Collider events are naturally described as sets of particles which have variable size and are inherently permutation symmetric. Machine learning architectures operating on collider events should ideally be able to handle variably sized inputs and be manifestly symmetric with respect to particle ordering. Building off of the recently developed Deep Sets paradigm, which is designed for learning...
How to represent a jet is one of the key aspects of machine learning algorithms for jet physics. Motivated by recent progress in machine learning community on point cloud recognition, we propose a new approach that represents a jet as an unordered set of particles with their measured properties, effectively a "particle" cloud. Specialized algorithms for point cloud recognition, e.g., Dynamic...
QCD calculations that resum soft-collinear logarithms by a parton-shower algorithm can not currently be used in PDF fits. This is due to the high computational cost of generating Monte-Carlo events for each variation of the PDFs, and reduces the number of data points available for the fits. We propose an approximation based on training a NN to predict the effect of varying the shower input...
Quark-gluon discrimination could greatly improve the sensitivity of a
number of analyses at the LHC, and as such has received a significant
amount of investigation. Because the differences between quark and
gluon jets are largely contained in the jet substructure and are often
very subtle, this problem lends itself to machine learning techniques.
We explore this question in the...
In this talk, we present two applications of deep learning in the areas of top quark identification and electromagnetic shower generation.
As deep learning methods are adopted for high energy physics, increasing attention is given to the development of dedicated architectures incorporating physical knowledge. We introduce a model that utilizes our knowledge of particle combinations and...
