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7,883 results on '"KUMAR, Naveen"'

28. Very High-energy Gamma-Ray Episodic Activity of Radio Galaxy NGC 1275 in 2022-2023 Measured with MACE

Author

Godambe, S., Mankuzhiyil, N., Borwankar, C., Ghosal, B., Tolamatti, A., Pal, M., Chandra, P., Khurana, M., Pandey, P., Dar, Z. A., Godiyal, S., Hariharan, J., Anand, Keshav, Norlha, S., Sarkar, D., Thubstan, R., Venugopal, K., Pathania, A., Kotwal, S., Kumar, Raj, Bhatt, N., Chanchalani, K., Das, M., Singh, K. K., Gour, K. K., Kothari, M., Kumar, Nandan, Kumar, Naveen, Marandi, P., Kushwaha, C. P., Koul, M. K., Dorjey, P., Dorji, N., Chitnis, V. R., Rannot, R. C., Bhattacharyya, S., Chouhan, N., Dhar, V. K., Sharma, M., and Yadav, K. K.

Subjects
Astrophysics - High Energy Astrophysical Phenomena
Abstract

The radio galaxy NGC 1275, located at the central region of Perseus cluster, is a well-known very high-energy (VHE) gamma-ray emitter. The Major Atmospheric Cherenkov Experiment Telescope has detected two distinct episodes of VHE (E > 80 GeV) gamma-ray emission from NGC 1275 during 2022 December and 2023 January. The second outburst, observed on 2023 January 10, was the more intense of the two, with flux reaching 58$\%$ of the Crab Nebula flux above 80 GeV. The differential energy spectrum measured between 80 GeV and 1.5 TeV can be described by a power law with a spectral index of $\Gamma = - 2.90 \pm 0.16_{stat}$ for both flaring events. The broadband spectral energy distribution derived from these flares, along with quasisimultaneous low-energy counterparts, suggests that the observed gamma-ray emission can be explained using a homogeneous single-zone synchrotron self-Compton model. The physical parameters derived from this model for both flaring states are similar. The intermediate state observed between two flaring episodes is explained by a lower Doppler factor or magnetic field, which subsequently returned to its previous value during the high-activity state observed on 2023 January 10., Comment: 7 Pages, 5 Figures, and 1 Table

Published
2024
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30. Variety HFO 707

Author

Phogat, D. S., Jattan, Minakshi, Jindal, Yogesh, Kumar, Naveen, and Kharor, Neeraj

Published
2023

48. Quantum phase properties of a state driven by a classical field

Author

Kumar, Naveen and Chatterjee, Arpita

Subjects
Quantum Physics
Abstract

We consider a nonclassical state generated by an atom-cavity field interaction in presence of a driven field. In the scheme, the two-level atom is moved through the cavity and driven by a classical field. The atom interacts dispersively with the cavity field, which results in a photon-number-dependent Stark shift. Assuming that the atom enters the cavity in the excited state $|{a}\rangle$, the obtained output cavity field is taken into account. The state vector $|\psi(t)\rangle$ describes the entire atom-field system but in our work we deal with the statistical aspects of the cavity field only. The quantum state that corresponds to the output cavity field is obtained by tracing out the atom part from $|{\psi(t)}\rangle\langle{\psi(t)}|$. Different quantum phase properties such as quantum phase distribution, angular $Q$ phase function, phase dispersion are evaluated for the obtained radiation field. The second-order correlation function $g^2(0)$, an indirect phase characteristic is also considered., Comment: 9 pages, 6 figures

Published
2024
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49. Multi-objective Binary Differential Approach with Parameter Tuning for Discovering Business Process Models: MoD-ProM

Author

Deshmukh, Sonia, Gupta, Shikha, and Kumar, Naveen

Subjects
Computer Science - Neural and Evolutionary Computing
Abstract

Process discovery approaches analyze the business data to automatically uncover structured information, known as a process model. The quality of a process model is measured using quality dimensions -- completeness (replay fitness), preciseness, simplicity, and generalization. Traditional process discovery algorithms usually output a single process model. A single model may not accurately capture the observed behavior and overfit the training data. We have formed the process discovery problem in a multi-objective framework that yields several candidate solutions for the end user who can pick a suitable model based on the local environmental constraints (possibly varying). We consider the Binary Differential Evolution approach in a multi-objective framework for the task of process discovery. The proposed method employs dichotomous crossover/mutation operators. The parameters are tuned using Grey relational analysis combined with the Taguchi approach. {We have compared the proposed approach with the well-known single-objective algorithms and state-of-the-art multi-objective evolutionary algorithm -- Non-dominated Sorting Genetic Algorithm (NSGA-II).} Additional comparison via computing a weighted average of the quality dimensions is also undertaken. Results show that the proposed algorithm is computationally efficient and produces diversified candidate solutions that score high on the fitness functions. It is shown that the process models generated by the proposed approach are superior to or at least as good as those generated by the state-of-the-art algorithms.

Published
2024

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