Conveners
Machine Learning, Big Data and Quantum Information
- David Shih (Rutgers University)
Machine Learning, Big Data and Quantum Information
- David Shih (Rutgers University)
As we probe higher energy scales of potential new physics the boost of Standard Model particles can be extremely high. When these decay hadronically their decay products are boosted and therefore collimated such that they can be reconstructed a single large-radius jets with distinctive internal structure. The process of calibrating these jets will be described. Additionally innovative...
From particle identification to the discovery of the Higgs boson, neural network algorithms have become an increasingly important tool for data analysis at the Large Hadron Collider. We present a novel approach to event and particle identification, called end-to-end deep learning, that combines deep learning image classification algorithms with low-level detector representation. Using two...
The proposed link between quantum entanglement and the
apparent thermalization in particle production at the Large
Hadron Collider (Rev. D 98, 054007 (2018)) will be presented.
The large amount of collected data at 13 TeV center of mass
energy in proton-proton collisions has enabled this initial
systematic study of the relationship between Quantum Information
Science and particle...
High-multiplicity all-hadronic final states are an important, but difficult final state for searching for physics beyond the Standard Model. A powerful search method is to look for large jets with accidental substructure due to multiple hard partons falling within a single jet. One way for estimating the background in this search is to exploit an approximate factorization in quantum...
We introduce a potentially powerful new method of searching for new physics at the LHC, using autoencoders and unsupervised deep learning. The key idea of the autoencoder is that it learns to map "normal" events back to themselves, but fails to reconstruct "anomalous" events that it has never encountered before. The reconstruction error can then be used as an anomaly threshold. We demonstrate...
Missing transverse momentum (MET) plays an essential role in many searches for Supersymmetry. However, increasing pile-up and other detector miss-measurements mean that separating signal events from those with no real missing transverse momentum can not always be trivial. The recent improvements in the reconstruction of the MET at the ATLAS experiment will be detailed including the use of...
Machine-learning assisted jet substructure tagging techniques have the potential to significantly improve searches for new particles and Standard Model measurements in hadronic final states. Techniques with simple analytic forms are particularly useful for establishing robustness and gaining physical insight. We will look at a method that applies machine learning to identify the amount of...
Broad resonances are generic predictions of many BSMs. But their discovery is expected to be challenging at the LHC and future collider experiments. It is because traditional resonance searches are based on the invariant mass distribution that will not be sharp enough for a broad resonance.
We used the deep neural network to develop a method to discover broad resonances at collider...
We apply techniques from Bayesian generative statistical modeling to uncover hidden features in jet substructure observables that discriminate between different a priori unknown underlying short distance physical processes in multi-jet events. In particular, we use a mixed membership model known as Latent Dirichlet Allocation to build a data-driven unsupervised top-quark tagger and ttbar event...
