arXiv subjects

Shishir Purohit

Publications and source records attributed to Shishir Purohit.

3 recordsLinked to original sources

arXiv2026

Development of a thin-target hard X-ray bremsstrahlung detection system to study confined runaway electrons in Aditya-U Tokamak

A specially shielded CdTe detector based hard X-ray (HXR) monitoring system equipped with a lead collimator has been developed and installed on the Aditya-U tokamak to investigate the dynamics of fast electrons (~20-200 keV) generated during sawtooth activity. The pre-existing HXR monitor in Aditya-U is exposed to the entire HXR bremsstrahlung emission from the plasma volume, peripheral limiters, and other structural components, which limits its ability to separately study the dynamics of lost and confined runaway electrons (REs). In contrast, the newly developed diagnostic has successfully measured the chord-averaged thin-target HXR bremsstrahlung emission encompassing the core plasma region, particularly within and around the sawtooth inversion radius. The measured HXR spectra are validated through forward modelling code that incorporates plasma parameters, confined RE characteristics, and the geometric configuration of the diagnostic system. The results confirm the capability of the developed HXR monitor to probe the fast-electron dynamics during internal plasma instabilities.

physics.plasm-ph↗

arXiv2024

Capability enhancement of the X-ray micro-tomography system via ML-assisted approaches

Ring artifacts in X-ray micro-CT images are one of the primary causes of concern in their accurate visual interpretation and quantitative analysis. The geometry of X-ray micro-CT scanners is similar to the medical CT machines, except the sample is rotated with a stationary source and detector. The ring artifacts are caused by a defect or non-linear responses in detector pixels during the MicroCT data acquisition. Artifacts in MicroCT images can often be so severe that the images are no longer useful for further analysis. Therefore, it is essential to comprehend the causes of artifacts and potential solutions to maximize image quality. This article presents a convolution neural network (CNN)-based Deep Learning (DL) model inspired by UNet with a series of encoder and decoder units with skip connections for removal of ring artifacts. The proposed architecture has been evaluated using the Structural Similarity Index Measure (SSIM) and Mean Squared Error (MSE). Additionally, the results are compared with conventional filter-based non-ML techniques and are found to be better than the latter.

eess.IV↗

arXiv2023

Deep Learning assisted microwave-plasma interaction based technique for plasma density estimation

The electron density is a key parameter to characterize any plasma. Most of the plasma applications and research in the area of low-temperature plasmas (LTPs) are based on the accurate estimations of plasma density and plasma temperature. The conventional methods for electron density measurements offer axial and radial profiles for any given linear LTP device. These methods have major disadvantages of operational range (not very wide), cumbersome instrumentation, and complicated data analysis procedures. The article proposes a Deep Learning (DL) assisted microwave-plasma interaction-based non-invasive strategy, which can be used as a new alternative approach to address some of the challenges associated with existing plasma density measurement techniques. The electric field pattern due to microwave scattering from plasma is utilized to estimate the density profile. The proof of concept is tested for a simulated training data set comprising a low-temperature, unmagnetized, collisional plasma. Different types of symmetric (Gaussian-shaped) and asymmetrical density profiles, in the range $10^{16}-10^{19}$ m$^{-3}$, addressing a range of experimental configurations have been considered in our study. Real-life experimental issues such as the presence of noise and the amount of measured data (dense vs sparse) have been taken into consideration while preparing the synthetic training data-sets. The DL-based technique has the capability to determine the electron density profile within the plasma. The performance of the proposed deep learning-based approach has been evaluated using three metrics- SSIM, RMSLE, and MAPE. The obtained results show promising performance in estimating the 2D radial profile of the density for the given linear plasma device and affirms the potential of the proposed ML-based approach in plasma diagnostics.

physics.plasm-ph↗