L2LFlows: Generating High-Fidelity 3D Calorimeter Images

Diefenbacher, Sascha; Eren, E.; Gaede, Frank; Kasieczka, G.; Krause, Claudius; Shekhzadeh, Imahn; Shih, David Q.

doi:10.48550/arxiv.2302.11594

Cited by 3 publications

(4 citation statements)

References 37 publications

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“…Further, to address the sparsity of voxelized showers, a point-cloud representation of the shower is combined with GANs [7] or diffusion [8]. There are many other prior works such as [9][10][11][12], which improves the fast simulation in terms of speed, accuracy, supporting more initial conditions, etc. However, one common aspect in all the above-mentioned methods is that once trained, these models are capable of generating showers for only one detector.…”

Section: Introductionmentioning

confidence: 99%

Transformers for Generalized Fast Shower Simulation

Raikwar,

Cardoso,

Chernyavskaya

et al. 2024

EPJ Web of Conf.

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Recently, transformer-based foundation models have proven to be a generalized architecture applicable to various data modalities, ranging from text to audio and even a combination of multiple modalities. Transformers by design should accurately model the non-trivial structure of particle showers thanks to the absence of strong inductive bias, better modeling of long-range dependencies, and interpolation and extrapolation capabilities. In this paper, we explore a transformer-based generative model for detector-agnostic fast shower simulation, where the goal is to generate synthetic particle showers, i.e., the energy depositions in the calorimeter. When trained with an adequate amount and variety of showers, these models should learn better representations compared to other deep learning models, and hence should quickly adapt to new detectors. In this work, we will show the prototype of a transformer-based generative model for fast shower simulation, as well as explore certain aspects of transformer architecture such as input data representation, sequence formation, and the learning mechanism for our unconventional shower data.

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Section: Introductionmentioning

confidence: 99%

Transformers for Generalized Fast Shower Simulation

Raikwar,

Cardoso,

Chernyavskaya

et al. 2024

EPJ Web of Conf.

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“…A promising alternative approach to potentially speed up simulation is to use a generative model based surrogate simulator. To this end, a plethora of different generative models have been proposed for the task, including Generative Adversarial Networks (GANs) [3][4][5][6][7][8][9][10], Bounded Information Bottleneck Autoencoders (BIB-AEs) [11,12], Wasserstein GANs (WGANS) [13,14], Normalising Flows [15][16][17] and Score-Based Models [18].…”

Section: Introductionmentioning

confidence: 99%

New angles on fast calorimeter shower simulation

Diefenbacher,

Eren,

Gaede

et al. 2023

Mach. Learn.: Sci. Technol.

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The demands placed on computational resources by the simulation requirements of high energy physics experiments motivate the development of novel simulation tools. Machine learning based generative models offer a solution that is both fast and accurate. In this work we extend the Bounded Information Bottleneck Autoencoder (BIB-AE) architecture, designed for the simulation of particle showers in highly granular calorimeters, in two key directions. First, we generalise the model to a multi-parameter conditioning scenario, while retaining a high degree of physics fidelity. In a second step, we perform a detailed study of the effect of applying a state-of-the-art particle flow-based reconstruction procedure to the generated showers. We demonstrate that the performance of the model remains high after reconstruction. These results are an important step towards creating a more general simulation tool, where maintaining physics performance after reconstruction is the ultimate target.

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“…However, they are of a different nature than most current implementations of generative networks which are based on variations of Autoencoders (see e.g. [42][43][44][45][46]), Generative Adversarial Networks (see e.g. [44,47]), Normalizing Flows (see e.g.…”

Section: Introductionmentioning

confidence: 99%

“…[44,47]), Normalizing Flows (see e.g. [46,[48][49][50]), Conditional Invertible Neural Networks (see e.g. [44,48,51]) or even Diffusion models [52].…”

Section: Introductionmentioning

confidence: 99%

Exploring unsupervised top tagging using Bayesian inference

et al. 2023

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Recognizing hadronically decaying top-quark jets in a sample of jets, or even its total fraction in the sample, is an important step in many LHC searches for Standard Model and Beyond Standard Model physics as well. Although there exists outstanding top-tagger algorithms, their construction and their expected performance rely on Montecarlo simulations, which may induce potential biases. For these reasons we develop two simple unsupervised top-tagger algorithms based on performing Bayesian inference on a mixture model. In one of them we use as the observed variable a new geometrically-based observable \tilde{A}_{3}Ã3, and in the other we consider the more traditional \tau_{3}/\tau_{2}τ3/τ2NN-subjettiness ratio, which yields a better performance. As expected, we find that the unsupervised tagger performance is below existing supervised taggers, reaching expected Area Under Curve AUC \sim 0.80-0.81∼0.80−0.81 and accuracies of about 69% -− 75% in a full range of sample purity. However, these performances are more robust to possible biases in the Montecarlo that their supervised counterparts. Our findings are a step towards exploring and considering simpler and unbiased taggers.

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L2LFlows: Generating High-Fidelity 3D Calorimeter Images

Cited by 3 publications

References 37 publications

Transformers for Generalized Fast Shower Simulation

Transformers for Generalized Fast Shower Simulation

New angles on fast calorimeter shower simulation

Exploring unsupervised top tagging using Bayesian inference

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