abstract

We consider the decay of the Higgs boson to \(W^{+}~W^{-}\) at a proposed Large Hadron Electron Collider and determine the likelihood of detecting a signal for the Higgs mass from its decay product \(W\)jets by imposing cuts to select candidate jet pairs and optimizing the value of the angular separation \(\Delta R\). It was found that at the LHeC experiment (CM energy \(\sqrt {s}=1.3\) TeV and luminosity of 100 fb\(^{-1}\) per year), the highest efficiency is obtained with \(\Delta R = 0.4\), along with a selection scheme of \(10\lt m\lt 85\) GeV, \(|\Delta \eta | \lt 1\), \(p_\mathrm {T}\) of jets 1 and 2 between 10–20 GeV and \(p_\mathrm {T}\) of jets 3 and \(4 \gt 10\) GeV: this led to an efficiency between 7.1–7.5% for finding the invariant 4-jet mass in a mass region \(\lt 140\) GeV. Under signal-to-background comparison, the signal showed a \(3.8 \sigma \) excess compared to the charged current \(W\)-jet background.

abstract

We examine the phenomenology of a simplified model of fermionic dark matter coupled to a light scalar mediator carrying lepton number 2. We find that the mediator can be very light and still consistent with laboratory and cosmological bounds. This model satisfies the thermal relic condition for natural values of dimensionless coupling constants and admits a mediator in the 10–100 MeV mass range favored by small-scale structure observations. As such, this model provides an excellent candidate for self-interacting dark matter.

abstract

In this article, we introduce higher derivative Lagrangians of this form \(\alpha _1 A_{\mu }(x)\dot {x}^\mu \), \(\alpha _2 G_{\mu }(x)\ddot {x}^\mu \), \(\alpha _3 B_{\mu }(x)\dddot {x}^\mu \), \(\alpha _4 K_{\mu }(x)\ddddot {x}^\mu \), \(\dots \), that generalize the electromagnetic interaction to higher order derivatives. We show that odd order Lagrangians describe interactions analog to electromagnetism, while even order Lagrangians are similar to gravitational interaction. From this analogy, we formulate the concept of the generalized induction principle assuming the coupling between the higher fields \(U_{(n),\mu }(x),\ n\geq 1\) and the higher currents \(j^{(n)\mu }=\rho (x){\mathrm {d}}^nx^{\mu }/{\mathrm {d}}s^n\), where \(\rho (x)\) is the spatial density of mass (\(n\) even) or of electric charge (\(n\) odd). In short, this article is an invitation to reflect on a generalization of the concept of force and of inertia. We discuss the implications of these paradigms more in depth in the last section of the paper.

abstract

In recent time, an infectious disease spreads by making the contact with the infected agent in a population. This contact may be affected by the movement of the infected agents in any geographical region. Most of the studies are done by considering underlying static network topology. The movement of agents is important to consider the underlying network topology, which in this work is assumed random. Therefore, a new model is desired to analyze the spreading behavior of infected disease due to the random movement of infected agents. In this paper, we propose a geometric network with mobile agents by considering the random movement of some fraction of the nodes, while remaining nodes are stationary. Dynamics of epidemic spreading is studied using the SIS and SIR models. A rest time \(t_{\mathrm {rest}}\) of an agent is introduced during its movement, and its effect on the epidemic is studied. We simulate the modified epidemic model on underlying network topologies of the geometric network with mobile agents. It represents the real-world scenario, where agents constantly create new connections with their movements in their regions. We also evaluate our model using real dataset from Brightkite. The simulation results are in accordance with our theoretical findings which show that the random geometry of the agents, as well as the \(t_{\mathrm {rest}}\), affect dynamics of epidemic spreading. The simulation on the dataset also shows the effectiveness of our proposed framework.

abstract

We investigated the effects of variation in the non-integer order of a fractional differential equation modeling activated enzyme molecules in brain wave. The dynamical changes in the system trajectories in both the chaotic and the periodic regimes of an existing second order differential equation model are numerically examined when the orders of the biological system are assigned non-integer values. The simulation showed that the dynamics of the system can be altered through the order of the derivatives. In particular, the integer-order system can be driven from chaotic oscillation into periodic state by adopting an appropriate non-integer orders when the system is associated with innate memory.