CaloFlow II: Even Faster and Still Accurate Generation of Calorimeter
Showers with Normalizing Flows
- URL: http://arxiv.org/abs/2110.11377v2
- Date: Fri, 5 May 2023 09:03:45 GMT
- Title: CaloFlow II: Even Faster and Still Accurate Generation of Calorimeter
Showers with Normalizing Flows
- Authors: Claudius Krause and David Shih
- Abstract summary: Recently, we introduced CaloFlow, a high-fidelity generative model for GEANT4 calorimeter shower emulation based on normalizing flows.
Here, we present CaloFlow v2, an improvement on our original framework that speeds up shower generation by a further factor of 500 relative to the original.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Recently, we introduced CaloFlow, a high-fidelity generative model for GEANT4
calorimeter shower emulation based on normalizing flows. Here, we present
CaloFlow v2, an improvement on our original framework that speeds up shower
generation by a further factor of 500 relative to the original. The improvement
is based on a technique called Probability Density Distillation, originally
developed for speech synthesis in the ML literature, and which we develop
further by introducing a set of powerful new loss terms. We demonstrate that
CaloFlow v2 preserves the same high fidelity of the original using qualitative
(average images, histograms of high level features) and quantitative
(classifier metric between GEANT4 and generated samples) measures. The result
is a generative model for calorimeter showers that matches the state-of-the-art
in speed (a factor of $10^4$ faster than GEANT4) and greatly surpasses the
previous state-of-the-art in fidelity.
Related papers
- T2V-Turbo: Breaking the Quality Bottleneck of Video Consistency Model with Mixed Reward Feedback [111.40967379458752]
We introduce T2V-Turbo, which integrates feedback from a mixture of differentiable reward models into the consistency distillation process of a pre-trained T2V model.
Remarkably, the 4-step generations from our T2V-Turbo achieve the highest total score on VBench, even surpassing Gen-2 and Pika.
arXiv Detail & Related papers (2024-05-29T04:26:17Z) - Calo-VQ: Vector-Quantized Two-Stage Generative Model in Calorimeter Simulation [14.42579802774594]
We introduce a novel machine learning method developed for the fast simulation of calorimeter detector response, adapting vector-quantized variational autoencoder (VQ-VAE)
Our model adopts a two-stage generation strategy: compressing geometry-aware calorimeter data into a discrete latent space, followed by the application of a sequence model to learn and generate the latent tokens.
Remarkably, our model achieves the generation of calorimeter showers within milliseconds.
arXiv Detail & Related papers (2024-05-10T17:12:48Z) - Guided Flows for Generative Modeling and Decision Making [55.42634941614435]
We show that Guided Flows significantly improves the sample quality in conditional image generation and zero-shot text synthesis-to-speech.
Notably, we are first to apply flow models for plan generation in the offline reinforcement learning setting ax speedup in compared to diffusion models.
arXiv Detail & Related papers (2023-11-22T15:07:59Z) - Post-training Quantization for Text-to-Image Diffusion Models with Progressive Calibration and Activation Relaxing [49.800746112114375]
We propose a novel post-training quantization method (Progressive and Relaxing) for text-to-image diffusion models.
We are the first to achieve quantization for Stable Diffusion XL while maintaining the performance.
arXiv Detail & Related papers (2023-11-10T09:10:09Z) - CaloClouds II: Ultra-Fast Geometry-Independent Highly-Granular
Calorimeter Simulation [0.0]
Generative machine learning models have been shown to speed up and augment the traditional simulation chain in physics analysis.
A major advancement is the recently introduced CaloClouds model, which generates calorimeter showers as point clouds for the electromagnetic calorimeter of the envisioned International Large Detector (ILD)
In this work, we introduce CaloClouds II which features a number of key improvements. This includes continuous time score-based modelling, which allows for a 25-step sampling with comparable fidelity to CaloClouds while yielding a $6times$ speed-up over Geant4 on a single CPU.
arXiv Detail & Related papers (2023-09-11T18:00:02Z) - Model-based Optimization of Superconducting Qubit Readout [59.992881941624965]
We demonstrate model-based readout optimization for superconducting qubits.
We observe 1.5% error per qubit with a 500ns end-to-end duration and minimal excess reset error from residual resonator photons.
This technique can scale to hundreds of qubits and be used to enhance the performance of error-correcting codes and near-term applications.
arXiv Detail & Related papers (2023-08-03T23:30:56Z) - Inductive Simulation of Calorimeter Showers with Normalizing Flows [0.0]
iCaloFlow is a framework for fast detector simulation based on an inductive series of normalizing flows trained on the pattern of energy depositions in pairs of consecutive calorimeter layers.
As we demonstrate, iCaloFlow can realize the potential of normalizing flows in performing fast, high-fidelity simulation on detector geometries that are 10 - 100 times higher than previously considered.
arXiv Detail & Related papers (2023-05-19T18:00:00Z) - Q-Diffusion: Quantizing Diffusion Models [52.978047249670276]
Post-training quantization (PTQ) is considered a go-to compression method for other tasks.
We propose a novel PTQ method specifically tailored towards the unique multi-timestep pipeline and model architecture.
We show that our proposed method is able to quantize full-precision unconditional diffusion models into 4-bit while maintaining comparable performance.
arXiv Detail & Related papers (2023-02-08T19:38:59Z) - CaloFlow for CaloChallenge Dataset 1 [0.0]
CaloFlow is a new and promising approach to fast calorimeter simulation based on normalizing flows.
We show how it can produce high-fidelity samples with a sampling time that is several orders of magnitude faster than Geant4.
arXiv Detail & Related papers (2022-10-25T18:00:25Z) - GMFlow: Learning Optical Flow via Global Matching [124.57850500778277]
We propose a GMFlow framework for learning optical flow estimation.
It consists of three main components: a customized Transformer for feature enhancement, a correlation and softmax layer for global feature matching, and a self-attention layer for flow propagation.
Our new framework outperforms 32-iteration RAFT's performance on the challenging Sintel benchmark.
arXiv Detail & Related papers (2021-11-26T18:59:56Z) - CaloFlow: Fast and Accurate Generation of Calorimeter Showers with
Normalizing Flows [0.0]
We introduce CaloFlow, a fast detector simulation framework based on normalizing flows.
For the first time, we demonstrate that normalizing flows can reproduce many-channel calorimeter showers with extremely high fidelity.
arXiv Detail & Related papers (2021-06-09T18:00:02Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.