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Tao Han

Publications and source records attributed to Tao Han.

At least 145 records · Page 8Linked to original sources
arXiv2022

Leptonic Scalars and Collider Signatures in a UV-complete Model

We study the non-standard interactions of neutrinos with light leptonic scalars ($ϕ$) in a global $(B-L)$-conserved ultraviolet (UV)-complete model. The model utilizes Type-II seesaw motivated neutrino interactions with an $SU(2)_L$-triplet scalar, along with an additional singlet in the scalar sector. This UV-completion leads to an enriched spectrum and consequently new observable signatures. We examine the low-energy lepton flavor violation constraints, as well as the perturbativity and unitarity constraints on the model parameters. Then we lay out a search strategy for the unique signature of the model resulting from the leptonic scalars at the hadron colliders via the processes $H^{\pm\pm} \to W^\pm W^\pm ϕ$ and $H^\pm \to W^\pm ϕ$ for both small and large leptonic Yukawa coupling cases. We find that via these associated production processes at the HL-LHC, the prospects of doubly-charged scalar $H^{\pm\pm}$ can reach up to 800 (500) GeV and 1.1 (0.8) TeV at the $2σ\ (5σ)$ significance for small and large Yukawa couplings, respectively. A future 100 TeV hadron collider will further increase the mass reaches up to 3.8 (2.6) TeV and 4 (2.7) TeV, at the $2σ\ (5σ)$ significance, respectively. We also demonstrate that the mass of $ϕ$ can be determined at about 10% accuracy at the LHC for the large Yukawa coupling case even though it escapes as missing energy from the detectors.

hep-ph↗
arXiv2022

Vector Boson Scattering Processes: Status and Prospects

Insight into the electroweak (EW) and Higgs sectors can be achieved through measurements of vector boson scattering (VBS) processes. The scattering of EW bosons are rare processes that are precisely predicted in the Standard Model (SM) and are closely related to the Higgs mechanism. Modifications to VBS processes are also predicted in models of physics beyond the SM (BSM), for example through changes to the Higgs boson couplings to gauge bosons and the resonant production of new particles. In this review, experimental results and theoretical developments of VBS at the Large Hadron Collider, its high luminosity upgrade, and future colliders are presented.

hep-ph↗
arXiv2022

DR.VIC: Decomposition and Reasoning for Video Individual Counting

Pedestrian counting is a fundamental tool for understanding pedestrian patterns and crowd flow analysis. Existing works (e.g., image-level pedestrian counting, crossline crowd counting et al.) either only focus on the image-level counting or are constrained to the manual annotation of lines. In this work, we propose to conduct the pedestrian counting from a new perspective - Video Individual Counting (VIC), which counts the total number of individual pedestrians in the given video (a person is only counted once). Instead of relying on the Multiple Object Tracking (MOT) techniques, we propose to solve the problem by decomposing all pedestrians into the initial pedestrians who existed in the first frame and the new pedestrians with separate identities in each following frame. Then, an end-to-end Decomposition and Reasoning Network (DRNet) is designed to predict the initial pedestrian count with the density estimation method and reason the new pedestrian's count of each frame with the differentiable optimal transport. Extensive experiments are conducted on two datasets with congested pedestrians and diverse scenes, demonstrating the effectiveness of our method over baselines with great superiority in counting the individual pedestrians. Code: https://github.com/taohan10200/DRNet.

cs.CV↗
arXiv2022

Electroweak fragmentation at high energies: A Snowmass White Paper

In particle collisions at energies higher than the mass scale involved, the collinear splitting becomes the dominant phenomena. Suitable descriptions of the physics in this regime include the parton distribution functions, the initial state radiations, the final state radiations and the fragmentation functions. It is of fundamental importance to formulate those functions with consistent theoretical treatments, and to provide the adequate formalism for applications, as motivated by the recent discussions of partonic scatterings at the multi-TeV energy regime. In this report, we briefly present the resummation of the final-state logarithms as fragmentation functions for the electroweak processes. As an explicit example, we study an electroweak gauge boson splitting in the process $pp\to WZj$ and demonstrate the important effects from the fragmentation.

hep-ph↗
arXiv2022

DeepMix: Mobility-aware, Lightweight, and Hybrid 3D Object Detection for Headsets

