RAMjET: RApid Machine lEarned Triage - AI to classify astrophysical phenomena in photometric lightcurves
Description
The MAtISSE project seeks to develop a new approach to rapid, real-time extraction and classification of photometric light curves using a modern differencing technique and advanced DL integrated onto a compact graphics processing unit.
Detailed example
Predictions
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
MAtISSE will develop this technique, which has the potential to greatly reduce the amount of data transmitted by an observatory, for implementation on a future CubeSat-based science payload with a thorough assessment of power requirements vs. processing and communications bandwidth. This technology will be especially applicable to small, power-limited spacecraft and may enable observations and science return that would be challenging or even impossible otherwise.
Controls / human review
ATO: Not reported; PIA: Not published