Publications (19)
Probing the electroweak final state in type I 2HDM at the LHC
Prasenjit Sanyal, Daohan Wang
Most of the experimental searches of the non-Standard Model Higgs boson(s) at the LHC rely on the QCD induced production modes. However, in some beyond Standard Model frameworks, t…
Deep Learning Jet Image as a Probe of Light Higgsino Dark Matter at the LHC
Huifang Lv, Daohan Wang, Lei Wu
Higgsino in supersymmetric standard models can play the role of dark matter particle. In conjunction with the naturalness criterion, the higgsino mass parameter is expected to be a…
Higgs Signal Strength Estimation with Machine Learning under Systematic Uncertainties
Minxuan He, Claudius Krause, Daohan Wang
We present a dedicated graph neural network (GNN)-based methodology for the extraction of the Higgs boson signal strength , incorporating systematic uncertainties. The architec…
and to probe the fermiophobic Higgs boson with high cutoff scales
Jinheung Kim, Soojin Lee, Prasenjit Sanyal +2
The light fermiophobic Higgs boson in the type-I two-Higgs-doublet model can evade the current search programs at the LHC since its production through the quark-antiqua…
Exploring lepton flavor violation phenomena of the and Higgs bosons at electron-proton colliders
Adil Jueid, Jinheung Kim, Soojin Lee +2
We comprehensively study the potential for discovering lepton flavor violation (LFV) phenomena associated with the and Higgs bosons at the LHeC and FCC-he. Our meticulous inves…
BitHEP -- The Limits of Low-Precision ML in HEP
Claudius Krause, Daohan Wang, Ramon Winterhalder
The increasing complexity of modern neural network architectures demands fast and memory-efficient implementations to mitigate computational bottlenecks. In this work, we evaluate…
Hierarchical High-Point Energy Flow Network for Jet Tagging
Wei Shen, Daohan Wang, Jin Min Yang
Jet substructure observable basis is a systematic and powerful tool for analyzing the internal energy distribution of constituent particles within a jet. In this work, we propose a…
Photon-jet events as a probe of axion-like particles at the LHC
Daohan Wang, Lei Wu, Jin Min Yang +1
Axion-like particles (ALPs) are predicted by many extensions of the Standard Model (SM). When ALP mass lies in the range of MeV to GeV, the cosmology and astrophysics will be large…
Probing Light Fermiophobic Higgs Boson via diphoton jets at the HL-LHC
Daohan Wang, Jin-Hwan Cho, Jinheung Kim +3
In this study, we explore the phenomenological signatures associated with a light fermiophobic Higgs boson, , within the type-I two-Higgs-doublet model at the HL-LHC. Ou…
Enhanced Higgs pair production from higgsino decay at the HL-LHC
Jianpeng Dai, Tao Liu, Daohan Wang +1
The scenario of multi-sector SUSY breaking predicts pseudo-goldstinos which are not absorbed by the gravitino and their mass can be as low as GeV. Since the inte…
Hunting for top partner with a new signature at the LHC
Daohan Wang, Lei Wu, Mengchao Zhang
Vector-like top partner plays a central role in many new physics models which attempt to address the hierarchy problem. The top partner is conventionally assumed to decay to a quar…
Proton Structure from Neural Simulation-Based Inference at the LHC
Ricardo Barrué, Lisa Benato, Ali Kaan Güven +10
The precise determination of the parton distribution functions (PDFs) of the proton is an essential ingredient for LHC analyses, including for those at the upcoming High-Luminosity…
Quark/Gluon Discrimination and Top Tagging with Dual Attention Transformer
Minxuan He, Daohan Wang
Jet tagging is a crucial classification task in high energy physics. Recently the performance of jet tagging has been significantly improved by the application of deep learning tec…
Detecting an axion-like particle with machine learning at the LHC
Jie Ren, Daohan Wang, Lei Wu +2
Axion-like particles (ALPs) appear in various new physics models with spontaneous global symmetry breaking. When the ALP mass is in the range of MeV to GeV, the cosmology and astro…
Probing triple Higgs coupling with machine learning at the LHC
Murat Abdughani, Daohan Wang, Lei Wu +2
Measuring the triple Higgs coupling is a crucial task in the LHC and future collider experiments. We apply the Message Passing Neural Network (MPNN) to the study of the non-resonan…
FAIR Universe HiggsML Uncertainty Dataset and Competition
Lisa Benato, Wahid Bhimji, Paolo Calafiura +26
The FAIR Universe HiggsML Uncertainty Challenge focused on measuring the physical properties of elementary particles with imperfect simulators. Participants were required to comput…
Heavy Bino and Slepton for Muon g-2 Anomaly
Yuchao Gu, Ning Liu, Liangliang Su +1
In light of very recent E989 experimental result, we investigate the possibility that heavy sparticles explain the muon g-2 anomaly. We focus on the bino-smuon loop in an effective…
Unbinned inclusive cross-section measurements with machine-learned systematic uncertainties
Lisa Benato, Cristina Giordano, Claudius Krause +5
We introduce a novel methodology for addressing systematic uncertainties in unbinned inclusive cross-section measurements and related collider-based inference problems. Our approac…
Discovery Prospects for the Light Charged Higgs Boson Decay to an Off-Shell Top Quark and a Bottom Quark at Future High-Energy Colliders
Jinheung Kim, Soojin Lee, Prasenjit Sanyal +2
The charged Higgs boson () with a mass below the top quark mass remains a viable possibility within the Type-I two-Higgs-doublet model under current constraints. While previ…