arXiv subjects

Debasish Dutta

Publications and source records attributed to Debasish Dutta.

8 recordsLinked to original sources

arXiv2026

Magnetic Signatures in Merger Products

Asteroseismic estimates of the magnetic field strength in the radiative interior of red giant stars depend strongly on the internal stellar structure derived from models. Since red giant branch merger products have been shown to be able to possess a different core structure than single stars of the same mass, we investigate how a mass-gain history influences our estimates of an internal magnetic field strength. We construct stellar models with and without a mass-gain event after the onset of the RGB evolutionary phase with masses of $1.1\,M_\odot \le M \le 2\,M_\odot$. First, by assuming a weak magnetic field, we investigate the influence of a mass-gain event on the global sensitivity of the oscillation frequencies to the magnetic field. We find that mass-gain models can be several times more sensitive to the field than single-star models of identical total mass at masses above $1.6\,M_\odot$. Therefore, considering a mass-gain evolutionary history for merger candidates allows a significant correction to the magnetic field strength. In the presence of strong magnetic fields, we also show that the critical field strength needed to suppress mixed dipole modes is significantly lower if a mass-gain event has occurred (for stars with masses $M\gtrsim1.6\,M_\odot$). The massive end of the suppressed stars' distribution is therefore strongly favored by a merger origin. We conclude that properly constraining the stellar evolutionary history is essential when aiming to constrain internal field strength estimates from asteroseismic observations.

astro-ph.SR↗

arXiv2026

A Galactic intermediate-mass stripped star with a Wolf-Rayet-like wind

Binary interaction in massive stars is expected to produce a large population of intermediate-mass ($2$-$8$ M$_\odot$) envelope-stripped stars, yet such objects have remained elusive in the Milky Way. We report the identification of an unambiguous Galactic example in a short-period ($P=5.94$ d), double-lined spectroscopic binary, discovered in the SDSS-V Milky Way Mapper survey. The system consists of a rapidly rotating O-type star and a hotter, lower-mass companion, which shows He II and N IV emission lines with large radial velocity variations, revealing its binary nature. Combined orbital constraints and joint spectroscopic and photometric modelling show that the companion is a hot ($T_\ast \approx 60$ kK), helium-rich star with a mass of $3.2$-$5.8$ M$_\odot$, placing it squarely in the intermediate-mass regime and below values typically inferred for classical Wolf-Rayet (WR) stars. The system's short period, negligible eccentricity, and rapidly rotating O-star point to a post-interaction configuration following efficient mass transfer and spin-up of the accretor. Comparison with binary evolution models suggests that the stripped star is observed in a brief inflated phase following mass transfer, which increases its optical flux contribution and facilitates its detection. The inferred mass-loss rate $\log \dot{M} = -6.3 \pm 0.1$ is in line with mass-loss rates observed for classical WR stars in the Milky Way and exceeds those measured for intermediate-mass stripped stars in the Magellanic Clouds, with the caveat that our target selection is biased towards systems with stronger emission features. As an unambiguous and well-characterised intermediate-mass stripped star, this system provides a key benchmark for models of binary evolution at solar metallicity, stripped-envelope supernova progenitors, and the formation of compact-object binaries.

astro-ph.SR↗

arXiv2025

Recent Advancements in Microscopy Image Enhancement using Deep Learning: A Survey

Microscopy image enhancement plays a pivotal role in understanding the details of biological cells and materials at microscopic scales. In recent years, there has been a significant rise in the advancement of microscopy image enhancement, specifically with the help of deep learning methods. This survey paper aims to provide a snapshot of this rapidly growing state-of-the-art method, focusing on its evolution, applications, challenges, and future directions. The core discussions take place around the key domains of microscopy image enhancement of super-resolution, reconstruction, and denoising, with each domain explored in terms of its current trends and their practical utility of deep learning.

eess.IV↗

arXiv2025

Image Segmentation with transformers: An Overview, Challenges and Future

Image segmentation, a key task in computer vision, has traditionally relied on convolutional neural networks (CNNs), yet these models struggle with capturing complex spatial dependencies, objects with varying scales, need for manually crafted architecture components and contextual information. This paper explores the shortcomings of CNN-based models and the shift towards transformer architectures -to overcome those limitations. This work reviews state-of-the-art transformer-based segmentation models, addressing segmentation-specific challenges and their solutions. The paper discusses current challenges in transformer-based segmentation and outlines promising future trends, such as lightweight architectures and enhanced data efficiency. This survey serves as a guide for understanding the impact of transformers in advancing segmentation capabilities and overcoming the limitations of traditional models.

