arXiv subjects

Soham Banerjee

Publications and source records attributed to Soham Banerjee.

14 recordsLinked to original sources

arXiv2026

Hyperon longitudinal polarization and vector meson spin alignment in a thermal model for heavy-ion collisions

The concept of a common local spin equilibrium for both spin-1/2 and spin-1 particles is incorporated into a thermal model of particle production in heavy-ion collisions at the top RHIC energies. We show that an effective spin polarization tensor leading to a correct description of the longitudinal spin polarization of $\Lambda$ hyperons simultaneously yields a positive alignment of vector mesons ($\phi$ and $K^{*0}$) that grows monotonically with transverse momentum and centrality. Similar trends can be seen in the data, suggesting a possible common mechanism for longitudinal spin polarization and alignment. However, model calculations are insufficient to explain the data in a fully quantitative way. The correlation found between the magnitude of the $\Lambda$ longitudinal polarization and vector meson alignment suggests further more elaborate investigations of this issue.

hep-ph↗

arXiv2026

diffpy.morph: Python tools for model independent comparisons between sets of 1D functions

diffpy$.$morph addresses a need to gain scientific insights from 1D scientific spectra in model independent ways. A powerful approach for this is to take differences between pairs of spectra and look for meaningful changes that might indicate underlying chemical, structural, or other modifications. The challenge is that the difference curve may contain uninteresting differences such as experimental inconsistencies and benign physical changes such as the effects of thermal expansion. diffpy$.$morph allows researchers to apply simple transformations, or "morphs", to one of the datasets to remove the unwanted differences revealing, when they are present, non-trivial differences. diffpy$.$morph is an open-source Python package available on the Python Package Index and conda-forge. Here, we describe its functionality and apply it to solve a range of experimental challenges on diffraction and PDF data from x-rays and neutrons, though we note that it may be applied to any 1D function in principle.

physics.comp-ph↗

arXiv2025

A Low-Cost UAV Deep Learning Pipeline for Integrated Apple Disease Diagnosis,Freshness Assessment, and Fruit Detection

Apple orchards require timely disease detection, fruit quality assessment, and yield estimation, yet existing UAV-based systems address such tasks in isolation and often rely on costly multispectral sensors. This paper presents a unified, low-cost RGB-only UAV-based orchard intelligent pipeline integrating ResNet50 for leaf disease detection, VGG 16 for apple freshness determination, and YOLOv8 for real-time apple detection and localization. The system runs on an ESP32-CAM and Raspberry Pi, providing fully offline on-site inference without cloud support. Experiments demonstrate 98.9% accuracy for leaf disease classification, 97.4% accuracy for freshness classification, and 0.857 F1 score for apple detection. The framework provides an accessible and scalable alternative to multispectral UAV solutions, supporting practical precision agriculture on affordable hardware.

cs.CV↗

arXiv2024

Dynamics of Hot QCD Matter 2024 -- Bulk Properties

The second Hot QCD Matter 2024 conference at IIT Mandi focused on various ongoing topics in high-energy heavy-ion collisions, encompassing theoretical and experimental perspectives. This proceedings volume includes 19 contributions that collectively explore diverse aspects of the bulk properties of hot QCD matter. The topics encompass the dynamics of electromagnetic fields, transport properties, hadronic matter, spin hydrodynamics, and the role of conserved charges in high-energy environments. These studies significantly enhance our understanding of the complex dynamics of hot QCD matter, the quark-gluon plasma (QGP) formed in high-energy nuclear collisions. Advances in theoretical frameworks, including hydrodynamics, spin dynamics, and fluctuation studies, aim to improve theoretical calculations and refine our knowledge of the thermodynamic properties of strongly interacting matter. Experimental efforts, such as those conducted by the ALICE and STAR collaborations, play a vital role in validating these theoretical predictions and deepening our insight into the QCD phase diagram, collectivity in small systems, and the early-stage behavior of strongly interacting matter. Combining theoretical models with experimental observations offers a comprehensive understanding of the extreme conditions encountered in relativistic heavy-ion and proton-proton collisions.

nucl-th↗

arXiv2024

Longitudinal spin polarization in a thermal model with dissipative corrections

In this work, we address the problem of longitudinal spin polarization of the $Λ$ hyperons produced in relativistic heavy-ion collisions. We combine a relativistic kinetic-theory framework that includes spin degrees of freedom treated in a classical way with the freeze-out parametrization used in previous investigations. The use of the kinetic theory allows us to incorporate dissipative corrections (due to the thermal shear and gradients of thermal vorticity) into the Pauli-Lubanski vector that determines spin polarization and can be directly compared with the experimental data. As in earlier similar studies, it turns out that a successful description of data can only be achieved with additional assumptions -- in our case, they involve the use of projected thermal vorticity and a suitably adjusted time for spin relaxation ($τ_s$). From our analysis, we find that $τ_s \sim 5$ fm/$c$, which is comparable with other estimates.

