Orateurs
Description
Among all of the applications of Machine Learning in HEP, anomaly detection
methods have been receiving a growing interest over the last years. Their use
is especially promising in the development of model independent search tech-
niques. Following this trend line, we propose new algorithms based on the arti-
ficial neural network concept of the Auto-Encoder, augmented with adversarial
training schemes, flow-based approaches, and variable decorrelation techniques.
The performance of our methods is going to be evaluated on the data designed
for the LHC Olympics 2020 challenge [1]. We will present results for both the
RnD dataset and the Black Box datasets proposed for this anomaly detection
competition.
[1] https://lhco2020.github.io/homepage/
