arXiv subjects

Tinghua Chen

Publications and source records attributed to Tinghua Chen.

7 recordsLinked to original sources

arXiv2026

Next-to-Leading-Order Multi-Jet Merging of Higgs Boson Production via Vector Boson Fusion in the Presence of Anomalous Couplings

We present the results of a detailed simulation study of Higgs boson production via vector boson fusion (VBF) at the Large Hadron Collider (LHC) in the presence of anomalous couplings. The analysis utilizes the Herwig event generator interfaced with VBFNLO, employing a multi-jet merging scheme that combines NLO accurate matrix elements for Higgs plus 2- and 3-jet multiplicities with LO accurate matrix elements for Higgs plus 4-jet multiplicities. We investigate both matching and merging setups to consistently combine these hard scattering processes with QCD parton showers. Finally, we demonstrate the robustness of azimuthal angle observables against QCD radiative corrections across these frameworks.

hep-ph↗

arXiv2025

Scalable Inference for Bayesian Multinomial Logistic-Normal Dynamic Linear Models

Many scientific fields collect longitudinal count compositional data. Each observation is a multivariate count vector, where the total counts are arbitrary, and the information lies in the relative frequency of the counts. Multiple authors have proposed Bayesian Multinomial Logistic-Normal Dynamic Linear Models (MLN-DLMs) as a flexible approach to modeling these data. However, adoption of these methods has been limited by computational challenges. This article develops an efficient and accurate approach to posterior state estimation, called $\textit{Fenrir}$. Our approach relies on a novel algorithm for MAP estimation and an accurate approximation to a key posterior marginal of the model. As there are no equivalent methods against which we can compare, we also develop an optimized Stan implementation of MLN-DLMs. Our experiments suggest that Fenrir can be three orders of magnitude more efficient than Stan and can even be incorporated into larger sampling schemes for joint inference of model hyperparameters. Our methods are made available to the community as a user-friendly software library written in C++ with an R interface.

stat.AP↗

arXiv2025

Scalable Bayesian Semiparametric Additive Regression Models For Microbiome Studies

Statistical analysis of microbiome data is challenging. Bayesian multinomial logistic-normal (MLN) models have gained popularity due to their ability to account for the count compositional nature of these data, but existing approaches are either computationally intractable or restricted to purely parametric or non-parametric methods, which limit their flexibility and scalability. In this work, we introduce \textit{MultiAddGPs}, a novel semi-parametric framework that integrates additive Gaussian Process (GP) regression within a Bayesian MLN model to disentangle linear and non-linear covariate effects, including non-stationary dynamics. Our approach builds on the computationally efficient Collapse-Uncollapse (CU) sampler and additive GP regression, introducing a novel back-sampling algorithm and marginal likelihood approximation for efficient inference and hyperparameter estimation. Our models are over 240,000 times faster than alternatives while simultaneously producing more accurate posterior estimates. Additionally, we incorporate non-stationary kernel functions designed to model treatment interventions and disease effects. We demonstrate our approach using simulated and real data studies and produce novel biological insights from a previously published human gut microbiome study. Our methods are publicly available as part of the \textit{fido} software package on CRAN \footnotemark.

stat.ME↗

arXiv2024

Watermarking Counterfactual Explanations

Counterfactual (CF) explanations for ML model predictions provide actionable recourse recommendations to individuals adversely impacted by predicted outcomes. However, despite being preferred by end-users, CF explanations have been shown to pose significant security risks in real-world applications; in particular, malicious adversaries can exploit CF explanations to perform query-efficient model extraction attacks on the underlying proprietary ML model. To address this security challenge, we propose CFMark, a novel model-agnostic watermarking framework for detecting unauthorized model extraction attacks relying on CF explanations. CFMark involves a novel bi-level optimization problem to embed an indistinguishable watermark into the generated CF explanation such that any future model extraction attacks using these watermarked CF explanations can be detected using a null hypothesis significance testing (NHST) scheme. At the same time, the embedded watermark does not compromise the quality of the CF explanations. We evaluate CFMark across diverse real-world datasets, CF explanation methods, and model extraction techniques. Our empirical results demonstrate CFMark's effectiveness, achieving an F-1 score of ~0.89 in identifying unauthorized model extraction attacks using watermarked CF explanations. Importantly, this watermarking incurs only a negligible degradation in the quality of generated CF explanations (i.e., ~1.3% degradation in validity and ~1.6% in proximity). Our work establishes a critical foundation for the secure deployment of CF explanations in real-world applications.

cs.LG↗

arXiv2024

Release Note -- VBFNLO 3.0

VBFNLO is a flexible parton level Monte Carlo program for the simulation of vector boson fusion (VBF), QCD-induced single and double vector boson production plus two jets, and double and triple vector boson production (plus jet) in hadronic collisions at next-to-leading order (NLO) in the strong coupling constant, as well as Higgs boson plus two and three jet production via gluon fusion at the one-loop level. For the new version -- Version 3.0 -- several major enhancements have been included. An interface according to the Binoth Les Houches Accord (BLHA) has been added for all VBF and di/tri-boson processes including fully leptonic decays. For all dimension-8 operators affecting vector boson scattering (VBS) processes, a modified T-matrix unitarization procedure has been implemented. Several new production processes have been added, namely the VBS $Zγjj$ and $γγjj$ processes at NLO, $γγjj $, $WWj$ and $ZZj$ production at NLO including the loop-induced gluon-fusion contributions and the gluon-fusion one-loop induced $Φjjj$ ($Φ$ is a CP-even or CP-odd scalar boson) process at LO, retaining the full top-mass dependence. Finally, the code has been parallelized using OpenMPI.

hep-ph↗

arXiv2022

NLO Multijet Merging for Higgs Production Beyond the VBF Approximation

We present results of the simulation of electroweak Higgs boson production at the Large Hadron Collider using both the NLO multijet merging and NLO matching frameworks provided by the general purpose event generator Herwig 7. For the simulation of the hard scattering processes, we use the HJets library for the full calculation and VBFNLO for the approximate calculation to compute the $2\to h+n$ amplitudes at tree-level with $n=2,3,4$ and at one-loop with $n=2,3$.

hep-ph↗

arXiv2019

A Projective Phase Space Generator for Hadronic Vector Boson Plus One Jet Production

In this paper we use our previously developed projective phase space generator for the calculation of the hadronic production of a vector boson with one additional jet at Next-to-Leading Order. The projective phase space generator allows us to make physical predictions in novel ways, speeding up both evaluation time and attainable accuracy. For the numerical evaluation we explore a computational model which combines the use of both multi-threading and distributed resources through the use of grid or cloud computing without depending on local institutional computer availability. The projective phase space method is well suited for this approach and gives through the use of cloud computing instant access to a large pool of resources.

hep-ph↗