hep-ph↗

arXiv2023

Particle-In-Cell Code Comparison for Ion Acceleration: EPOCH and Smilei

Particle-in-Cell (PIC) codes are a popular tool to model laser-plasma interactions. Many different PIC codes already exist, and many new PIC codes are being developed constantly. It is therefore important to compare different PIC codes to ascertain which code is best suited for a particular kind of physical problem. In a paper by Smith et al. (2021) they compared three different codes on a problem relating to proton acceleration in the Target Normal Sheath Acceleration regime from a normal incidence ultra-intense laser pulse. Smith et al. (2021) included in their study the widely used EPOCH code. However, they did not include results from the Smilei code, which is another popular PIC code in the plasma community with a variety of features and physics packages. In the present work, we compare the Smilei code to the EPOCH code for the same test case as Smith et al. (2021). Broadly we find the two codes to be highly consistent with agreement in total ion, electron, and field energy at a percent level or better. The electron and ion energy distribution functions agree well at lower energies and the differences at higher energies (e.g. because of the finite number of macroparticles) are similar to what Smith et al. (2021) saw for other codes. We found that Smilei consumed 25% more RAM than EPOCH did but the execution time was 30% less for Smilei on one processor. We include the input files to encourage future comparisons.

physics.plasm-ph↗

arXiv2023

Direct Implicit and Explicit Energy-Conserving Particle-in-Cell Methods for Modeling of Capacitively-Coupled Plasma Devices

Achieving large-scale kinetic modelling is a crucial task for the development and optimization of modern plasma devices. With the trend of decreasing pressure in applications such as plasma etching, kinetic simulations are necessary to self-consistently capture the particle dynamics. The standard, explicit, electrostatic, momentum-conserving Particle-In-Cell method suffers from restrictive stability constraints on spatial cell size and temporal time step, requiring resolution of the electron Debye length and electron plasma period respectively. This results in a very high computational cost, making the technique prohibitive for large volume device modeling. We investigate the Direct Implicit algorithm and the explicit Energy Conserving algorithm as alternatives to the standard approach, both of which can reduce computational cost with a minimal (or controllable) impact on results. These algorithms are implemented into the well-tested EDIPIC-2D and LTP-PIC codes, and their performance is evaluated via 2D capacitively coupled plasma discharge simulations. The investigation revels that both approaches enable the utilization of cell sizes larger than the Debye length, resulting in reduced runtime, while incurring only minor inaccuracies in plasma parameters. The Direct Implicit method also allows for time steps larger than the electron plasma period, however care must be taken to avoid numerical heating or cooling. It is demonstrated that by appropriately adjusting the ratio of cell size to time step, it is possible to mitigate this effect to an acceptable level.

physics.plasm-ph↗

arXiv2022

An Improved Deep Learning Approach For Product Recognition on Racks in Retail Stores

Automated product recognition in retail stores is an important real-world application in the domain of Computer Vision and Pattern Recognition. In this paper, we consider the problem of automatically identifying the classes of the products placed on racks in retail stores from an image of the rack and information about the query/product images. We improve upon the existing approaches in terms of effectiveness and memory requirement by developing a two-stage object detection and recognition pipeline comprising of a Faster-RCNN-based object localizer that detects the object regions in the rack image and a ResNet-18-based image encoder that classifies the detected regions into the appropriate classes. Each of the models is fine-tuned using appropriate data sets for better prediction and data augmentation is performed on each query image to prepare an extensive gallery set for fine-tuning the ResNet-18-based product recognition model. This encoder is trained using a triplet loss function following the strategy of online-hard-negative-mining for improved prediction. The proposed models are lightweight and can be connected in an end-to-end manner during deployment for automatically identifying each product object placed in a rack image. Extensive experiments using Grozi-32k and GP-180 data sets verify the effectiveness of the proposed model.

cs.CV↗

arXiv2021

Twisted electron impact single ionization coincidence cross-sections for noble gas atoms

We present the angular profiles of the triple differential cross-section (TDCS) for the (e,2e) process on the noble gas atoms, namely He (1s), Ne (2s and 2p), and Ar (3p) for the plane wave and the twisted electron impact. We develop the theoretical formalism in the first-born approximation. The present study compares the TDCS for different values of orbital angular momentum number $m$ and opening angles $θ_p$ of the twisted electron beam with that of the plane wave beam. In addition, we also investigate the TDCS for macroscopic atomic targets to explore the influence of opening angle $θ_p$ of the twisted electron beam on the TDCS. Our results show that the peaks in binary and recoil region shift from the momentum transfer direction. The results also show that for larger opening angles the peaks for $p$-type orbitals split into double-peak structures which are not observed in the plane wave results for the given kinematics. The angular profiles for averaged cross-section show the dependence of TDCS on the opening angles which are significantly different from the plane wave TDCS.

