GraphNeT and Gamma Rays:
Integrating the MAGIC Telescopes
GraphNeT Workshop
Jarred Green
18 August 2025
Max Planck Institute for Physics
jgreen@mpp.mpg.de
Outline
- Current Reconstruction Method
- MAGIC Graphs
- The Data and GraphNeT
- Results
- GraphNet Perspectives
Jarred Green - jgreen@mpp.mpg.de
1. Current Reconstruction
Jarred Green - jgreen@mpp.mpg.de
1. Current Reconstruction
Jarred Green - jgreen@mpp.mpg.de
1. Current Reconstruction
1. Raw data
- charge measured in each pixel
- 50 time slices recorded in each pixel [waveforms]
Jarred Green - jgreen@mpp.mpg.de
2. Image cleaning
- isolate main part of image
- average pixel arrival time
1. Current Reconstruction
Jarred Green - jgreen@mpp.mpg.de
3. Image parameterization
{
'size': 546.8863769783503,
'xc': -3.7898364035228065,
'yc': 7.294274369102854,
'length': 7.767692666363586,
'width': 1.7648053936769377,
'delta': -0.5687518727314761,
...
}1. Current Reconstruction
"Hillas Parameters" (1977!)
Jarred Green - jgreen@mpp.mpg.de
{
'size': 546.8863769783503,
'xc': -3.7898364035228065,
'yc': 7.294274369102854,
'length': 7.767692666363586,
'width': 1.7648053936769377,
'delta': -0.5687518727314761,
...
}"Hillas Parameters"
Random Forest
1. Current Reconstruction
4. Algorithms: the ML part
Jarred Green - jgreen@mpp.mpg.de
{
'size': 546.8863769783503,
'xc': -3.7898364035228065,
'yc': 7.294274369102854,
'length': 7.767692666363586,
'width': 1.7648053936769377,
'delta': -0.5687518727314761,
...
}tl;dr
- Particle Type
- Energy
- Arrival Direction
ML
1. Current Reconstruction
1039 \ \text{pixels} \\ \times 50 \ \text{time slices} \\ \times 2 \ \text{telescopes} \\ = 103900 \ \text{data points}
Jarred Green - jgreen@mpp.mpg.de
A way to include as much information as possible?
1. Current Reconstruction
Jarred Green - jgreen@mpp.mpg.de
Proton
Gamma
Muon
Find a new ways to represent raw data
1. Current Reconstruction
for MAGIC?
Jarred Green - jgreen@mpp.mpg.de
2. Graphs
Jarred Green - jgreen@mpp.mpg.de
time
Sample MC gamma event
2. Graphs
- ❌ 200k+ nodes / graph
- ❌ Training ~weeks
- ✅ Keeps full context of 'night sky background'
Jarred Green - jgreen@mpp.mpg.de
time
Can we make these graphs smaller somehow?
cleaning!
- ✅ Small graphs with 200-2K nodes
- ✅ Training ~hours
- ❌ Maybe loosing some background information
2. Graphs
Jarred Green - jgreen@mpp.mpg.de
3. Data and GraphNeT
- 2D detector instead of 3D
- Every pixel always triggers [full graphs]
- 2 separate detectors with no absolute shared clock
- Noise/background pixels contain useful information
- Instrument is pointed - we only care about arrival directions around an ROI
Vovk et al. 2018 [1806.03167]
Cherenkov astronomy vs neutrinos
Jarred Green - jgreen@mpp.mpg.de
Data processing pipeline
.parquet or .lmdb files exported
about ~1GB / min of data
3. Data and GraphNeT
Python package magic-gnn
all the low-level telescope stuff
Data processing pipeline
RAW MAGIC data
GraphNeT
Training with
cleaned graphs
Training with
full graphs
2️⃣
1️⃣
Jarred Green - jgreen@mpp.mpg.de
Available Data:
- 2M MC gamma events
- 100k MC proton events
- way too much data to load into memory
- SQLite is quite slow for data fetching big graphs (~1s)
- Rough when you have 1M training samples
3. Data and GraphNeT
Jarred Green - jgreen@mpp.mpg.de
Enter: LMDB
3. Data and GraphNeT
- Lightning Memory-Mapped Database
- Key-value store (not relational)
- Can serialize any data to the value
- Allows for concurrent reads
- Reads around ~10x faster on MAGIC graphs
Main con for me at the moment:
- DB index ↔️ Event ID mapping
Jarred Green - jgreen@mpp.mpg.de
Enter: LMDB
3. Data and GraphNeT
lmdb_dataset.py
needs to be:
- cleaned up
- match the GraphNeT API
- documented
- tests
- etc
Fun hackathon idea?
Jarred Green - jgreen@mpp.mpg.de
4. Results
✅ Particle ID classification
✅ Direction reconstruction
⏳Energy regression
Particle Classification with
DynEdge
- Similar performance to standard analysis on MC
- Only 75k training example per particle type
- Trained on full graphs and cleaned (both work!)
- Only added a cross-attention layer so the two telescopes can talk
Jarred Green - jgreen@mpp.mpg.de
4. Results
✅ Particle ID classification
✅ Direction reconstruction
⏳Energy regression
Direction reconstruction with
DeepIce
- Similar performance to standard analysis on MC
- 2M gamma training samples
- Used a Euclidean loss
- Angular resolution robust enough to do science ✨
- Only slight tweaks to the default configuration
Jarred Green - jgreen@mpp.mpg.de
5. GraphNet Perspectives
- MAGIC Detector Class
- LMDB Dataset Class
- Files that aggregate detectors/datasets
- New losses, tasks, models
What code did I add to get this to work?
So nothing crazy needed to be done at all!
Jarred Green - jgreen@mpp.mpg.de
5. GraphNet Perspectives
- Documentation
- New detectors
- Training and inference
- Installation
- Availability of powerful models for testing
- Custom labels
- Overall mental model
Where did I run into the most/least trouble?
- How to format non-neutrino data
- Graph-level features
- Fixed graph geometry
- Still a bit tough to implement new Dataset formats
Jarred Green - jgreen@mpp.mpg.de
🤠 Is GraphNeT the first multimessenger GNN framework?
5. GraphNet Perspectives
Thank you!
Jarred Green - jgreen@mpp.mpg.de
GraphNeT MAGIC Workshop V
By astrojarred
