Add to ORKGIn this talk, I will present a Generative-Adversarial Network (GAN) based on convolutional neural networks that is used to simulate the production of pairs of jets at the LHC. The GAN is trained on events generated using MadGraph5 + Pythia8, and Delphes3 fast detector simulation. A number of kinematic distributions both at Monte Carlo truth level and after the detector simulation can be reproduced by the generator network with a very good level of agreement
Initial studies have suggested generative adversarial networks (GANs) have promise as fast simulatio...
Deep Learning is becoming a standard tool across science and industry to optimally solve a variety o...
We introduce a generative model to simulate radiation patterns within a jet using the Lund jet plane...
Machine Learning techniques have been used in different applications by the HEP community: in this t...
Machine Learning techniques have been used in different applications by the HEP community: in this t...
https://arxiv.org/abs/1701.05927 We provide a bridge between generative modeling in the Machine Lea...
Deep Learning techniques are being studied for different applications by the HEP community: in this ...
The increasing luminosities of future data taking at Large Hadron Collider and next generation colli...
Deep Learning techniques are being studied for different applications by the HEP community: in this ...
The precise simulation of particle transport through detectors remains a key element for the success...
In high energy physics, one of the most important processes for collider data analysis is the compar...
Using generative adversarial networks (GANs), we investigate the possibility of creating large amoun...
We propose and demonstrate the use of a Model-Assisted Generative Adversarial Network to produce sim...
Various aspects of LHC simulations can be supplemented by generative networks. For event generation ...
Initial studies have suggested generative adversarial networks (GANs) have promise as fast simulatio...
Deep Learning is becoming a standard tool across science and industry to optimally solve a variety o...
We introduce a generative model to simulate radiation patterns within a jet using the Lund jet plane...
Machine Learning techniques have been used in different applications by the HEP community: in this t...
Machine Learning techniques have been used in different applications by the HEP community: in this t...
https://arxiv.org/abs/1701.05927 We provide a bridge between generative modeling in the Machine Lea...
Deep Learning techniques are being studied for different applications by the HEP community: in this ...
The increasing luminosities of future data taking at Large Hadron Collider and next generation colli...
Deep Learning techniques are being studied for different applications by the HEP community: in this ...
The precise simulation of particle transport through detectors remains a key element for the success...
In high energy physics, one of the most important processes for collider data analysis is the compar...
Using generative adversarial networks (GANs), we investigate the possibility of creating large amoun...
We propose and demonstrate the use of a Model-Assisted Generative Adversarial Network to produce sim...
Various aspects of LHC simulations can be supplemented by generative networks. For event generation ...
Initial studies have suggested generative adversarial networks (GANs) have promise as fast simulatio...
Deep Learning is becoming a standard tool across science and industry to optimally solve a variety o...
We introduce a generative model to simulate radiation patterns within a jet using the Lund jet plane...