Conveners
Anomaly detection (LHCO)
- Gregor Kasieczka (Hamburg University (DE))
- David Shih (Rutgers University)
We have developed a framework for performing unsupervised new physics searches using jet substructure in di-jet events, where the likelihood functions are inferred in a data-driven manner. This framework is based on a machine learning algorithm called Latent Dirichlet Allocation, a statistical model initially used for describing the topical structure of documents. In this talk I will present...
As our jet classifiers grow in complexity, limitations in simulating QCD will start to bottleneck our ability to train classifiers that perform as well on data as they do in simulation. One proposed approach to avoid this problem is the CWoLa method, in which the classifier is trained directly on data to distinguish between statistical mixtures of classes. The main challenge when applying...
We present a detailed study on Variational Autoencoders (VAEs) performing in anomalous jet tagging. By taking in low-level jet constituents' information, and only training with background jets in an unsupervised manner, the VAE is able to encode important information for reconstructing jets, while learning an expressive posterior distribution in the latent space. The encoder (inference) and...
We present results of an anomaly detection method via sequence modeling.
We will unveil the answer to Black Box 1 and discuss the outcomes of the challenge. Note that there are two talks here - one that was hurriedly prepared for the unveiling of the results at the time of the challenge and a second, polished set of slides that we have prepared without time pressure after the workshop.
Implementation is still in progress. In principle Deep Neural Networks with Keras.
