Determination of neutrino mass ordering from Supernova neutrinos with T2HK and DUNE

In this paper we study the possibility of determining the neutrino mass ordering from the future supernova neutrino events at the DUNE and T2HK detectors. We estimate the expected number of neutrino event rates from a future supernova explosion assuming GKVM flux model corresponding to different processes that are responsible for detecting the supernova neutrinos at these detectors. We present our results in the form of $\chi^2$, as a function of supernova distance. For a systematic uncertainty of 5\%, our results show that, the neutrino mass ordering can be determined at $5 ~\sigma$ C.L. if the supernova explosion occurs at a distance of 44 kpc for T2HK and at a distance of 6.5 kpc for DUNE. Our results also show that the sensitivity of T2HK gets affected by the systematic uncertainties for the smaller supernova distances. Further, we show that in both DUNE and T2HK, the sensitivity gets deteriorated to some extent due to presence of energy smearing of the neutrino events. This occurs because of the reconstruction of the neutrino energy from the energy-momentum measurement of the outgoing leptons at the detector.Comment: 19 pages, 7 figure

Effect of torsion in long-baseline neutrino oscillation experiments

In this work we investigate the effect of curved spacetime on neutrino oscillation. In a curved spacetime, the effect of curvature on fermionic fields is represented by spin connection. The spin connection consists of a non-universal ``contorsion" part which is expressed in terms of vector and axial current density of fermions. The contraction of contorsion part with the tetrad fields, which connects the internal flat space metric and the spacetime metric, is called torsion. In a scenario where neutrino travels through background of fermionic matter at ordinary densities in a curved spacetime, the Hamiltonian of neutrino oscillation gets modified by the torsional coupling constants $\lambda_{21}^{\prime}$ and $\lambda_{31}^{\prime}$. The aim of this work is to study the effect of $\lambda_{21}^{\prime}$ and $\lambda_{31}^{\prime}$ in DUNE and P2SO. In our study we, (i) discuss the effect of torsional coupling constants on the neutrino oscillation probabilities, (ii) estimate the capability of P2SO and DUNE to put bounds on these parameters and (iii) study how the physics sensitivities get modified in presence of torsion.Comment: 21 pages, 7 figures, 3 table

Type III seesaw under A4A_4 modular symmetry with leptogenesis and muon g−2g-2

We make an attempt to study neutrino phenomenology in the framework of type-III seesaw by considering $A_4$ modular symmetry in the super-symmetric context. In addition, we have included local $U(1)_{B-L}$ symmetry which eventually helps us to avoid certain unwanted terms in the superpotential. Hitherto, the seesaw being type-III, it involves the fermion triplet superfields $\Sigma$, along with which, we have included a singlet weighton field $(\rho)$. In here, modular symmetry plays a crucial role by avoiding the usage of excess flavon (weighton) fields. Also, the Yukawa couplings acquire modular forms which are expressed in terms of Dedekind eta function $\eta(\tau)$. However, for numerical analysis we use $q$ expansion expressions of these couplings. Therefore, the model discussed here is triumphant enough to accommodate the observed neutrino oscillation data. Additionally, it also successfully explains leptogenesis and sheds some light on the current results of muon ($g-2$).Comment: 23 pages, 15 figure

Hybrid Techniques for Short Term Load Forecasting

Short Term Load Forecasting (STLF) is the projection of system load demands for the next day or week. Because of its openness in modeling, simplicity of implementation, and improved performance, the ANN-based STLF model has gained traction. The neural model consists of weights whose optimal values are determined using various optimization approaches. This paper uses an Artificial Neural Network (ANN) trained using multiple hybrid techniques (HT) such as Back Propagation (BP), Cuckoo Search  (CS) model, and Bat algorithm (BA) for load forecasting. Here, a thorough examination of the various strategies is taken to determine their scope and ability to produce results using different models in different settings. The simulation results show that the BA-BP model has less predicting error than other techniques. However, the Back Propagation model based on the Cuckoo Search method produces less inaccuracy, which is acceptable

Analysis and evaluation of two short-term load forecasting techniques

Abstract Short-term load forecasting (STLF) is very important for an efficient operation of the power system because the exact and stable load forecasting brings good results to the power system. This manuscript presents the application of two new models in STLF i.e. Cross multi-models and second decision mechanism and Residential load forecasting in smart grid using deep neural network models. In the cross multi-model and second decision mechanism method, the horizontal and longitudinal load characteristics are useful for the construction of the model with the calculation of the total load. The dataset for this model is considered from Maine in New England, Singapore, and New South Wales of Australia. While, In the residential load forecasting method, the Spatio-temporal correlation technique is used for the construction of the iterative ResBlock and deep neural network which helps to give the characteristics of residential load with the use of a publicly available Redd dataset. The performances of the proposed models are calculated by the Root Mean Square Error, Mean Absolute Error, and Mean Absolute Percentage Error. From the simulation results, it is concluded that the performance of cross multi-model and second decision mechanism is good as compare to the residential load forecasting.</jats:p

Short Term Load Forecasting using Metaheuristic Techniques

Abstract The power systems are important by using short term load forecasting (STLF) because it predicts the load in 24 hours ahead or a week ahead. The artificial neural network (ANN) using short term load forecasting brings good result in the predicted load because of its accurateness, easiness in the processing of data, construction of the model as well as excellent performances. The optimization value of ANN is found by different methods which consist of some weights. This manuscript explains the work of ANN with back propagation (BP), genetic algorithm (GA) as well as particle swarm optimization (PSO) for the STLF. The detailed work of the GA and PSO based BP is presenting in this paper which helps for its utilization in the STLF and also able to find the good result in the predicted load. Finally, the result of GA and PSO are compared by simulation and after that, it concluded, the PSO-BP is a good method for STLF using ANN.</jats:p