TinyML Models
Larun uses a federation of specialized TinyML models, each optimized for a specific astronomical detection or analysis task. All models are under 100KB, enabling edge deployment and browser-based inference.
Try These Models Online
Upload FITS files and run instant inference with any of these 12 models in the cloud.
5 free analyses per month • No setup required • Results in <100ms
Download Models for Local Use
Get the complete model bundle for local inference on your own hardware (free forever).
Two Ways to Use These Models
Cloud Platform: Upload FITS files for instant inference (5 free/month, then $9/month for 50 analyses).View plans
Local Download: Download models and run unlimited inference on your own hardware (free forever).Get started
Detection Models
Orbis
TrainedEXOPLANET-001
Transit detection, planet by planet
Primary exoplanet transit detection trained on real Kepler/TESS light curves. Feature-based classifier distinguishing transits, eclipsing binaries, and noise.
Lumina
TrainedVSTAR-001
Every star has a rhythm
Variable star classification. Distinguishes Cepheids, RR Lyrae, Delta Scuti, and other variable star types.
Helios
TrainedFLARE-001
Catch the star's outburst
Stellar flare detection. Feature-based classifier for quiescent, flare, and strong flare states.
Gravitas
TrainedMICROLENS-001
Gravity bends light. We find it.
Microlensing event detection. Feature-based classifier for no event, simple lens, and complex events.
Nova
TrainedSUPERNOVA-001
First to see the burst
Supernova and transient detection. Feature-based classifier for Type I, II supernovae, kilonovae, and TDEs.
Discovery Federation Models
Try Citizen Discovery Engine →Layer-2 server models running in the Citizen Discovery Engine. Inspired by Matteo Paz's VARnet which found 1.5M objects in NEOWISE — these models form the core of larun.space/discover.
Ripple
TrainedVARDET-001
Wavelet-powered variability detection
VARnet-inspired variability detector. Lomb-Scargle + Daubechies wavelet features fed into a Random Forest. Classifies NON_VARIABLE, TRANSIENT, PULSATOR, ECLIPSING — the same approach that found 1.5M objects in NEOWISE.
Sentinel
UnsupervisedANOMALY-001
Nothing unusual escapes it
Isolation Forest anomaly detector on 14-dim feature vectors. Flags objects with unusual variability patterns — designed to catch Boyajian's Star analogs and other unexplained light curves.
Clarity
TrainedDEBLEND-001
Separate what the sky mixed together
Detects blended / contaminated TESS pixels using multi-frequency analysis and pixel-crowding metrics (CROWDSAP, FLFRCSAP). Flags CLEAN, MILD_BLEND, STRONG_BLEND, CONTAMINATED.
Tempo
AlgorithmicPERIODOGRAM-001
Find the beat in the noise
Consensus period finder using 4 methods: Lomb-Scargle, BLS, Phase Dispersion Minimization, and Autocorrelation. Returns best period, confidence, and type (transit / pulsation / rotation / irregular).
Analysis Models
Spectra
TrainedSPECTYPE-001
A star's fingerprint, classified
Stellar spectral classification. Classifies O, B, A, F, G, K, M, L type stars from photometric features.
Resonance
TrainedASTERO-001
Hear what stars are made of
Asteroseismology analysis. Detects solar-like, red giant, delta Scuti, and gamma Dor oscillations.
Forma
TrainedGALAXY-001
Shape tells the story
Galaxy morphology classification. Feature-based classifier for elliptical, spiral, barred spiral, irregular, and merger galaxies.
Run Models Locally
Use our TinyML models on your own hardware with pure NumPy - no TensorFlow required:
# Clone the repository
git clone https://github.com/Paddy1981/larun.git
cd larun && pip install numpy
# Load and run a model
from src.model import get_model
# Basic detection
detector = get_model("EXOPLANET-001")
detector.load("models/trained/EXOPLANET-001_weights.npz")
predictions, confidence = detector.predict(lightcurve_data)
# Multi-view detection (advanced)
from src.model.multiview_exoplanet import MultiViewExoplanetDetector
mv_detector = MultiViewExoplanetDetector()
mv_detector.load("models/trained/MULTIVIEW-EXOPLANET_weights.npz")
probs = mv_detector.forward(global_view, local_view, secondary_view)Model Architecture
All Larun models share a common architecture optimized for TinyML deployment:
Input
Light curve data
Preprocessing
Detrend & normalize
Feature Extraction
1D Conv layers
Classification
Dense + dropout
Output
Probabilities