相关论文: A multi-instance deep neural network classifier: a…
We investigate how precisely the CP nature of the 125 GeV Higgs boson, parametrized by a scalar-pseudoscalar Higgs mixing angle, can be determined in Higgs-to-tau-pair decay with subsequent tau-lepton decays to charged prongs at the Large…
Multilabel classification is a relatively recent subfield of machine learning. Unlike to the classical approach, where instances are labeled with only one category, in multilabel classification, an arbitrary number of categories is chosen…
A possibility to prove spin and CP-eigenvalue of a Standard Model (SM) Higgs boson is presented. We exploit angular correlations in the subsequent decay H -> ZZ -> 4l (muons or electrons) for Higgs masses above 200 GeV. We compare the…
We investigate the prospects of determining the CP nature of the 126 GeV neutral spin-0 (Higgs) boson h, discovered at the LHC, at a future linear collider. We consider the production of h by the Higgsstrahlung process and its subsequent…
In this paper, we focus on improving binary 2D instance segmentation to assist humans in labeling ground truth datasets with polygons. Humans labeler just have to draw boxes around objects, and polygons are generated automatically. To be…
The search for additional Higgs particles and the exact measurements of Higgs (self-) couplings is a major goal of future collider experiments. In this paper we investigate the possible sizes of new physics signals in these searches in the…
The status of Higgs boson measurements in the $WW^{\ast}$, $\tau\tau$, and $\mu\mu$ decay modes with the ATLAS experiment is presented. These measurements are done using $pp$ collision data from the Large Hadron Collider with either the…
The Higgs Triplet Model (HTM) is one of important examples for extended Higgs sectors, because tiny neutrino masses can be simply explained. Unlike the canonical type-I seesaw model, a scale of new particles can be taken as…
We study the LHC searches for the heavy $CP$-odd Higgs boson $A$ and $CP$-even Higgs boson $H$ in the context of general two-Higgs-doublet model. Specifically, we consider the decay mode of $A/H\to t \bar t $ through the $b \bar b$ or $t…
Multiclass neural networks are a common tool in modern unsupervised domain adaptation, yet an appropriate theoretical description for their non-uniform sample complexity is lacking in the adaptation literature. To fill this gap, we propose…
We study measurement of the CP property of the Higgs boson at a photon linear collider. One method where we take advantage of interference between Higgs-production and background amplitudes is proposed. A broad peak of the photon energy…
Particle identification in large high-energy physics experiments typically relies on classifiers obtained by combining many experimental observables. Predicting the probability density function (pdf) of such classifiers in the multivariate…
Decays of Higgs boson-like particles into multileptons is a well-motivated process for investigating physics beyond the Standard Model (SM). A unique feature of this final state is the precision with which the SM is known. As a result,…
The Higgs boson, discovered back in 2012 through collision data at the Large Hadron Collider (LHC) by ATLAS and CMS experiments, marked a significant inflection point in High Energy Physics (HEP). Today, it's crucial to precisely measure…
Rule based classifiers that use the presence and absence of key sub-strings to make classification decisions have a natural mechanism for quantifying the uncertainty of their precision. For a binary classifier, the key insight is to treat…
Once a Higgs boson has been discovered, it is also important to know what are its properties, in particular its couplings to the other particles. At the LHC, there will be many observable channels that can be used to measure the relevant…
In multi-instance (MI) learning, each object (bag) consists of multiple feature vectors (instances), and is most commonly regarded as a set of points in a multidimensional space. A different viewpoint is that the instances are realisations…
In many pattern recognition problems, a single feature vector is not sufficient to describe an object. In multiple instance learning (MIL), objects are represented by sets (\emph{bags}) of feature vectors (\emph{instances}). This requires…
Multi-class classification problems are fundamental in many varied domains in research and industry. To solve multi-class classification problems, heuristic strategies such as One-vs-One or One-vs-All can be employed. However, these…
The Pearson-Matthews correlation coefficient (usually abbreviated MCC) is considered to be one of the most useful metrics for the performance of a binary classification or hypothesis testing method (for the sake of conciseness we will use…