physics.atom-ph↗

arXiv2021

Towards scanning nanostructure x-ray microscopy

We demonstrate spatial mapping of the local and nano-scale structure of thin film objects using spatially resolved PDF analysis of synchrotron x-ray diffraction data. This is demonstrated in a lab-on-chip combinatorial array of sample spots containing catalytically interesting nanoparticles deposited from liquid precursors using an ink-jet liquid handling system. We present a software implementation of the whole protocol including an approach for automated data acquisition and analyis using the atomic pair distribution function (PDF) method. The protocol software can handle semi-automated data reduction, normalization and modelling, with user-defined recipes generating a comprehensive collection of metadata and analysis results. By slicing the collection using included functions it was possible to build images of the 2D object containing using different quantities for contrast, allowing us to determine the spatial map relating to different aspects of the local structure on the array.

cond-mat.mtrl-sci↗

arXiv2020

Complete Strain Mapping of Nanosheets of Tantalum Disulfide

Quasi-two-dimensional (quasi-2D) materials hold promise for future electronics because of their unique band structures that result in electronic and mechanical properties sensitive to crystal strains in all three dimensions. Quantifying crystal strain is a prerequisite to correlating it with the performance of the device, and calls for high resolution but spatially resolved rapid characterization methods. Here we show that using fly-scan nano X-ray diffraction we can accomplish a tensile strain sensitivity below 0.001% with a spatial resolution of better than 80 nm over a spatial extent of 100 $μ$m on quasi 2D flakes of 1T-TaS2. Coherent diffraction patterns were collected from a $\sim$ 100 nm thick sheet of 1T-TaS2 by scanning 12keV focused X-ray beam across and rotating the sample. We demonstrate that the strain distribution around micron and sub-micron sized 'bubbles' that are present in the sample may be reconstructed from these images. The experiments use state of the art synchrotron instrumentation, and will allow rapid and non-intrusive strain mapping of thin film samples and electronic devices based on quasi 2D materials.

cond-mat.mtrl-sci↗

arXiv2019

Cluster-mining: An approach for determining core structures of metallic nanoparticles from atomic pair distribution function data

We present a novel approach for finding and evaluating structural models of small metallic nanoparticles. Rather than fitting a single model with many degrees of freedom, the approach algorithmically builds libraries of nanoparticle clusters from multiple structural motifs, and individually fits them to experimental PDFs. Each cluster-fit is highly constrained. The approach, called cluster-mining, returns all candidate structure models that are consistent with the data as measured by a goodness of fit. It is highly automated, easy to use, and yields models that are more physically realistic and result in better agreement to the data than models based on cubic close-packed crystallographic cores, often reported in the literature for metallic nanoparticles.

cond-mat.mtrl-sci↗

arXiv2019

Algorithm for distance list extraction from pair distribution functions

We present an algorithm to extract the distance list from atomic pair distribution functions (PDFs) in a highly automated way. The algorithm is constructed via curve fitting based on a Debye scattering equation model. Due to the non-convex nature of the resulting optimization problem, a number of techniques are developed to overcome various computational difficulties. A key ingredient is a new approach to obtain a reasonable initial guess based on the theoretical properties of the mathematical model. Tests on various nanostructured samples show the effectiveness of the initial guess and the accuracy and overall good performance of the extraction algorithm. This approach could be extended to any spectrum that is approximated as a sum of Gaussian functions.

cond-mat.mtrl-sci↗

arXiv2016

Local atomic and magnetic structure of dilute magnetic semiconductor (Ba,K)(Zn,Mn)$_2$As$_2$

We have studied the atomic and magnetic structure of the dilute ferromagnetic semiconductor system (Ba,K)(Zn,Mn)$_2$As$_2$ through atomic and magnetic pair distribution function analysis of temperature-dependent x-ray and neutron total scattering data. We detected a change in curvature of the temperature-dependent unit cell volume of the average tetragonal crystallographic structure at a temperature coinciding with the onset of ferromagnetic order. We also observed the existence of a well-defined local orthorhombic structure on a short length scale of $\lesssim 5$ Å, resulting in a rather asymmetrical local environment of the Mn and As ions. Finally, the magnetic PDF revealed ferromagnetic alignment of Mn spins along the crystallographic $c$-axis, with robust nearest-neighbor ferromagnetic correlations that exist even above the ferromagnetic ordering temperature. We discuss these results in the context of other experiments and theoretical studies on this system.

cond-mat.str-el↗