activity
20202026
most citedStudy of CKM angle sensitivity using flavor untagged decays

8 citations · 15 across the 6 of their papers we have counts for

collaborators

6 papers

physics.ins-det2026

DarkFlow: Hierarchical Digital SiPM Architecture with Low-Loss Dataflow Readout for Dark Matter Detection

Zirui Wang, Aras Repond, Shawn Westerdale +1

Direct dark matter detection experiments require large-scale photon sensing arrays with hundreds of thousands of synchronized readout channels. Silicon photomultipliers (SiPMs) hav…

hep-ph2025★ 6 cited

t-channel dark matter at the LHC -- a whitepaper

Chiara Arina, Benjamin Fuks, Luca Panizzi +67

This report, summarising work achieved in the context of the LHC Dark Matter Working Group, investigates the phenomenology of -channel dark matter models, spanning minimal setup…

physics.app-ph2023

The interface states in gate-all-around transistors (GAAFETs)

Yue-Yang Liu, Haoran Lu, Zirui Wang +5

The atomic-level structural detail and the quantum effects are becoming crucial to device performance as the emerging advanced transistors, representatively GAAFETs, are scaling do…

cond-mat.mes-hall2021

Understanding Hot Carrier Reliability in FinFET Technology from Trap-based Approach

Runsheng Wang, Zixuan Sun, Yue-Yang Liu +4

In this paper, the recent advances of our studies on hot carrier degradation (HCD) are presented from trap-based approach. The microscopic speculation of interface trap generation…

hep-ex2020★ 8 cited

Study of CKM angle sensitivity using flavor untagged decays

D. Ao, D. Decamp, W. B. Qian +8

A sensitivity study for the measurement of the CKM angle from decays is performed using meson reconstructed in the quasi flavour-specif…

cs.CV2020★ 1 cited

A Study on Evaluation Standard for Automatic Crack Detection Regard the Random Fractal

Hongyu Li, Jihe Wang, Yu Zhang +2

A reasonable evaluation standard underlies construction of effective deep learning models. However, we find in experiments that the automatic crack detectors based on deep learning…