Gamified laser beam measurement to attract new students in optics and photonics
Bibliographic record
Abstract
The Photonic Games is an annual educational event, initiated in 2008, aimed at sparking high school students' interest in optics and photonics through interactive, science-based challenges.One such activity involves a "laser beam profiling" game, inspired by Star Wars, where participants use laser pointers and a laser beam profiler.Initially designed in LabVIEW, the game was revamped in 2024 using Python and ModernGL, enhancing both user experience and technical engagement.This new version, hosted on GitHub, features 3D starship models and space backgrounds, providing a platform for students to explore programming and optics in a hands-on environment.Participants gain practical insights into laser beam measurement concepts, including beam profiling, centroid calculation, pixel saturation, and decimation, reinforcing precision and technical skill development.By integrating professional tools and open-source programming, the Photonic Games foster an enriching, educational experience, effectively motivating students to pursue careers in engineering, physics, and related fields.At the conference presentation, we will also show a live demonstration of the challenge.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.005 |
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".