Speaker
Description
Simulation plays an essential role in modern high energy physics experiments. However, the simulation of particle showers in the calorimeter systems of detectors with traditional Monte Carlo procedures represents a major computational bottleneck, and this subdetector system has long been the focus of fast simulation efforts. More recently, approaches based on deep generative models have shown promise in providing accurate surrogate simulators while delivering significant reductions in compute times.
While a broad range of generative models have been applied to this task in the literature, significantly less attention has been given to incorporating them into the existing software ecosystems. While this is essential for a model to eventually be deployed in a production environment, it also provides a means of evaluating the physics performance of a model after reconstruction. Such a development therefore provides access to a new suite of metrics, which ultimately determine a model’s suitability as a fast simulation tool.
In this contribution we describe DDFastShowerML, a library now available in Key4hep. This generic library provides a means of combining inference of generative models trained to simulate calorimeter showers with the DD4hep toolkit, using the fast simulation hooks that exist in Geant4. This allows a seamless combination of full and fast simulation, making it possible to run fast ML inference in the full simulation of experiments with detector geometries featuring realistic levels of detail, followed by standard reconstruction algorithms. This makes it possible to benchmark generative models with realistic physics analyses and is a prerequisite for eventually using them in an experiment’s Monte Carlo production chain. The flexibility of the library will be demonstrated through examples of different models that have been integrated, and different detector geometries that have been studied. A summary of future development directions will also be given.
Significance
This presentation will detail a generic library that enables the integration of generative ML models for fast calorimeter simulation into full detector simulations with various detector geometries, including those developed for future colliders (e.g. FCC-ee, ILC, etc.). This is significant as it provides access to realistic physics and computational benchmarks, including the full reconstruction chain of these detector concepts.
| Experiment context, if any | Generic R&D/ Future Colliders |
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