arXiv subjects

Subhasish Das

Publications and source records attributed to Subhasish Das.

5 recordsLinked to original sources

arXiv2026

Importance and Science Outcomes from the first XSPECT/XPoSat Workshop

This paper summarizes the science outcomes of the first Workshop on Data Analysis using observations from the XSPECT payload onboard the XPoSat, which brought together early-career researchers and experts to explore the instrument's scientific capabilities through lectures and hands-on analyses. Participants performed end-to-end data analysis, including calibration, spectral modeling, and timing studies, on seven sources comprising Neutron Star Low-Mass X-ray Binaries, pulsars, and Black Hole X-ray Binaries, demonstrating the instrument's scientific potential. The observations, obtained during the first year of XSPECT operations, together with in-house developed software, were provided to the participants, making them the first users outside the instrument team to analyze XSPECT data. For NS-LMXBs, Aql X-1 exhibited a classical Type-I X-ray burst, enabling constraints on the stellar radius through spectral fitting. Sco X-1, observed across its complete Z-track, revealed systematic spectral evolution driven by accretion-rate fluctuations and disk-corona coupling, while Cir X-1 displayed orbital phase-dependent transitions between hard and soft states, reflecting changes in accretion geometry. Among accretion-powered pulsars, GX 301-2 showed a double-peaked, energy-dependent pulse profile and strong iron fluorescence lines due to stellar wind reprocessing, whereas Vela X-1 exhibited orbital phase-dependent absorption and steady coronal temperatures. Among BH-XRBs, Cyg X-1 transitioned from a hard to soft-intermediate state with increasing disk contribution and spectral softening, while Cyg X-3 remained in the intermediate state with multiple emission lines originating from a clumpy stellar wind. The workshop outcomes highlight the scientific promise of XSPECT and the importance of collaborative training in maximizing the science from XSPECT and future Indian space astronomy missions.

astro-ph.HE↗

arXiv2026

X-ray spectral and temporal evolution of atoll source 4U 1820-30 with AstroSat: detection of high frequency quasi-periodic oscillation

AstroSat/LAXPC and SXT observed the persistent neutron star low-mass X-ray binary 4U 1820-30 between 2016 and 2022. During these observations, the hardness-intensity diagram (HID) and color-color diagram (CCD) indicated that the source was in the banana state. We divided the CCD into 11 segments for spectral and timing analyses. For each segment in the CCD, we modeled the spectral data using two distinct approaches over the 0.7-20.0 keV band. A combination of a multi-color-disk component with an inner disk temperature of around 0.6 keV and Comptonized emission from the boundary layer (BL)/ hot corona provided the best description of the X-ray spectral data of this source. The truncation radius was found to be in the range of $\sim$ 19-40 km. The Comptonized component has an optical depth in the range of $\sim 7 - 13$ with electron temperature in the range of $\sim 2.5 - 3.8$ keV. The optical depth of the corona varies significantly along the position on the CCD, while $\sim$ 80\% of the X-ray flux comes from the Comptonized component. We discuss possible physical scenarios to explain the relationship between the spectral evolution and motion of the source along the CCD. The timing analysis revealed kHz QPOs peaks at $\sim 710$ Hz and $\sim 740$ Hz in the lower left banana branch. An energy-dependent study indicates that these QPOs are stronger in the high-energy band.

astro-ph.HE↗

arXiv2025

Spectro-Polarimetric Study of Weakly Magnetized Neutron Star X-ray binary GX 349+2

We report the first spectro-polarimetric investigation of the bright Z-type source GX 349+2 using simultaneous observations of \textit{IXPE}, \textit{NuSTAR} and \textit{Swift/XRT}. The source exhibited significant polarization in the 2-8 keV energy range during the flaring branch (FB) and normal branch (NB). The estimated polarization degree (PD) and polarization angle (PA) for FB are $1.74 \pm 0.52\%$ ($3.3\sigma$) and $19.4 \pm 8.9^\circ$, respectively; while for NB, PD and PA are $0.8 \pm 0.22\%$ ($3.6\sigma$) and $35.4 \pm 7.9^\circ$, respectively. The energy-resolved polarization for NB revealed an increase in PD from $0.78\pm0.2\%$ ($3.6\sigma$) to $1.32\pm0.40\%$ ($3.3\sigma$) and a change in PA from $17.9\pm8.1^\circ$, and $53.2\pm8.6^\circ$, in the energy range of 2-4 and 4-8 keV, respectively. Using the simultaneous observations of \textit{Swift/XRT} and \textit{NuSTAR}, we investigated the spectral properties of the source during NB, and the Western model well explained it. The spectra also depicted a strong and broad Fe K$\alpha$ line. However, spectro-polarimetric analyses carried out by \textit{IXPE} align closely with model-independent polarimetric results. We discuss results obtained from the polarimetric studies in the context of various coronal geometries, and we confirm the slab-like geometry in the NB for GX 349+2.

astro-ph.HE↗

arXiv2024

Comparative Analysis of Machine Learning and Deep Learning Models for Classifying Squamous Epithelial Cells of the Cervix

The cervix is the narrow end of the uterus that connects to the vagina in the female reproductive system. Abnormal cell growth in the squamous epithelial lining of the cervix leads to cervical cancer in females. A Pap smear is a diagnostic procedure used to detect cervical cancer by gently collecting cells from the surface of the cervix with a small brush and analyzing their changes under a microscope. For population-based cervical cancer screening, visual inspection with acetic acid is a cost-effective method with high sensitivity. However, Pap smears are also suitable for mass screening due to their higher specificity. The current Pap smear analysis method is manual, time-consuming, labor-intensive, and prone to human error. Therefore, an artificial intelligence (AI)-based approach for automatic cell classification is needed. In this study, we aimed to classify cells in Pap smear images into five categories: superficial-intermediate, parabasal, koilocytes, dyskeratotic, and metaplastic. Various machine learning (ML) algorithms, including Gradient Boosting, Random Forest, Support Vector Machine, and k-Nearest Neighbor, as well as deep learning (DL) approaches like ResNet-50, were employed for this classification task. The ML models demonstrated high classification accuracy; however, ResNet-50 outperformed the others, achieving a classification accuracy of 93.06%. This study highlights the efficiency of DL models for cell-level classification and their potential to aid in the early diagnosis of cervical cancer from Pap smear images.

eess.IV↗

arXiv2022

Reasoning about Actions over Visual and Linguistic Modalities: A Survey

'Actions' play a vital role in how humans interact with the world and enable them to achieve desired goals. As a result, most common sense (CS) knowledge for humans revolves around actions. While 'Reasoning about Actions & Change' (RAC) has been widely studied in the Knowledge Representation community, it has recently piqued the interest of NLP and computer vision researchers. This paper surveys existing tasks, benchmark datasets, various techniques and models, and their respective performance concerning advancements in RAC in the vision and language domain. Towards the end, we summarize our key takeaways, discuss the present challenges facing this research area, and outline potential directions for future research.

cs.CL↗