Add to ORKGWe propose using neural networks to detect data departures from a given reference model, with no prior bias on the nature of the new physics responsible for the discrepancy. The model-independent nature of our approach, and its ability to deal with rare signals such as those expected at the LHC, is quantitatively assessed in toy examples
Searching for new physics, i.e., physical laws that go beyond the reference models, is the absolute ...
International audienceWe discuss a method that employs a multilayer perceptron to detect deviations ...
We propose a new scientific application of unsupervised learning techniques to boost our ability to ...
I will describe an approach to search for new phenomena in data, by detecting discrepancies between ...
We propose using neural networks to detect data departures from a given reference model, with no pri...
We propose using neural networks to detect data departures from a given reference model, with no pri...
New Physics Learning Machine (NPLM) is a machine-learning based strategy to detect data departures f...
We investigate enhancing the sensitivity of new physics searches at the LHC by machine learning in t...
New Physics Learning Machine (NPLM) is a machine-learning based strategy to detect data departures f...
We use Machine Learning(ML) techniques to exploit kinematic information in VH, the production of a H...
We study the prospects of characterising Dark Matter at colliders using Machine Learning (ML) techn...
A method for correcting for detector smearing effects using machine learning techniques is presented...
Many proposed extensions to the Standard Model of particle physics predict long-lived particles, whi...
We discuss a method that employs a multilayer perceptron to detect deviations from a reference model...
Applications of machine learning tools to problems of physical interest are often criticized for pro...
Searching for new physics, i.e., physical laws that go beyond the reference models, is the absolute ...
International audienceWe discuss a method that employs a multilayer perceptron to detect deviations ...
We propose a new scientific application of unsupervised learning techniques to boost our ability to ...
I will describe an approach to search for new phenomena in data, by detecting discrepancies between ...
We propose using neural networks to detect data departures from a given reference model, with no pri...
We propose using neural networks to detect data departures from a given reference model, with no pri...
New Physics Learning Machine (NPLM) is a machine-learning based strategy to detect data departures f...
We investigate enhancing the sensitivity of new physics searches at the LHC by machine learning in t...
New Physics Learning Machine (NPLM) is a machine-learning based strategy to detect data departures f...
We use Machine Learning(ML) techniques to exploit kinematic information in VH, the production of a H...
We study the prospects of characterising Dark Matter at colliders using Machine Learning (ML) techn...
A method for correcting for detector smearing effects using machine learning techniques is presented...
Many proposed extensions to the Standard Model of particle physics predict long-lived particles, whi...
We discuss a method that employs a multilayer perceptron to detect deviations from a reference model...
Applications of machine learning tools to problems of physical interest are often criticized for pro...
Searching for new physics, i.e., physical laws that go beyond the reference models, is the absolute ...
International audienceWe discuss a method that employs a multilayer perceptron to detect deviations ...
We propose a new scientific application of unsupervised learning techniques to boost our ability to ...