Smartphone Cosmic Ray Network and Solar Flare Data

ISEF Category: Physics and Astronomy

Subcategory: Nuclear and Particle Physics  ·  Difficulty: Advanced  ·  Setup: Home Setup  ·  Time: Full Year

The Hook

Your phone can do more than take photos. With the right app, its camera can help detect tiny particles from space. That means your everyday device can become part of a real cosmic ray network. You can then ask whether solar flares line up with changes in what your phones record.

What Is It?

Cosmic rays are high-energy particles from space that hit Earth's atmosphere and create showers of secondary particles. Some of those particles can pass through a smartphone camera sensor and leave a tiny signal. The CRAYFIS and DECO projects use that idea to turn phones into simple particle detectors.

Think of each phone like a small net catching rare raindrops. One net misses a lot, but many nets in many places can reveal a pattern. Your project asks whether spikes in the phone network match solar flare activity recorded by GOES, the NASA and NOAA satellites that track the Sun's soft X-ray output.

This is not the same as proving the Sun makes cosmic rays. Most cosmic rays come from outside the solar system, and the Sun usually changes particle conditions in indirect ways. That makes the project stronger, because you can test a real physics question about timing, background noise, and correlations across locations.

Why This Is a Good Topic

This makes a strong science fair topic because you can collect real data from many devices, compare it with public space-weather records, and test a clear hypothesis. The question connects physics, data science, and the Sun-Earth system. You can also learn how to clean noisy data, align time series, and check whether a pattern is real or just random.

Research Questions

• How does local latitude affect the detected cosmic ray event rate on smartphones?
• What is the effect of solar flare class on short-term changes in smartphone event counts?
• Does the distance between devices in different cities change how often their event spikes line up?
• To what extent do phone model and camera sensor type affect background event rate?
• Which time lag between GOES soft X-ray peaks and smartphone event spikes gives the strongest correlation?
• How does filtering out low-quality runs change the apparent link between solar activity and detector counts?

Basic Materials

• Android or iPhone smartphones with camera access
• Open-source CRAYFIS or DECO app, if available for your device
• Stable phone chargers or battery packs
• Wi-Fi or cellular data access
• Shared spreadsheet or cloud form for logging detections
• Laptop or desktop computer for data cleaning and graphing
• Public GOES solar flare data from NOAA or NASA
• Notebook for device notes, location notes, and run conditions.

Advanced Materials

• Smartphones with different camera sensor generations
• Mounts or stands that keep phones fixed and darkened
• External temperature loggers for environment notes
• GPS-tagged logging setup or field notes template
• Laptop with Python or R for time-series analysis
• Access to archived GOES X-ray flux files from NOAA
• Optional cosmic ray or muon reference data from public datasets
• Image analysis tools for checking raw sensor frames when available.

Software & Tools

• Google Sheets: Organizes phone event logs and helps you sort data by device, date, and city.
• Python: Lets you clean time series, align data streams, and test correlations.
• Jupyter Notebook: Keeps code, graphs, and notes in one place for repeatable analysis.
• NOAA GOES Data Portal: Provides public solar flare and soft X-ray data for comparison.
• ImageJ: Helps inspect sensor frames or image noise patterns when the app exports raw images.

Experiment Steps

1. Define the exact signal you will count, such as app-detected events, event clusters, or daily rate changes.
2. Choose the comparison rule between phone data and solar data, including the time window and the lag you will test.
3. Set up a shared logging system so every device records the same fields, such as phone model, location, run status, and date.
4. Plan filters that remove obvious bad data, like interrupted runs, unstable device placement, or missing timestamps.
5. Build a baseline model from quiet periods first, so you can tell whether flare periods differ from normal background.
6. Decide which statistics will test your claim, such as correlation, grouped averages, or a permutation test.

Common Pitfalls

• Mixing different app versions or detector settings, which makes counts from one phone impossible to compare with another.
• Recording data in bright rooms or moving phones, which changes sensor noise and creates fake spikes.
• Comparing phone events to flare times without a lag test, which can hide the true timing or create a false match.
• Using only one city or one device model, which makes local conditions look like a physics effect.
• Treating every spike as a solar flare signal, which ignores random clustering and background cosmic ray variation.

What Makes This Competitive

A strong version of this project does more than plot two lines on the same graph. You would need careful controls, a clear baseline, and a test for whether any match beats chance. A better project would compare multiple cities, multiple phone models, and multiple lag windows, then use a solid statistical test. That turns a simple network idea into a real study of noisy distributed measurements.

Project Variations

• Compare cosmic ray event rates across different altitudes, such as coastal, suburban, and mountain locations.
• Test whether Android and iPhone sensors show different background detection patterns under the same logging conditions.
• Replace solar flare timing with geomagnetic storm indices from NOAA to see whether space weather changes the network signal.

Learn More

• CRAYFIS project page: Read about the smartphone cosmic ray detector concept and how phone sensors can record particle events on the project site.
• DECO project page: Learn how distributed smartphones can contribute to cosmic ray and particle detection from the project documentation.
• NOAA GOES X-ray Flux data: Find solar flare timing and soft X-ray measurements through NOAA's space weather data pages.
• NASA Space Weather Prediction Center: Search for background on flares, geomagnetic storms, and public solar data products.
• PubMed: Search for review articles on cosmic rays, smartphone detectors, or distributed sensor networks.
• arXiv: Search for preprints on mobile particle detection, cosmic ray networks, and time-series correlation methods.