相关论文: A multi-instance deep neural network classifier: a…

Statistical tools have been developed to quantify the agreement between a hypothesis or a model and the observed data for probabilistic phenomena. In this work, we define statistical methods used to measure the properties of the higgs boson…

Multiple instance data are sets or multi-sets of unordered elements. Using metrics or distances for sets, we propose an approach to several multiple instance learning tasks, such as clustering (unsupervised learning), classification…

We show that neural network classifiers can be helpful to discriminate Higgs production from background at LHC in the Higgs mass range M= 200 GeV. We employ a common feed-forward neural network trained by the backpropagation algorithm for…

The investigation of the properties of a Higgs boson, especially a test of the predicted linear dependence of the branching ratios on the mass of the final state, is currently one of the most compelling arguments for building a linear…

Multi-Higgs doublet models include more than one neutral Higgs bosons, some of which have different properties under a CP transformation. We examine their productions at $\gamma \gamma$ colliders. It is found that helicity observations of…

One of the most promising approaches for data analysis and exploration of large data sets is Machine Learning techniques that are inspired by brain models. Such methods use alternative learning rules potentially more efficiently than…

The prospects for a precise exploration of the properties of a single or many observed Higgs bosons at future accelerators are summarized, with particular emphasis on the abilities of a Linear Collider (LC). Some implications of these…

The multiple-biomarker classifier problem and its assessment are reviewed against the background of some fundamental principles from the field of statistical pattern recognition, machine learning, or the recently so-called "data science". A…

We introduce a class of asymmetries sensitive to the spin and CP properties of the new boson discovered by the ATLAS and CMS experiments. These asymmetries can be measured in the four-lepton final state, and are defined by integrating the…

Binary classification is a task that involves the classification of data into one of two distinct classes. It is widely utilized in various fields. However, conventional classifiers tend to make overconfident predictions for data that…

The CP nature of the Higgs coupling to top quarks is addressed in this paper, in single charged lepton final states of $t\bar{t}h$ events produced in proton-proton collisions at the LHC. Pure scalar ($h=H$) and pseudo-scalar ($h=A$) Higgs…

We explore the phenomenology of quantum entanglement at collider experiments by computing the polarization density matrix of processes yielding two massive gauge bosons. After reviewing the formalism, we detail observables suitable to test…

We analyse the prospects to detect at the LHC the neutral Higgs particles of the Minimal Supersymmetric Standard Model, when the masses of the two CP-even $h,H$ and of the CP-odd $A$ boson are close to one another, and the value of $\tb$ is…

We study the binary classification problem for Poisson point processes, which are allowed to take values in a general metric space. The problem is tackled in two different ways: estimating nonparametricaly the intensity functions of the…

We propose a new method to analyze the impact of errors in algorithms for multi-instance pose estimation and a principled benchmark that can be used to compare them. We define and characterize three classes of errors - localization,…

Multiple-instance learning is a subset of weakly supervised learning where labels are applied to sets of instances rather than the instances themselves. Under the standard assumption, a set is positive only there is if at least one instance…

Classifiers based on probabilistic graphical models are very effective. In continuous domains, maximum likelihood is usually used to assess the predictions of those classifiers. When data is scarce, this can easily lead to overfitting. In…

We propose a novel method to reconstruct event by event the full kinematics of the cascade decay process, $h \to \tau^+\tau^- \to (\pi^+ \bar{\nu}) (\pi^- \nu)$, which allows us to measure the $\tau^+\tau^-$ spin correlation, a measure of…

We interpret the new particle at the Large Hadron Collider as a CP-even scalar and investigate its electroweak quantum number. Assuming an unbroken custodial invariance as suggested by precision electroweak measurements, only four…

With the Large Hadron Collider's Run 3 in progress, the 125 GeV Higgs boson couplings are being examined in greater detail, while searching for additional scalars. Multi-Higgs frameworks allow Higgs couplings to significantly deviate from…