In-storage computing for multi-messenger astronomy in neutrino experiments and cosmological surveys
Description
This project aims to address the big-data challenges and stringent time constraints facing multi-messenger astronomy (MMA) in neutrino experiments and cosomological surveys. Instead of following the traditional computing paradigm of moving data to th
Detailed example
The output of the AI system is a set of predictions which will be used as the basis for a drastic reduction in the amount of data to be fed to the next stage involving reconstruction and analysis. Before feeding the data to this stage, the AI system will also perform preprocessing operations such as noise removal to facilitate and speed up subsequent data processing.
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
The purpose is to enhance the ability of large scale neutrino experiments like DUNE to detect neutrinos from core-collapse supernovas (CCSNs) and to extract useful information about their source in real time to provide prompt multi-messenger alerts to other observatories. Aside from enabling prompt SN pointing that is also precise, this will cut down the rate of fake SN triggers (curretnly estimated at ~1/month) and therefore offer potential savings from a reduction in the hardware resources required for storing the large amounts of data associated with CCSN candidates.
Audit / financial statement impact
The use case does not have an effect on civil rights/liberties/privacy, access to education/housing/insurance/credit/employment, access to critical government resources/services, human health/safety, critical infrastructure/public safety
Controls / human review
ATO: No; PIA: Not published
Data needed
Simulated data closely approximating real-world raw detector data expected from CCSNs is used to train and validate the ML models used in the data reduction and preprocessing pipeline.