Mobile headsets should be capable of understanding 3D physical environments to offer a truly immersive experience for augmented/mixed reality (AR/MR). However, their small form-factor and limited computation resources make it extremely challenging to execute in real-time 3D vision algorithms, which are known to be more compute-intensive than their 2D counterparts. In this paper, we propose DeepMix, a mobility-aware, lightweight, and hybrid 3D object detection framework for improving the user experience of AR/MR on mobile headsets. Motivated by our analysis and evaluation of state-of-the-art 3D object detection models, DeepMix intelligently combines edge-assisted 2D object detection and novel, on-device 3D bounding box estimations that leverage depth data captured by headsets. This leads to low end-to-end latency and significantly boosts detection accuracy in mobile scenarios. A unique feature of DeepMix is that it fully exploits the mobility of headsets to fine-tune detection results and boost detection accuracy. To the best of our knowledge, DeepMix is the first 3D object detection that achieves 30 FPS (an end-to-end latency much lower than the 100 ms stringent requirement of interactive AR/MR). We implement a prototype of DeepMix on Microsoft HoloLens and evaluate its performance via both extensive controlled experiments and a user study with 30+ participants. DeepMix not only improves detection accuracy by 9.1--37.3% but also reduces end-to-end latency by 2.68--9.15x, compared to the baseline that uses existing 3D object detection models.

cs.CV↗
arXiv2022

WIMP Dark Matter at High Energy Muon Colliders $-$A White Paper for Snowmass 2021

In a previous publication, we showed that a high energy muon collider can make decisive statements about the electroweak (WIMP) Dark Matter (DM), reaching a DM mass which could give the observed thermal relic abundance. In this document, we report new studies of the spin-$0$ minimal WIMP DM at high energy muon colliders, and update our results on the fermionic spin-$1/2$ case. We find that, by combining multiple inclusive missing mass search channels, it is possible to fully cover the thermal targets of fermionic and scalar doublets, and Dirac triplet, with a 10 TeV muon collider. Higher energies, 14 TeV$-$30 TeV, would be able to cover the thermal targets of Majorana and scalar triplet. For direct discovery of the higher EW multiplets with $n\geq5$, one may need to go beyond a 30 TeV muon collider to fully cover their thermal mass expectation.

hep-ph↗
arXiv2022

BSM $ν$ physics: complementarity across energies -- a white paper for Snowmass 2021

We reiterate that there is significant complementarity between low-energy experiments and high-energy colliders in exploring new physics associated with neutrino properties and their mass generation mechanisms. Signals of the new physics in the two energy regimes may be correlated with each other from the same underlying dynamics. We demonstrate the complementary nature by presenting the physics reaches for the Seesaw models of Type I, II and III, and for general neutrino interactions in an effective field theory framework, and in a $Z'$ model.

hep-ph↗
arXiv2022

Axion-Like Particles at High Energy Muon Colliders -- A White paper for Snowmass 2021

We study the discovery potential for heavy axion-like particles (ALPs) and the perspectives for determining their coupling properties at a muon collider. Focusing on their couplings to the Standard Model (SM) gauge bosons $γ, Z, W^\pm$, we show that a high-energy muon collider can substantially extend the mass coverage, essentially reaching the kinematic limit of the collider energy. The unique kinematics allow for non-ambiguous determination of the individual coupling strengths. The associated production via $μ^+μ^-$ annihilation and the VBF processes with the tagged outgoing muons can be utilized to verify the CP property of the ALPs. We illustrate our results for a muon collider running at 3 TeV and 10 TeV.

hep-ph↗
arXiv2022

Neutrino Self-Interactions: A White Paper

Neutrinos are the Standard Model (SM) particles which we understand the least, often due to how weakly they interact with the other SM particles. Beyond this, very little is known about interactions among the neutrinos, i.e., their self-interactions. The SM predicts neutrino self-interactions at a level beyond any current experimental capabilities, leaving open the possibility for beyond-the-SM interactions across many energy scales. In this white paper, we review the current knowledge of neutrino self-interactions from a vast array of probes, from cosmology, to astrophysics, to the laboratory. We also discuss theoretical motivations for such self-interactions, including neutrino masses and possible connections to dark matter. Looking forward, we discuss the capabilities of searches in the next generation and beyond, highlighting the possibility of future discovery of this beyond-the-SM physics.