cs.CV↗

arXiv2025

Identification of Traditional Medicinal Plant Leaves Using an effective Deep Learning model and Self-Curated Dataset

Medicinal plants have been a key component in producing traditional and modern medicines, especially in the field of Ayurveda, an ancient Indian medical system. Producing these medicines and collecting and extracting the right plant is a crucial step due to the visually similar nature of some plants. The extraction of these plants from nonmedicinal plants requires human expert intervention. To solve the issue of accurate plant identification and reduce the need for a human expert in the collection process; employing computer vision methods will be efficient and beneficial. In this paper, we have proposed a model that solves such issues. The proposed model is a custom convolutional neural network (CNN) architecture with 6 convolution layers, max-pooling layers, and dense layers. The model was tested on three different datasets named Indian Medicinal Leaves Image Dataset,MED117 Medicinal Plant Leaf Dataset, and the self-curated dataset by the authors. The proposed model achieved respective accuracies of 99.5%, 98.4%, and 99.7% using various optimizers including Adam, RMSprop, and SGD with momentum.

cs.CV↗

arXiv2025

State-of-the-Art Transformer Models for Image Super-Resolution: Techniques, Challenges, and Applications

Image Super-Resolution (SR) aims to recover a high-resolution image from its low-resolution counterpart, which has been affected by a specific degradation process. This is achieved by enhancing detail and visual quality. Recent advancements in transformer-based methods have remolded image super-resolution by enabling high-quality reconstructions surpassing previous deep-learning approaches like CNN and GAN-based. This effectively addresses the limitations of previous methods, such as limited receptive fields, poor global context capture, and challenges in high-frequency detail recovery. Additionally, the paper reviews recent trends and advancements in transformer-based SR models, exploring various innovative techniques and architectures that combine transformers with traditional networks to balance global and local contexts. These neoteric methods are critically analyzed, revealing promising yet unexplored gaps and potential directions for future research. Several visualizations of models and techniques are included to foster a holistic understanding of recent trends. This work seeks to offer a structured roadmap for researchers at the forefront of deep learning, specifically exploring the impact of transformers on super-resolution techniques.

cs.CV↗

arXiv2025

Developing a Modular Compiler for a Subset of a C-like Language

The paper introduces the development of a modular compiler for a subset of a C-like language, which addresses the challenges in constructing a compiler for high-level languages. This modular approach will allow developers to modify a language by adding or removing subsets as required, resulting in a minimal and memory-efficient compiler. The development process is divided into small, incremental steps, where each step yields a fully functioning compiler for an expanding subset of the language. The paper outlines the iterative developmental phase of the compiler, emphasizing progressive enhancements in capabilities and functionality. Adherence to industry best practices of modular design, code reusability, and documentation has enabled the resulting compiler's functional efficiency, maintainability, and extensibility. The compiler proved to be effective not only in managing the language structure but also in developing optimized code, which demonstrates its practical usability. This was also further assessed using the compiler on a tiny memory-deficient single-board computer, again showing the compiler's efficiency and suitability for resource-constrained devices.

cs.PL↗

arXiv2023

On the evolutionary nature of puffed-up stripped star binaries and their occurrence in stellar populations

The majority of massive stars are formed in multiple systems and will interact with companions via mass transfer. This interaction typically leads to the primary shedding its envelope and the formation of a "stripped star". Classically, stripped stars are expected to quickly contract to become hot UV-bright helium stars. Surprisingly, recent optical surveys have unveiled a large number of stripped stars that are larger and cooler, appearing "puffed-up" and overlapping with the Main Sequence (MS). Here, we study the evolutionary nature and lifetimes of puffed-up stripped (PS) stars using stellar-evolution code MESA. We computed grids of binary models at four metallicities from Z = 0.017 to 0.0017. Contrary to previous assumptions, we find that stripped stars regain thermal equilibrium shortly after the end of mass transfer. Their further contraction is determined by the rate at which the residual H-rich envelope is depleted, with the main agents being H-shell burning (dominant) and mass-loss in winds. The duration of the PS star phase is 1$\%$ of the total lifetime and up to 100 times more than thermal timescale. We explored several relevant factors: orbital period, mass ratio, winds, and semiconvection. We carried out a simple population estimation, finding that $\sim$0.5-0.7 $\%$ of all the stars with $\log (L/L_{\rm \odot}$) $>$ 3.7 are PS stars. Our results indicate that tens to hundred of PS stars may be hiding in the MS population, disguised as normal stars: $\sim$100 in the Small Magellanic Cloud alone. Their true nature may be revealed by low surface gravities, high N enrichment, and likely slow rotations

astro-ph.SR↗