Speaker
Description
The interTwin project develops an open-source Digital Twin Engine to integrate application-specific Digital Twins (DTs) across scientific domains. Its framework for the development of DTs supports interoperability, performance, portability and accuracy. As part of this initiative, we implemented the CaloINN normalizing-flow model for calorimeter simulations within the interTwin framework. Calorimeter shower simulations are computationally expensive, and generative models offer an efficient alternative. However, achieving a balance between accuracy and speed remains a challenge, with distribution tail modeling being a key limitation. CaloINN provides a trade-off between simulation quality and efficiency. The ongoing study targets validating the model using high granularity simulations from the Open Data Detector, as well as introducing a set of post-processing modifications of analysis-level observables aimed at improving the accuracy of distribution tails. This work demonstrates the applicability of the interTwin platform for high-energy physics simulations and highlights its potential for broader scientific use.
| AI keywords | normalizing flow, generative model, post-processing |
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