Abstract
During Run 2 of the Large Hadron Collider at CERN, the LHCb experiment has spent more than 80% of the pledged CPU time to produce simulated data samples. The upgraded LHCb detector, being commissioned now, will be able to collect much larger data samples, requiring many more simulated events to analyze the collected data. Simulation is a key necessity of analysis to interpret signal, reject background and measure efficiencies. The needed simulation will exceed the pledged resources, requiring an evolution in technologies and techniques to produce these simulated samples. In this contribution, we discuss Lamarr, a Gaudi-based framework to deliver simulated samples parametrizing both the detector response and the reconstruction algorithms. Generative Models powered by several algorithms and strategies are employed to effectively parametrize the high-level response of the multiple components of the LHCb detector, encoding within neural networks the experimental errors and uncertainties introduced in the detection and reconstruction process. Where possible, models are trained directly on real data, leading to a simulation process completely independent of the detailed simulation.
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