相关论文: Signal mixture estimation for degenerate heavy Hig…
We review some of the results of our recent work dealing with the novel type of Higgs signals that arise when one considers extensions of the standard model. We discuss first possible deviations on the Higgs couplings due to heavy…
In this article, deep learning is applied to estimate the uplink channels for mixed analog-to-digital converters (ADCs) massive multiple-input multiple-output (MIMO) systems, where a portion of antennas are equipped with high-resolution…
While deep learning has reduced the prevalence of manual feature extraction, transformation of data via feature engineering remains essential for improving model performance, particularly for underwater acoustic signals. The methods by…
We explore the direct Higgs-top CP measurement via the $pp\to t\bar{t}h$ channel at the high-luminosity LHC. We show that a combination of machine learning techniques and efficient kinematic reconstruction methods can boost new physics…
We study the implications of the LHC diphoton and Z+photon Higgs signals on the Higgs triplet model with Y=0. We discuss three different scenarios: (i) the observed boson is the light Higgs boson $h$; (ii) it is the heavy Higgs boson $H$;…
While the properties of the signal that was discovered in the Higgs searches at the LHC are consistent so far with the Higgs boson of the Standard Model (SM), it is crucial to investigate to what extent other interpretations that may…
We investigate diboson signals in the Standard Model (SM) with an extended Higgs sector, motivated by the excesses in the diboson channels at the LHC. We begin with the unitarity sum-rules of the weak gauge boson scattering assuming the…
Hierarchical mergers in a dense environment are one of the primary formation channels of intermediate-mass black hole (IMBH) binary system. We expect that the resulting massive binary system will exhibit mass asymmetry. The emitted…
We show how a statistically significant signal of heavy charged Higgs bosons of a general Two-Higgs Doublet Model produced in association with tau-neutrino pairs can be established at future Linear Colliders in the $H^+\to t\bar b\to 4$ jet…
Although considerable effort has been dedicated to improving the solution to the hyperspectral unmixing problem, non-idealities such as complex radiation scattering and endmember variability negatively impact the performance of most…
We present a novel deep convolutional neural network (DCNN) system for fine-grained image classification, called a mixture of DCNNs (MixDCNN). The fine-grained image classification problem is characterised by large intra-class variations…
Many analyses are performed by the LHC experiments to search for heavy gauge bosons, which appear in several new physics models. The invariant mass reconstruction of heavy gauge bosons is difficult when they decay to $\tau$ leptons due to…
Di-Higgs production at the LHC associated with missing transverse energy is explored in the context of simplified models that generically parameterize a large class of models with heavy scalars and dark matter candidates. Our aim is to…
This paper introduces the first deep neural network-based estimation metric for the jigsaw puzzle problem. Given two puzzle piece edges, the neural network predicts whether or not they should be adjacent in the correct assembly of the…
In recent years, artificial neural networks (ANNs) have won numerous contests in pattern recognition and machine learning. ANNS have been applied to problems ranging from speech recognition to prediction of protein secondary structure,…
The Higgs potential is vital to understand the electroweak symmetry breaking mechanism, and probing the Higgs self-interaction is arguably one of the most important physics targets at current and upcoming collider experiments. In…
Gravitational wave detection has opened up new avenues for exploring and understanding some of the fundamental principles of the universe. The optimal method for detecting modelled gravitational-wave events involves template-based matched…
In phenomenological preparation for new measurements one searches for the carriers of quality signatures. Often, the first approach quantities may be difficult to measure or to provide sufficiently precise predictions for comparisons.…
We develop a neural network model, based on the processes of high-energy heavy-ion collisions, to study and predict several experimental observables in Au+Au collisions. We present a data-driven deep learning framework for predicting…
Since the discovery in 2012 of the Higgs boson at the LHC, as the last missing piece of the Standard Model of particle physics, any hint of new physics has been intensively searched for, with no confirmation to date. There are however…