Speaker
Rute Pedro
(LIP -Laboratorio de Instrumentacao e Fisica Experimental de Particulas)
Description
In a previous paper we observed that Deep Neural Networks trained on specific signals still performed well in discriminating new signals unseen during training, indicating the transferrable nature of Deep Learning in HEP applications and their potential to perform model-independent searches in the LHC data. Recently, we explored semi-supervised learning techniques - both shallow and deep - and compared their performance at identifying BSM test signals to the ones obtained with the fully supervised counterpart. In particular, we analysed the recently proposed Deep Support Vector Data Description (DeepSVDD) algorithm, which is specifically trained for outlier identification, unlike the Autoencoder family popularly used for anomaly detection.
Primary author
Rute Pedro
(LIP -Laboratorio de Instrumentacao e Fisica Experimental de Particulas)
