Search

The Large Hadron Collider provides a unique opportunity to study quantum entanglement and violation of Bell inequalities at the highest energy available today. In this paper, we will investigate these quantum correlations with top quark pair production, which represents a system of two-qubits. The spacelike separation requirement for the two causally disconnected top quarks requires they fly relativistically away from each other, which motivates the use of the boosted top-tagging with the semi-leptonic top pair channel. Although measuring the spin polarization of the hadronic top quark is known to be challenging, our study indicates that it is feasible to reconstruct the spin density matrix of the two-qubit system using an optimal hadronic polarimeter. This is achieved with the aid of jet substructure techniques and NN-inspired reconstruction methods, which improve the mapping between subjets and quarks. We find that entanglement can already be observed at more than $5\sigma$ level with existing data, and violation of Bell inequalities may be probed above 4$\sigma$ level at the HL-LHC with 3 ab$^{-1}$ of data., Comment: 11 pages, 6 figures, 1 table

For time-critical IoT applications using deep learning, inference acceleration through distributed computing is a promising approach to meet a stringent deadline. In this paper, we implement a working prototype of a new distributed inference acceleration method HALP using three raspberry Pi 4. HALP accelerates inference by designing a seamless collaboration among edge devices (EDs) in Edge Computing. We maximize the parallelization between communication and computation among the collaborative EDs by optimizing the task partitioning ratio based on the segment-based partitioning. Experimental results show that the distributed inference HALP achieves 1.7x inference acceleration for VGG-16. Then, we combine distributed inference with conventional neural network model compression by setting up different shrinking hyperparameters for MobileNet-V1. In this way, we can further accelerate inference but at the cost of inference accuracy loss. To strike a balance between latency and accuracy, we propose dynamic model selection to select a model which provides the highest accuracy within the latency constraint. It is shown that the model selection with distributed inference HALP can significantly improve service reliability compared to the conventional stand-alone computation., Accepted by European Wireless 2022

We study the potential of deep learning to resolve the combinatorial problem in supersymmetrylike events with two invisible particles at the LHC. As a concrete example, we focus on dileptonic tt¯ events, where the combinatorial problem becomes an issue of binary classification: pairing the correct lepton with each b quark coming from the decays of the tops. We investigate the performance of a number of machine learning algorithms, including attention-based networks, which have been used for a similar problem in the fully hadronic channel of tt¯ production, and the Lorentz Boost Network, which is motivated by physics principles. We then consider the general case when the underlying mass spectrum is unknown, and hence no kinematic end point information is available. Compared against existing methods based on kinematic variables, we demonstrate that the efficiency for selecting the correct pairing is greatly improved by utilizing deep learning techniques.

We study the potential of deep learning to resolve the combinatorial problem in SUSY-like events with two invisible particles at the LHC. As a concrete example, we focus on dileptonic $t \bar t$ events, where the combinatorial problem becomes an issue of binary classification: pairing the correct lepton with each $b$ quark coming from the decays of the tops. We investigate the performance of a number of machine learning algorithms, including attention-based networks, which have been used for a similar problem in the fully-hadronic channel of $t\bar t$ production; and the Lorentz Boost Network, which is motivated by physics principles. We then consider the general case when the underlying mass spectrum is unknown, and hence no kinematic endpoint information is available. Compared against existing methods based on kinematic variables, we demonstrate that the efficiency for selecting the correct pairing is greatly improved by utilizing deep learning techniques., 22 pages, 15 figures, 1 table, matches the published version

A new paradigm for data-driven, model-agnostic new physics searches at colliders is emerging, and aims to leverage recent breakthroughs in anomaly detection and machine learning. In order to develop and benchmark new anomaly detection methods within this framework, it is essential to have standard datasets. To this end, we have created the LHC Olympics 2020, a community challenge accompanied by a set of simulated collider events. Participants in these Olympics have developed their methods using an R&D dataset and then tested them on black boxes: datasets with an unknown anomaly (or not). This paper will review the LHC Olympics 2020 challenge, including an overview of the competition, a description of methods deployed in the competition, lessons learned from the experience, and implications for data analyses with future datasets as well as future colliders., 108 pages, 53 figures, 3 tables

Constraining the Higgs boson properties is a cornerstone of the LHC program and future colliders. In this Snowmass contribution, we study the potential to directly probe the Higgs-top CP-structure via the $t\bar{t}h$ production at the HL-LHC, 100 TeV FCC and muon colliders. We find the limits on the CP phase ($\alpha$) at 95% CL are $|\alpha| \lesssim 36^\circ$ with dileptonic $t\bar t (h\to b\bar b) $ and $|\alpha| \lesssim 25^\circ$ with combined $t\bar t (h\to \gamma\gamma) $ at the HL-LHC. The 100 TeV FCC brings a significant improvement in sensitivity with $|\alpha| \lesssim 3^\circ$ for the dileptonic $t\bar t (h\to b\bar b) $, due to the remarkable gain in the signal cross-section and the increased luminosity. At future muon colliders, we find that the bounds with semileptonic $t\bar t (h\to b\bar b) \nu\bar\nu$ are $|\alpha| \lesssim 9^\circ$ for 10 TeV and $|\alpha| \lesssim 3^\circ$ for 30 TeV, respectively., Comment: 12 pages, 8 figures, contribution to Snowmass 2021

The rapidly-developing intersection of machine learning (ML) with high-energy physics (HEP) presents both opportunities and challenges to our community. Far beyond applications of standard ML tools to HEP problems, genuinely new and potentially revolutionary approaches are being developed by a generation of talent literate in both fields. There is an urgent need to support the needs of the interdisciplinary community driving these developments, including funding dedicated research at the intersection of the two fields, investing in high-performance computing at universities and tailoring allocation policies to support this work, developing of community tools and standards, and providing education and career paths for young researchers attracted by the intellectual vitality of machine learning for high energy physics., Comment: Contribution to Snowmass 2021