SearcharxivSearch

arXiv subjects

Pushpender Singh

Publications and source records attributed to Pushpender Singh.

2 recordsLinked to original sources

Geometrical Tuning of Light-Matter Interaction in Atomic Trimer Antennas: A Symmetry-Resolved Modal Analysis

Atomic trimers constitute the smallest geometry in which collective electric and magnetic responses emerge from coupled electric dipoles. We present a theoretical study of collective mode excitation in atomic trimers as the geometry is continuously tuned from linear to equilateral, using the coupled-dipole method with a multipole expansion formulated about the optimal scattering center. By combining eigenmode analysis and symmetry classification, we provide a complete symmetry-resolved map of the six in-plane and three out-of-plane modes, revealing how symmetry reduction across the $D_{\infty h}$, $C_{2v}$, and $D_{3h}$ configurations governs the evolution of eigenmodes and their spectral features, lifting degeneracies, activating dark modes, and enabling full access to the modal spectrum. Based on this modal understanding, we demonstrate that forward-backward scattering can be switched solely by frequency detuning in a nearly linear trimer, without geometric reconfiguration. Furthermore, a linear trimer under s-polarized excitation supports a magnetic mode with a strongly enhanced magnetic field and a large Purcell factor, making it a promising platform for probing magnetic dipole transitions in atoms, with emission preferentially directed into the transverse plane. These results establish atomic trimers as a minimal platform where symmetry-controlled electric-magnetic mode engineering can be fully resolved and exploited for tailoring light-matter interaction at the atomic level.

physics.optics

GenNet : Reading Comprehension with Multiple Choice Questions using Generation and Selection model

Multiple-choice machine reading comprehension is difficult task as its required machines to select the correct option from a set of candidate or possible options using the given passage and question.Reading Comprehension with Multiple Choice Questions task,required a human (or machine) to read a given passage, question pair and select the best one option from n given options. There are two different ways to select the correct answer from the given passage. Either by selecting the best match answer to by eliminating the worst match answer. Here we proposed GenNet model, a neural network-based model. In this model first we will generate the answer of the question from the passage and then will matched the generated answer with given answer, the best matched option will be our answer. For answer generation we used S-net (Tan et al., 2017) model trained on SQuAD and to evaluate our model we used Large-scale RACE (ReAding Comprehension Dataset From Examinations) (Lai et al.,2017).

cs.CL