernova models exist. EstrellaNueva is an open-source software to calculate expected rates of supernova neutrinos in detectors using target materials with typical compositions, and additional compositions can be easily added. This software considers the flavor transformation of neutrinos in the supernova through the adiabatic Mikheyev–Smirnov–Wolfenstein effect, and their interaction in detectors through several channels. Most of the interaction cross sections, such as neutrino–electron and neutrino–proton elastic scattering, inverse beta decay, and coherent elastic neutrino–nucleus scattering, have been analytically implemented. This software provides a link between supernova simulations and the expected events in detectors by calculating fluences and event rates in order to ease any comparison between theory and observation. It provides a simple and standalone tool to explore many physics scenarios, offering an option to add analytical cross sections and define any target material.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.034 | 0.013 |
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".