DeepAI
Log In Sign Up

Towards Reliable Neural Generative Modeling of Detectors

The increasing luminosities of future data taking at Large Hadron Collider and next generation collider experiments require an unprecedented amount of simulated events to be produced. Such large scale productions demand a significant amount of valuable computing resources. This brings a demand to use new approaches to event generation and simulation of detector responses. In this paper, we discuss the application of generative adversarial networks (GANs) to the simulation of the LHCb experiment events. We emphasize main pitfalls in the application of GANs and study the systematic effects in detail. The presented results are based on the Geant4 simulation of the LHCb Cherenkov detector.

READ FULL TEXT VIEW PDF

page 1

page 2

page 3

page 4

05/28/2019

Fast Data-Driven Simulation of Cherenkov Detectors Using Generative Adversarial Networks

The increasing luminosities of future Large Hadron Collider runs and nex...
01/16/2019

LHC analysis-specific datasets with Generative Adversarial Networks

Using generative adversarial networks (GANs), we investigate the possibi...
10/05/2020

Data Augmentation at the LHC through Analysis-specific Fast Simulation with Deep Learning

We present a fast simulation application based on a Deep Neural Network,...
09/03/2020

Simulation of an Elevator Group Control Using Generative Adversarial Networks and Related AI Tools

Testing new, innovative technologies is a crucial task for safety and ac...
03/28/2019

Cherenkov Detectors Fast Simulation Using Neural Networks

We propose a way to simulate Cherenkov detector response using a generat...
05/31/2021

The use of Generative Adversarial Networks to characterise new physics in multi-lepton final states at the LHC

Semi-supervision in Machine Learning can be used in searches for new phy...

1 Introduction

At the moment, the Large Hadron Collider (LHC) is preparing for the data-taking period, Run 3, for which it is planned to increase the luminosity for LHCb experiment 

[1]. The order of magnitude increase in luminosity will require a large number of simulated events to perform physics analyses. Since pledges on computing resources don’t scale as fast as luminosity, traditional detector simulation techniques based on Monte Carlo methods (MC) modelling the radiation-matter interactions [2, 3]

must be complemented and partially replaced with Fast Simulation options. An interesting alternative to detailed simulation are parametric simulations, where the relationship between incident particle kinematics and observables is obtained using physics-motivated relations. With one of the possible universal approximators being neural networks, machine-learning driven simulation methods 

[4] are getting more and more popular in high-energy physics experiments [5, 6, 7, 8, 9].

The LHC includes four main experiments: ALICE, ATLAS, CMS, and LHCb. The latter, LHCb, is a single-arm forward spectrometer originally conceived for studies on CP-symmetry violations and rare decays in the -sector. For the purpose of many of these measurements, LHCb is equipped with two Ring Cherenkov Detectors (RICH) optimized to allow an excellent kaon-pion separation within a wide range of momentum and pseudorapidity and to provide, in general, outstanding Particle Identification (PID) performance [10]. Full simulation of the RICH detectors is one of the most CPU-expensive step in the LHCb simulation since it requires accurate modeling of optical photon propagation with diffraction and absorption effects, as well as processes with low-energy secondary electrons [11].

In this article, we discuss a GAN-based approach for an ultra-fast machine-learning driven simulation of the RICH sub-detectors of the LHCb experiment. We continue developing the previously proposed approach [12] for training GANs on real data, with the main emphasis on evaluating the systematic effects arising due to effective neural-network based parameterization. This is done using LHCb simulated samples.

2 RICH detector and its data

The principle of operation of the RICH sub-detector is based on the Cherenkov effect. A particle moving through the medium at a velocity higher than the phase velocity of light in the medium emits Cherenkov photons. The photons are emitted in a cone whose spread angle is a function of the particle’s velocity. Measuring this angle, via the radius of a reflected ring, and knowing its momentum, allows to identify the particle by constraining its mass.

The quantities obtained from the RICH reconstruction algorithm are RichDLLx where x can denote kaons - k, protons - p, muons - mu, electrons - e and below threshold - bt. RichDLLx for each track is defined in terms of the difference between the logarithmic likelihood for a given particle type hypothesis and the pion hypothesis for that track [13] as

where
– likelihood to observe a given picture, as a function of all charged particle types,
– hypothesized particle type for track ,
– a pion hypothesis,
– a hypothesis maximizing is searched for.