hep-ph↗
arXiv2022

Heavy Higgs Bosons in 2HDM at a Muon Collider

We study the discovery potential of the non-Standard Model (SM) heavy Higgs bosons in the Two-Higgs-Doublet Models (2HDMs) at a multi-TeV muon collider and explore the discrimination power among different types of 2HDMs. We find that the pair production of the non-SM Higgs bosons via the universal gauge interactions is the dominant mechanism once above the kinematic threshold. Single Higgs boson production associated with a pair of heavy fermions could be important in the parameter region with enhanced Yukawa couplings. For both signal final states, $μ^+μ^-$ annihilation channels dominate over the vector boson fusion (VBF) processes, except at high center of mass energies where the VBF processes receive large logarithmic enhancement with the increase of energies. Single Higgs boson $s$-channel production in $μ^+μ^-$-annihilation via the radiative return can also be important for the Type-L 2HDM in the very large $\tanβ$ region, extending the kinematic reach of the heavy Higgs boson mass to the collider energy. Considering both the production and decay of non-SM Higgs bosons, signals can be identified over the Standard Model backgrounds. With the pair production channels via annihilation, 95\% C.L. exclusion reaches in the Higgs mass up to the production mass threshold of $\sqrt{s}/2$ are possible when channels with different final states are combined. Including single production modes can extended the reach further. Different types of 2HDMs can be distinguishable for moderate and large values of $\tanβ$.

hep-ph↗
arXiv2022

Quark and Gluon Contents of a Lepton at High Energies

In high-energy leptonic collisions, such as at a multi-TeV muon collider, the collinear splittings of the electroweak (EW) gauge bosons and leptons are the dominant phenomena, and the scattering processes should thus be formulated in terms of the EW parton distribution functions (EW PDFs). We complete this formalism in the Standard Model to include the QCD sector and evaluate the quark and gluon PDFs inside a lepton at the double-log accuracy. The splittings of the photon and subsequently the quarks and gluons control the quark/gluon PDFs below the EW scale. The massive gauge bosons lead to substantial contributions at high scales. The jet production cross section can reach the order of a few nb (50 pb) in $e^+e^-$ ($μ^+μ^-$) collisions, at the TeV c.m. energies with a moderate acceptance cut, that governs the overall event shape up to about $p_T^j \sim 60$ GeV.

hep-ph↗
arXiv2022

DistrEdge: Speeding up Convolutional Neural Network Inference on Distributed Edge Devices

As the number of edge devices with computing resources (e.g., embedded GPUs, mobile phones, and laptops) increases, recent studies demonstrate that it can be beneficial to collaboratively run convolutional neural network (CNN) inference on more than one edge device. However, these studies make strong assumptions on the devices' conditions, and their application is far from practical. In this work, we propose a general method, called DistrEdge, to provide CNN inference distribution strategies in environments with multiple IoT edge devices. By addressing heterogeneity in devices, network conditions, and nonlinear characters of CNN computation, DistrEdge is adaptive to a wide range of cases (e.g., with different network conditions, various device types) using deep reinforcement learning technology. We utilize the latest embedded AI computing devices (e.g., NVIDIA Jetson products) to construct cases of heterogeneous devices' types in the experiment. Based on our evaluations, DistrEdge can properly adjust the distribution strategy according to the devices' computing characters and the network conditions. It achieves 1.1 to 3x speedup compared to state-of-the-art methods.

cs.DC↗
arXiv2022

Baryon number violation at the LHC: the top option

Subject to strong experimental constraints at low energies, baryon number violation is nonetheless well motivated from a theoretical point of view. We examine the possibility of observing baryon-number-violating top-quark production or decay at hadron colliders. We adopt a model independent effective approach and focus on operators with minimal mass-dimension. Corresponding effective coefficients could be directly probed at the Large Hadron Collider (LHC) already with an integrated luminosity of 1 inverse femtobarn at 7 TeV, and further constrained with 30 (100) inverse femtobarns at 7 (14) TeV.

hep-ph↗
arXiv2022

Directly Probing the Higgs-top Coupling at High Scales

We explore the sensitivity to new physics for the coupling of the Higgs boson ($h$) and top quark ($t$) at high energy scales with the process $pp\to t\bar{t}h$ at the high-luminosity LHC. This process probes the coupling in both the space-like and time-like domains at a high scale, complementary to the off-shell Higgs processes in the time-like domain. The effects from physics beyond the Standard Model are parametrized in terms of the effective field theory framework and a non-local Higgs-top form factor. Focusing on the boosted Higgs regime in association with jet substructure techniques, we show that the present search can directly probe the Higgs-top coupling to good precision, providing a strong sensitivity to the new physics scale.

