Related papers: Signal mixture estimation for degenerate heavy Hig…

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…

In this paper, we constrain CP violation in the Higgs sector using the measured signal strengths in the various Higgs search channels. To this end, we introduce a general parameterization for a resonance which is an admixture of a CP-even…

In many signal processing applications, including communications, sonar, radar, and localization, a fundamental problem is the detection of a signal of interest in background noise, known as signal detection [1] [2]. A simple version of…

We study the dominant signatures that arise in Higgs physics at colliders when extending the Standard Model (SM) with a Yukawa interaction to heavy neutral leptons (HNL), while suppressing their mixing to active neutrinos. We focus on the…

In the quasi-inert Higgs doublet model, we study the LHC diphoton rate for a standard model-like Higgs boson and the top quark forward-backward asymmetry at Tevatron. Taking into account the constraints from the vacuum stability, unitarity,…

Signal analysis and classification is fraught with high levels of noise and perturbation. Computer-vision-based deep learning models applied to spectrograms have proven useful in the field of signal classification and detection; however,…

We study the production of top-antitop pairs at the Large Hadron Collider as a testbed for discovering heavy Z' bosons belonging to a composite Higgs model, as, in this scenario, such new gauge interaction states are sizeably coupled to the…

Recently, ATLAS and CMS experiments at the LHC put on light the relevant results in the measurement precision of the Higgs and BSM. In such a report, where the resonance direct search was made in the $\gamma Z$ channel, a mass adjustment…

The ATLAS and CMS collaborations have recently shown data suggesting the presence of a Higgs boson in the vicinity of 125 GeV. We show that a two-Higgs doublet model spectrum, with the pseudo-scalar state being the lightest, could be…

This paper investigates an issue of distributed fusion estimation under network-induced complexity and stochastic parameter uncertainties. First, a novel signal selection method based on event-trigger is developed to handle network-induced…

Beginning from a basic neural-network architecture, we test the potential benefits offered by a range of advanced techniques for machine learning, in particular deep learning, in the context of a typical classification problem encountered…

Blindly decoding a signal requires estimating its unknown transmit parameters, compensating for the wireless channel impairments, and identifying the modulation type. While deep learning can solve complex problems, digital signal processing…

Hyperspectral imaging can help better understand the characteristics of different materials, compared with traditional image systems. However, only high-resolution multispectral (HrMS) and low-resolution hyperspectral (LrHS) images can…

Analyses of collider data, often assisted by modern Machine Learning methods, condense a number of observables into a few powerful discriminants for the separation of the targeted signal process from the contributing backgrounds. These…

The explicit regularization and optimality of deep neural networks estimators from independent data have made considerable progress recently. The study of such properties on dependent data is still a challenge. In this paper, we carry out…

We discuss the possible signals of the degenerate BESS model at the LHC. This model describes a strongly interacting scenario responsible of the spontaneous breaking of the electroweak symmetry. It predicts two triplets of extra gauge…

Motivated by the need to identify erroneous disparity assignments, various approaches for uncertainty and confidence estimation of dense stereo matching have been presented in recent years. As in many other fields, especially deep learning…

In this paper we propose to use convolutional neural networks (CNNs) to improve the precision measurement of the Higgs boson-gluon effective coupling at lepton colliders. The CNN is employed to recognize the Higgs boson and a $Z$ boson…

Deep learning has been recently applied to many problems in wireless communications including modulation classification and symbol decoding. Many of the existing end-to-end learning approaches demonstrated robustness to signal distortions…

We present the efficient technique to extract the signal of the intermediate mass Higgs boson from the backgrounds at future $\gamma \gamma$ colliders. For a clear Higgs detection, it is important to fit the original electron accelerator…