The task of the data-driven approach to modeling the RICH detector is to learn how to simulate the distributions of RichDLLx values for different decays of interest for physics measurements. In our previous work [12], we show that fast simulation of the RICH detectors can be accurately performed using GANs [14]. The proposed model shows good approximation to the real data distributions. This approach allows to speed-up the simulations production with respect to with detailed simulation, and, in addition, being trained using real data it is not affected by the intrinsic bias of simulation. However, it requires a study of effects arising due to effective GAN parameterization.

In this article, we explore whether our model allows us to control systematic uncertainties in a real physics analysis scenario. We study how well our model generalizes to the decays not seen during training. For this we use MC samples. We use the track reconstructed data as input conditional variables: momentum (), pseudorapidity (

) and number of hits in the Scintillating Pad Detector (nSPDHits) 

[15].

3 Our model architecture

Generative Adversarial Networks (GAN) is a powerful class of generative models based on the simultaneous training of two neural network. The first network, called generator, generates synthetic samples, while the other one, called discriminator, tries to distinguish real samples from those produced by the generator. These two networks learn to compete with each other in a zero-sum game. In this way, the generator learns to produce samples that do not differ from the real ones.

As a starting point for our model, we use the Cramér-GAN [16]. This GAN flavor uses a metric between distributions, called the Energy distance (multivariate generalization of the Cramér distance). It preserves all the nice properties of the Wasserstein GAN [17], while solving the biased gradients problem [16].

The architecture of our neural network is shown in Figure 1. This architecture is demonstrated to be sufficient [12] to describe the RICH variables with high accuracy when trained on the reconstructed calibration samples from the real data obtained with the LHCb detector [18]. Calibration samples are special datasets selected and reconstructed avoiding selection bias on a set of probe particle species. With a novel data-driven training based on the sWeights background subtraction [19, 20] the quality of the description obtained is sufficiently high.

Figure 1: The architecture of our model

4 Results

The purpose of this study is to show the transferability of our models to decays not present in the training set. In order to work with clean decay signatures, the study is performed on the detailed MC samples. We want to emphasize that while this procedure is performed using simulated samples, globally, our model is designed to be trained using real data samples. In this paper, we show our results of training the GAN on muons from a mixture of simulated events: inclusive and and evaluating this GAN on the test decay.

Figure 2: Histograms of distributions of real and generated output variables for the test decay .

Figure 2 shows the RichDLLx variable distributions for the detailed simulation and data generated by our GAN on the test decay channel. These results show that the GAN performance remains stable despite the change of training set (from real data sample to simulated one). However, since not only the global distribution match is important in physics analysis, we also study the quality of the description as a function of the input parameters. To do that, we introduce the efficiency ratio metric as follows.

  1. Measure the efficiency of RichDLLx

    cuts at various quantiles of the

    RichDLLx distribution:

  2. Do this as a function of the input variables: .

  3. Calculate the efficiency ratio between GAN predictions and simulated events (in bins of a variable):

Figure 3 shows dependence of RichDLLmu

efficiency ratio between GAN predictions and detailed simulated events on input variables for three quartiles. In case of an ideal trained GAN this ratio should be close to 1, thus showing that the efficiencies can be predicted precisely using GAN model. We have good agreement between simulation and data in three projections, although quality of agreement degrades in the tails of the distributions.

Figure 3: Dependence of RichDLLmu efficiency ratio on input variables: momentum (P), pseudorapidity (), number of hits in the Scintillating Pad Detector (nSPDHits) for the test decays unseen by GAN during training.

Figure 4 shows the dependence of RichDLLmu efficiency ratio on the momentum (P) with 75, 90, and 95% selection efficiencies. In this region, one can see that the quality of the description degrades on the tails of the distributions. At low momenta, the difference can be up to 50%. While this problem is quite significant, we do would like to stress that we are talking about the tails of the distribution, thus the overall description is not affected significantly. We expect the problem to be less pronounced as learning statistics and model complexity increase.

Figure 4: Dependence of RichDLLmu efficiency ratio on momentum (P).

5 Conclusion

We show that using a GAN-based approach for fast simulation of RICH sub-detectors in LHCb provides good description of the efficiencies. Some effects observed in the tails of the distribution do not affect the overall conclusion. After testing the quality on the decays unseen during training, we conclude that the description transfer is also robust and thus can be used for real-life physics analysis in LHCb.

Acknowledgement

The research leading to these results has received funding from Russian Science Foundation under grant agreement 17-72-20127. This research was supported in part through computational resources of HPC facilities at HSE University [21].