hep-ph↗
arXiv2021

MutualNet: Adaptive ConvNet via Mutual Learning from Different Model Configurations

Most existing deep neural networks are static, which means they can only do inference at a fixed complexity. But the resource budget can vary substantially across different devices. Even on a single device, the affordable budget can change with different scenarios, and repeatedly training networks for each required budget would be incredibly expensive. Therefore, in this work, we propose a general method called MutualNet to train a single network that can run at a diverse set of resource constraints. Our method trains a cohort of model configurations with various network widths and input resolutions. This mutual learning scheme not only allows the model to run at different width-resolution configurations but also transfers the unique knowledge among these configurations, helping the model to learn stronger representations overall. MutualNet is a general training methodology that can be applied to various network structures (e.g., 2D networks: MobileNets, ResNet, 3D networks: SlowFast, X3D) and various tasks (e.g., image classification, object detection, segmentation, and action recognition), and is demonstrated to achieve consistent improvements on a variety of datasets. Since we only train the model once, it also greatly reduces the training cost compared to independently training several models. Surprisingly, MutualNet can also be used to significantly boost the performance of a single network, if dynamic resource constraint is not a concern. In summary, MutualNet is a unified method for both static and adaptive, 2D and 3D networks. Codes and pre-trained models are available at \url{https://github.com/taoyang1122/MutualNet}.

cs.CV↗
arXiv2021

An Approach for Combining Multimodal Fusion and Neural Architecture Search Applied to Knowledge Tracing

Knowledge Tracing is the process of tracking mastery level of different skills of students for a given learning domain. It is one of the key components for building adaptive learning systems and has been investigated for decades. In parallel with the success of deep neural networks in other fields, we have seen researchers take similar approaches in the learning science community. However, most existing deep learning based knowledge tracing models either: (1) only use the correct/incorrect response (ignoring useful information from other modalities) or (2) design their network architectures through domain expertise via trial and error. In this paper, we propose a sequential model based optimization approach that combines multimodal fusion and neural architecture search within one framework. The commonly used neural architecture search technique could be considered as a special case of our proposed approach when there is only one modality involved. We further propose to use a new metric called time-weighted Area Under the Curve (weighted AUC) to measure how a sequence model performs with time. We evaluate our methods on two public real datasets showing the discovered model is able to achieve superior performance. Unlike most existing works, we conduct McNemar's test on the model predictions and the results are statistically significant.

cs.LG↗
arXiv2021

OnSlicing: Online End-to-End Network Slicing with Reinforcement Learning

Network slicing allows mobile network operators to virtualize infrastructures and provide customized slices for supporting various use cases with heterogeneous requirements. Online deep reinforcement learning (DRL) has shown promising potential in solving network problems and eliminating the simulation-to-reality discrepancy. Optimizing cross-domain resources with online DRL is, however, challenging, as the random exploration of DRL violates the service level agreement (SLA) of slices and resource constraints of infrastructures. In this paper, we propose OnSlicing, an online end-to-end network slicing system, to achieve minimal resource usage while satisfying slices' SLA. OnSlicing allows individualized learning for each slice and maintains its SLA by using a novel constraint-aware policy update method and proactive baseline switching mechanism. OnSlicing complies with resource constraints of infrastructures by using a unique design of action modification in slices and parameter coordination in infrastructures. OnSlicing further mitigates the poor performance of online learning during the early learning stage by offline imitating a rule-based solution. Besides, we design four new domain managers to enable dynamic resource configuration in radio access, transport, core, and edge networks, respectively, at a timescale of subseconds. We implement OnSlicing on an end-to-end slicing testbed designed based on OpenAirInterface with both 4G LTE and 5G NR, OpenDayLight SDN platform, and OpenAir-CN core network. The experimental results show that OnSlicing achieves 61.3% usage reduction as compared to the rule-based solution and maintains nearly zero violation (0.06%) throughout the online learning phase. As online learning is converged, OnSlicing reduces 12.5% usage without any violations as compared to the state-of-the-art online DRL solution.

cs.NI↗