Cherenkov detectors fast simulation using neural networks

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Abstract

We propose a way to simulate Cherenkov detector response using a generative adversarial neural network to bypass low-level details. This network is trained to reproduce high level features of the simulated detector events based on input observables of incident particles. This allows the dramatic increase of simulation speed. We demonstrate that this approach provides simulation precision which is consistent with the baseline and discuss possible implications of these results.

Introduction

New runs of the Large Hadron Collider and next generation of colliding experiments with increased luminosity will require an unprecedented amount of simulated events to be produced. This would bring an extreme challenge to the computing resources. Thus new approaches to events generation and simulation of detector responses are needed. Cherenkov detectors, being relatively slow to simulate, are well suited for applying recent approaches to fast simulation. Until recently, the most popular approaches were tabulated response [1] and parameterisation of detector response [2]. While both approaches produce valuable results, they require a significant effort at each retuning. That is why a new way to parameterise the detector response needs to be introduced. This way can be paved using a modern day machine learning tools. The most common approach used now is Generative adversarial neural networks (GANs) [3], while variational autoencoders can also be used [4]. This paper present the first attempt to parameterise a Cherenkov detector response using GANs.

Section snippets

Fast simulation method

GANs provide a rule to connect input observables with distributions of output ones [3]. A first attempt to apply the GAN to fast simulation in physics analyses was performed recently in [5]. This attempt used a Geant4 generated calorimeter response as a training sample with the aim to mimic the low-level detector response.

Our model instead concentrates on the high-level observables reconstruction, thus, effectively bypassing the photon generation stage. This allows us to concentrate on the

Input data sample

As a source of reliable simulated events, we used the FastDIRC [9] simulation of the Detector of Internally Reflected Cherenkov light. This detector was first used by the BaBar experiment [10] and now is going to be introduced in the GlueX [11] experiment. The generation is two-fold: in the first stage a sufficient amount of the photons are generated; in the second stage a kernel density estimation is used to produce the likelihood for particle identification. The biggest problem of the fast

Model study

We construct a neural network for each particle species that takes signal kinematic observables along with a distance to the closest adjacent particle and train it to predict the multidimensional distribution of the likelihoods produced by FastDirc.

The amount of input observables and the architecture of the neural network was optimised to obtain a subpercent quality of the prediction. The final architecture design is a 10-dense-layers neural network, each containing 128 neurons. The model was

Conclusion

We present a novel approach of the fast simulation of Cherenkov detectors. This approach is based on the generative adversarial neural networks and is gives a good precision, while being very fast.

Acknowledgement

The research leading to these results has received funding from the Russian Science Foundation under grant agreement n 17-72-20127.

References (12)

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