References

References

  • [1] Fartoukh S, Kostoglou S, Solfaroli Camillocci M, Arduini G, Bartosik H, Bracco C, Brodzinski K, Bruce R, Buffat X, Calviani M, Cerutti F, Efthymiopoulos I, Goddard B, Iadarola G, Karastathis N, Lechner A, Metral E, Mounet N, Nuiry F X, Papadopoulou P S, Papaphilippou Y, Petersen B, Persson T H B, Redaelli S, Rumolo G, Salvant B, Sterbini G, Timko H, Tomas Garcia R and Wenninger J 2021 LHC Configuration and Operational Scenario for Run 3 Tech. rep. CERN Geneva URL https://cds.cern.ch/record/2790409
  • [2] Ferrari A, Sala P R, Fassò A and Ranft J 2005 FLUKA: A multi-particle transport code (program version 2005) CERN Yellow Reports: Monographs (Geneva: CERN) URL https://cds.cern.ch/record/898301
  • [3] Agostinelli S et al. (GEANT4) 2003 Nucl. Instrum. Meth. A506 250–303
  • [4] Paganini M, de Oliveira L and Nachman B 2018 Phys. Rev. Lett. 120 042003 (Preprint 1705.02355)
  • [5] Maevskiy A, Ratnikov F, Zinchenko A and Riabov V 2021 Eur. Phys. J. C 81 599 (Preprint 2012.04595)
  • [6] Chekalina V, Orlova E, Ratnikov F, Ulyanov D, Ustyuzhanin A and Zakharov E 2019 EPJ Web Conf. 214 02034 (Preprint 1812.01319)
  • [7] 2020 Fast simulation of the ATLAS calorimeter system with Generative Adversarial Networks Tech. rep. CERN Geneva all figures including auxiliary figures are available at https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PUBNOTES/ATL-SOFT-PUB-2020-006 URL https://cds.cern.ch/record/2746032
  • [8] Musella P and Pandolfi F 2018 Comput. Softw. Big Sci. 2 8 (Preprint 1805.00850)
  • [9] Fanelli C and Pomponi J 2019 Mach. Learn. Sci. Tech. 1 015010 (Preprint 1911.11717)
  • [10] Adinolfi M et al. (LHCb RICH Group) 2013 Eur. Phys. J. C 73 2431 (Preprint 1211.6759)
  • [11] Easo S, Belyaev I, Corti G, Jones C, Papanestis A, Pokorski W, Ranjard F and Robbe P 2005 IEEE Trans. Nucl. Sci. 52 1665–1668
  • [12] Maevskiy A, Derkach D, Kazeev N, Ustyuzhanin A, Artemev M and Anderlini L 2020 Journal of Physics: Conference Series 1525 012097 URL https://doi.org/10.1088/1742-6596/1525/1/012097
  • [13] Forty R W and Schneider O 1998 CERN–LHCb–98–040
  • [14] Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A and Bengio Y 2014 Advances in Neural Information Processing Systems vol 27 ed Ghahramani Z, Welling M, Cortes C, Lawrence N and Weinberger K Q (Curran Associates, Inc.) URL https://proceedings.neurips.cc/paper/2014/file/5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdf
  • [15] Picatoste Olloqui E (LHCb) 2009 J. Phys. Conf. Ser. 160 012046
  • [16] Bellemare M G, Danihelka I, Dabney W, Mohamed S, Lakshminarayanan B, Hoyer S and Munos R 2017 CoRR abs/1705.10743 (Preprint 1705.10743) URL http://arxiv.org/abs/1705.10743
  • [17] Arjovsky M, Chintala S and Bottou L 2017 Wasserstein gan (Preprint 1701.07875)
  • [18] Aaij R et al. 2019 EPJ Tech. Instrum. 6 1 (Preprint 1803.00824)
  • [19] Pivk M and Le Diberder F 2005 Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 555 356–369 ISSN 0168-9002 URL http://dx.doi.org/10.1016/j.nima.2005.08.106
  • [20] Borisyak M and Kazeev N 2019 Journal of Instrumentation 14 P08020–P08020 ISSN 1748-0221 URL http://dx.doi.org/10.1088/1748-0221/14/08/P08020
  • [21] Kostenetskiy P S, Chulkevich R A and Kozyrev V I 2021 Journal of Physics: Conference Series 1740 012050 URL https://doi.org/10.1088/1742-6596/1740/1/012050