arXiv subjects

Ankul Prajapati

Publications and source records attributed to Ankul Prajapati.

3 recordsLinked to original sources

arXiv2021

Deep Learning for estimating parameters of Gravitational Waves

In recent years, improvements in Deep Learning (DL) techniques towards Gravitational Wave (GW) astronomy have led to a significant rise in the development of various classification algorithms that have been successfully employed to extract GWs of binary blackhole merger events from noisy time-series data. However, the success of these models is constrained by the length of time-sample and the class of GW source: binary blackhole and neutron star binaries to some extent. In this work, we intended to advance the boundaries of DL techniques using Convolutional Neural Networks, to go beyond binary classification and predict the physical parameters of the events. We aim to propose an alternative method that can be employed for realtime detection and parameter prediction. The DL model we present has been trained on 12s of data to predict the GW source parameters if detected. During training, the maximum accuracy attained was 90.93%, with a validation accuracy of 89.97%.

astro-ph.HE↗

arXiv2020

Predicting future astronomical events using deep learning

In a quest towards an intelligent decision-making machine, the ability to make plausible predictions is the central pillar of its intelligence. A predicting algorithm's central idea is to understand the governing physical rules and make plausible and apt predictions based on the same governing laws. Extending the study towards the astrophysical phenomenon puts the model's ability to test since the model has to understand various parameters that govern the dynamics of the event and understand the spatial and temporal evolution by applying the plausible laws. This work presents a deep learning model to predict plausible future events that maintain spatial and temporal coherence. We have trained over two broad classes, the evolution of Sa, Sb, S0, and Sd galaxy mergers and evolution of gravitational lenses with a higher redshift of the foreground galaxy having $15M_{\odot}$. We extended our work towards developing a direct measure of the performance metric for any prediction algorithm. We thereby introduce a novel metric, Correctness Factor (CF), which directly outputs how accurate a prediction is.

astro-ph.IM↗

arXiv2020

Next Generation Quantum Dots Based Multijunction Photovoltaics

Photovoltaic cells (PVc), as an energy provider to the next generation and the biggest source of renewable energy. Since the last decade improving efficiency and reducing the cost of PVc has been a subject of active research among scientists. Promising progress in the field of material science and manufacturing process at Nano-level played a big role. Still, at present there are many challenges before photovoltaics for efficient and economic energy. However, Photovoltaics cell based on p-n type homojunction semiconductors with different organic and inorganic materials reported thus for generally suffer from poor performance. According to the available literature, colloidal quantum dots having immense properties like a wide range of light absorption, easily charge separation and transport. To utilize the maximum part of the spectrum of solar energy reaching to the earth and making effective energy production, here we introduce the complete cell architecture and numerical investigation on quantum dot based solar cells (QDSCs) with a heterostructure multijunction approach. Successive ionic layer adsorption at different heterogeneous interfaces were analyzed. We majorly focused on improving the electrical and optical properties of the QDSCs achieved by different materials and structural approaches. Here, we report a heterostructure II-Type of band alignment engineering strategy for QDSCs interfaces that significantly enhances the efficiency descriptors. In the context of intermediate band solar cell (IBSC), we investigated optical properties of QDs and strain effects on multilayer PVc and we summarize the strain effect in QDs growth and local energy band bending of conduction band (CB) and valence band (VB).

physics.app-ph↗