Stars, galaxies, and quasars are fascinating celestial objects that play pivotal roles in our understanding of the universe. Stars are luminous spheres of plasma, whereas galaxies are vast systems which contain stars, quasars, gas, dust, and dark matter held together by gravity and quasars, short for “quasi-stellar radio sources”, are extremely bright and distant celestial objects and can outshine entire galaxies. Large galaxy surveys, like Solan Digital Sky Survey(SDSS), aim to observe and catalogue a vast number of celestial objects within a given region of the sky. Thus, the first step in the study of these objects is to classify them. This classification provides a systematic framework for organizing and studying these objects. It allows astronomers to make meaningful comparisons, identify relationships and connections, and ultimately gain insights into the fundamental processes that shape our universe. The conventional classification process for these objects is known to be laborious and time-consuming. However, by leveraging AI and machine learning techniques, the task becomes much faster. The objective of this paper is to classify these objects based on data from SDSS by employing supervised machine learning algorithms and developing neural networks to compare their performance in terms of accuracy and time required for classification. The dense neural network, we built, showed the best results in terms of both accuracy and time. We saw that decision-tree-based ensemble methods performed much better than the other algorithms, the details of which are discussed in the manuscript.

Keywords: Astronomical data survey, Classification, Artificial Neural Network, Supervised machine learning algorithms, Confusion matrix.

This paper presents a case study of designing a 4000 MT Hybrid solar powered cold storage system for storing potatoes. Cold storage can restrict the wastage of perishable foods produced in the country . For this design, the building plan calculation , cooling load calculation, design and selection of refrigeration system elements, and hybrid solar plant design have been done. For this project, a hybrid solar plant is chosen, where the plant is powered both by the grid and a battery backup system in case of an emergency grid outage. As the cold storage units are commonly situated in remote areas, electric cut-off is a regular issue, which is why we’re using hybrid solar plant construction. Different designs of the same capacity cold storage plant are also compared to get the best result.

Keywords: cold storage, cooling load, refrigeration, hybrid solar plant.

Quantum computing is coming up as futuristic technology in engineering and science. The famous SturmLiouville (SL) differential equation along with some specific boundary conditions appear in many branches of physics as well as in other fundamental fields. The numerical solution of such boundary value problems (like heat transfer, potential problem etc.) is frequently computationally expensive, and traditional computers may not be able to deliver accurate solutions in an acceptable length of time. We propose quantum computing approach to solve such boundary value problems that will be more efficient and time solvent. The present research demands that the Harrow-Hassidim-Lloyd (HHL) quantum algorithm provides an efficient technique for solving such boundary value problems that arise in many branches of physical science and engineering. The prime focus of this research is to explore HHL quantum algorithm in detail. The quantum phase estimation algorithm is applied for solving eigen values and eigenvectors associated with the SL type of boundary value problem. As a case study, in this research, we have solved the steady state heat transfer problem with boundary conditions. Our computation is based on Qiskit module made by IBM and available as a library in Python. A detailed analysis of the computational complexity of our algorithm that compares its performance with classical methods on a range of boundary value problems has been discussed. Role of Tridiagonal Toeplitz matrix is revealed. Results demonstrate that the method of quantum algorithm for solving boundary value problems can significantly outperform classical methods, especially for larger problem sizes. This work represents a significant methodology in solving important equations of physics and mathematics in future quantum computers.

Keywords: Boundary value problems, Quantum computing, HHL algorithm, Quantum phase estimation algorithm

The Hubble Space Telescope (HST) is an extraordinary instrument that has made a revolution in our understanding of the universe since its launch in 1990. Since then, it has provided unprecedented optimism and hope to reach bigger things. It has been a game-changing invention for astronomers, physicists, and science enthusiasts around the world enabling them to discover miracles that were formerly considered impossible to achieve. The HST's legacy extends beyond its scientific contributions. Its captivating images and public accessibility have inspired millions worldwide, fuelling public interest in astronomy and space exploration. This research paper aims to examine the scientific impact of the Hubble Telescope across various fields of astrophysics and cosmology. Through our research, we aim to highlight some of the critical scientific breakthroughs facilitated by the Hubble Telescope, including the measurement of the Hubble constant, the study of galaxies and dark matter, the investigation of exoplanets, and the exploration of the early universe.

Keywords: Astrophysics, Hubble Space Telescope (HST), Cosmology, Galactic studies, Exoplanets, Spectroscopy, James Webb Space Telescope (JWST), Nancy Grace Roman Space Telescope (RST), WideField Infrared Survey Telescope (WFIRST), Corrective optics, Spherical aberration, Stellar astronomy, Transit method, Dark matter, Gravitational lensing, Ultraviolet and infrared observations.

We study the anomalous Nernst coefficient (ANC) in a biased dice lattice under the application of a circularly polarized off-resonant light within the Berry phase formalism. We employ the Floquet theory to study the effect of off-resonant light. The off-resonant light provides a Haldane-type mass term, which breaks the time reversal (TR) symmetry. The photoinduced mass term is valley dependent and can be tuned by changing the frequency of the off-resonant light. We find that the interplay between the bias term and the off-resonant light-induced mass term results in a tunable valley-dependent Berry curvature. The valley-controlled ANC is calculated as a function of the chemical potential for different values of the mass terms. We find that the total ANC, the sum of the contributions from the Dirac points 𝐾 and 𝐾 ′ , no longer vanishes due to the breaking of TR symmetry. Our results demonstrate the control of the valley degree of freedom using the off-resonant light in a biased dice lattice, which can have a potential application in valley caloritronics.

Keywords: Anomalous Nernst Effect, Berry Phase, Floquet Theory, Dirac Points.