Hadrons, Better, Faster, Stronger

Buhmann, Erik; Diefenbacher, Sascha; Eren, E.; Gaede, Frank; Daniel, Hundhausen,; Kasieczka, G.; Korcari, William; Krüger, K.; McKeown, Peter P.; Rustige, Lennart

doi:10.48550/arxiv.2112.09709

Cited by 3 publications

(6 citation statements)

References 26 publications

(37 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We project the cells with energy depositions (hits) onto a rectangular grid of 30 × 30 × 30 cells. We choose photon showers, because their structure is more regular and faster to learn than the structure of pion showers [32].…”

Section: Dataset and Modelmentioning

confidence: 99%

“…The goal of this paper is to study the statistical amplification of deep generative models, focusing on interpolation from the smoothness inductive bias, for detector simulations as a realistic and highly relevant application. Fast surrogate models for detector simulations have been developed [13][14][15][16][17][18][19][20][21][22][23][24][25] and improved [26][27][28][29][30][31][32][33][34][35][36][37][38][39][40] to the level that they are ready to be used in the upcoming LHC runs. In fact, the ATLAS Collaboration has already integrated a Generative Adversarial Network (GAN) into its fast calorimeter simulation and will use it to generate over a billion events [41,42].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Calomplification -- The Power of Generative Calorimeter Models

Bieringer,

Butter,

Diefenbacher

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Motivated by the high computational costs of classical simulations, machine-learned generative models can be extremely useful in particle physics and elsewhere. They become especially attractive when surrogate models can efficiently learn the underlying distribution, such that a generated sample outperforms a training sample of limited size. This kind of GANplification has been observed for simple Gaussian models. We show the same effect for a physics simulation, specifically photon showers in an electromagnetic calorimeter.

show abstract

Section: Dataset and Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Calomplification -- The Power of Generative Calorimeter Models

Bieringer,

Butter,

Diefenbacher

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Without further modifications or post-processing, samples from VAEs usually have comparatively low quality. This setup, including modifications and post-processing, has been considered for calorimeter simulations in high-energy physics [101,[112][113][114][115][116][117].…”

Section: Learning the Simulated Particle Interactions With Matter Thr...mentioning

confidence: 99%

“…Once trained well, GANs generate realistically-looking samples across various domains. Hence, there have been several applications of GANs to calorimeter shower simulation [100,101,112,116,.…”

Section: Learning the Simulated Particle Interactions With Matter Thr...mentioning

confidence: 99%

“…There are, however, differences in sampling time and quality that depend on the used architecture. Given this trade off, current applications of deep generative models at the LHC and beyond usually focus on a higher sampling speed, since a suboptimal performance in shower quality tends to matter less in downstream tasks [116]. For the purpose of optimizing a calorimeter, however, a better sampling quality, i.e.…”

Section: Hybrid Calorimeter For a Future Particle Collidermentioning

confidence: 99%

See 1 more Smart Citation

Toward the End-to-End Optimization of Particle Physics Instruments with Differentiable Programming: a White Paper

Dorigo¹,

Giammanco²,

Vischia³

et al. 2022

Preprint

View full text Add to dashboard Cite

The full optimization of the design and operation of instruments whose functioning relies on the interaction of radiation with matter is a superhuman task, given the large dimensionality of the space of possible choices for geometry, detection technology, materials, data-acquisition, and informationextraction techniques, and the interdependence of the related parameters. On the other hand, massive potential gains in performance over standard, "experience-driven" layouts are in principle within our reach if an objective function fully aligned with the final goals of the instrument is maximized by means of a systematic search of the configuration space. The stochastic nature of the involved quantum processes make the modeling of these systems an intractable problem from a classical statistics point of view, yet the construction of a fully differentiable pipeline and the use of deep learning techniques may allow the simultaneous optimization of all design parameters.In this document we lay down our plans for the design of a modular and versatile modeling tool for the end-to-end optimization of complex instruments for particle physics experiments as well as industrial and medical applications that share the detection of radiation as their basic ingredient. We consider a selected set of use cases to highlight the specific needs of different applications.

show abstract

EPiC-GAN: Equivariant Point Cloud Generation for Particle Jets

Buhmann¹,

Kasieczka²,

Thaler³

2023

Preprint

View full text Add to dashboard Cite

With the vast data-collecting capabilities of current and future high-energy collider experiments, there is an increasing demand for computationally efficient simulations. Generative machine learning models enable fast event generation, yet so far these approaches are largely constrained to fixed data structures and rigid detector geometries. In this paper, we introduce EPiC-GAN -equivariant point cloud generative adversarial network -which can produce point clouds of variable multiplicity. This flexible framework is based on deep sets and is well suited for simulating sprays of particles called jets. The generator and discriminator utilize multiple EPiC layers with an interpretable global latent vector. Crucially, the EPiC layers do not rely on pairwise information sharing between particles, which leads to a significant speedup over graph-and transformer-based approaches with more complex relation diagrams. We demonstrate that EPiC-GAN scales well to large particle multiplicities and achieves high generation fidelity on benchmark jet generation tasks.

show abstract

Hadrons, Better, Faster, Stronger

Cited by 3 publications

References 26 publications

Calomplification -- The Power of Generative Calorimeter Models

Calomplification -- The Power of Generative Calorimeter Models

Toward the End-to-End Optimization of Particle Physics Instruments with Differentiable Programming: a White Paper

EPiC-GAN: Equivariant Point Cloud Generation for Particle Jets

Contact Info

Product

Resources

About