In high-energy particle physics, complex Monte Carlo simulations are needed to connect the theory to measurable quantities. Often, the significant computational cost of these programs becomes a bottleneck in physics analyses. In this contribution, we evaluate an approach based on a Deep Neural Network to reweight simulations to different models or model parameters, using the full kinematic information in the event. This methodology avoids the need for simulating the detector response multiple times by incorporating the relevant variations in a single sample. We test the method on Monte Carlo simulations of top quark pair production used in CMS, that we reweight to different SM parameter values and to different QCD models
We propose the use of Monte Carlo histogram reweighting to extrapolate predictions of machine learni...
The field of high energy physics aims to discover the underlying structure of matter by searching fo...
First-principle simulations are at the heart of the high-energy physics research program. They link ...
In high-energy particle physics, complex Monte Carlo simulations are needed to connect the theory to...
In high-energy particle physics, complex Monte Carlo (MC) simulations are needed to compare theory p...
In particle physics, Monte Carlo (MC) event generators are needed to compare theory to the measured ...
To better understand and identify the four top quark production event in proton-proton collisions at...
Abstract One of the key tasks of any particle collider is measurement. In practice, this is often do...
© 2020 authors. Monte Carlo event generators are an essential tool for data analysis in collider phy...
We describe a multi-disciplinary project to use machine learning techniques based on neural networks...
One burden of high energy physics data analysis is uncertainty within the measurement, both systemat...
In this analysis the usage of deep neural networks for an improved event selection forthe top-quark-...
In this report I will show the application of a deep learning algorithm on a Monte Carlo simulation ...
At the CMS experiment, a growing reliance on the fast Monte Carlo application (FastSim) will accompa...
The discovery of the Higgs boson (H) ten years ago constitutes the pinnacle of the construction of t...
We propose the use of Monte Carlo histogram reweighting to extrapolate predictions of machine learni...
The field of high energy physics aims to discover the underlying structure of matter by searching fo...
First-principle simulations are at the heart of the high-energy physics research program. They link ...
In high-energy particle physics, complex Monte Carlo simulations are needed to connect the theory to...
In high-energy particle physics, complex Monte Carlo (MC) simulations are needed to compare theory p...
In particle physics, Monte Carlo (MC) event generators are needed to compare theory to the measured ...
To better understand and identify the four top quark production event in proton-proton collisions at...
Abstract One of the key tasks of any particle collider is measurement. In practice, this is often do...
© 2020 authors. Monte Carlo event generators are an essential tool for data analysis in collider phy...
We describe a multi-disciplinary project to use machine learning techniques based on neural networks...
One burden of high energy physics data analysis is uncertainty within the measurement, both systemat...
In this analysis the usage of deep neural networks for an improved event selection forthe top-quark-...
In this report I will show the application of a deep learning algorithm on a Monte Carlo simulation ...
At the CMS experiment, a growing reliance on the fast Monte Carlo application (FastSim) will accompa...
The discovery of the Higgs boson (H) ten years ago constitutes the pinnacle of the construction of t...
We propose the use of Monte Carlo histogram reweighting to extrapolate predictions of machine learni...
The field of high energy physics aims to discover the underlying structure of matter by searching fo...
First-principle simulations are at the heart of the high-energy physics research program. They link ...