arXiv subjects

R. Sharma

Publications and source records attributed to R. Sharma.

At least 19 recordsLinked to original sources

arXiv2026

Modeling the Effect of the Heliospheric Magnetic Field on Cosmic Ray Muon Shadows

Shadows cast in the cosmic ray (CR) muon sky by the Sun were located using muon data from the MINOS far detector in Northern Minnesota. The shadows were observed independently across three time periods; near solar minimum, near solar maximum, and over the entire 13 year span of the data. A distribution of muon positions for each shadow was then sampled to simulate CR motions near the Sun using the Parker spiral model of the Heliospheric Magnetic Field (HMF) and a dipole model of the Geomagnetic Field (GMF). The resulting particle distributions were then compared to their position with respect to the Sun. Results show that the Parker spiral model is most consistent with the solar minimum shadow and least consistent with the solar maximum shadow, as expected. The simple Parker spiral is more consistent with the data for a harder CR spectrum than is actually present, indicating the need for a more detailed HMF model. Plausible modifications to the Parker spiral model which would affect the overall shift of the Sun's CR shadow are discussed.

astro-ph.SR↗

arXiv2025

Real-Time AI-Driven Milling Digital Twin Towards Extreme Low-Latency

Digital twin (DT) enables smart manufacturing by leveraging real-time data, AI models, and intelligent control systems. This paper presents a state-of-the-art analysis on the emerging field of DTs in the context of milling. The critical aspects of DT are explored through the lens of virtual models of physical milling, data flow from physical milling to virtual model, and feedback from virtual model to physical milling. Live data streaming protocols and virtual modeling methods are highlighted. A case study showcases the transformative capability of a real-time machine learning-driven live DT of tool-work contact in a milling process. Future research directions are outlined to achieve the goals of Industry 4.0 and beyond.

eess.SY↗

arXiv2025

Development and performance test of p-type Silicon pad array detector

This article reports on the development and comprehensive evaluation of p-type silicon detector arrays fabricated at the Semi-Conductor Laboratory (SCL), Mohali, India. The detectors consist of an 8~$\times$~9 array of 1~$\times$~1~cm$^2$ pads fabricated on 6-inch wafers and read out using the High Granularity Calorimeter Readout Chip (HGCROC). Electrical characterization of the detector through current vs. voltage (IV) and capacitance vs. voltage (CV) measurements demonstrated consistent breakdown and full depletion voltages across all pads, in agreement with Technology Computer-Aided Design (TCAD) device simulations. Laboratory measurements with a $^{90}$Sr source and beam tests at PS, CERN with 10 GeV pions, showed a clear Minimum Ionizing Particle (MIP) signal, well separated from the pedestal and uniform response of the pads with an average signal-to-noise (S/N) ratio above 5.5. The measured shower profiles with 2-4 GeV positron beams for various thicknesses of a tungsten absorber placed in front of the detector are found to be in agreement with the corresponding Geant4 simulations. The performance test results for the detector show that it is a promising candidate for the future ALICE upgrade detector named Forward Calorimeter (FoCal). The FoCal will have alternating layers of low and high-granularity silicon pad detectors with absorbers as a part of the electromagnetic segment, and along with its hadronic segment, will study the direct photons, neutral hadrons, vector mesons, and jets production in the low Bjorken-x region.

physics.ins-det↗

arXiv2025

The star HIP 41378 potentially misaligned with its cohort of long-period planets

The obliquity between the stellar spin axis and the planetary orbit, detected via the Rossiter-McLaughlin (RM) effect, is a tracer of the formation history of planetary systems. While obliquity measurements have been extensively applied to hot Jupiters and short-period planets, they remain rare for cold and long-period planets due to observational challenges, particularly their long transit durations. We report the detection of the RM effect for the 19-hour-long transit of HIP 41378 f, a temperate giant planet on a 542-day orbit, observed through a worldwide spectroscopic campaign. We measure a slight projected obliquity of 21 $\pm$ 8 degrees and a significant 3D spin-orbit angle of 52 $\pm$ 6 degrees, based on the measurement of the stellar rotation period. HIP 41378 f is part of a 5-transiting planetary system with planets close to mean motion resonances. The observed misalignment likely reflects a primordial tilt of the stellar spin axis relative to the protoplanetary disk, rather than dynamical interactions. HIP 41378 f is the first non-eccentric long-period (P>100 days) planet observed with the RM effect, opening new constraints on planetary formation theories. This observation should motivate the exploration of planetary obliquities across a longer range of orbital distances through international collaboration.

astro-ph.EP↗

arXiv2025

Physics-Informed Machine Learning Regulated by Finite Element Analysis for Simulation Acceleration of Melt Pool Dynamics in Laser Powder Bed Fusion

Efficient simulation of Laser Powder Bed Fusion (LPBF) is crucial for process prediction due to the lasting issue of high computational cost associated with traditional numerical methods such as finite element analysis (FEA). While a Physics-Informed Neural Network (PINN) can predict solution fields with small training data and enables the generalization of new process parameters via transfer learning, it suffers from accuracy degradation in time-dependent problems due to the accumulation of residual and the difficulty in capturing the steep spatial and temporal gradients inherent in the LPBF process. To overcome this issue, this study develops an efficient modeling framework, FEA-Regulated Physics-Informed Neural Network (FEA-PINN), to accelerate the prediction of melt pool dynamics phenomena in an LPBF process while maintaining the FEA accuracy. The innovation of FEA-PINN manifested itself in two aspects. First, a novel strategy has been developed within the PINN model to capture the dynamic phase change of powder-liquid-solid, enabling the tracking of material status during laser melting. The model further incorporates temperature-dependent material properties, phase change behavior of the powder bed, Marangoni convection, and natural convection within the melt pool. Second, the FEA-PINN framework integrates corrective FEA simulations during inference to enforce physical consistency, reduce error drift, and capture the steep gradients. A comparative analysis shows that FEA-PINN achieves accuracy comparable to FEA while significantly reducing computational cost. The framework has been validated against benchmark FEA data for single-track scanning in LPBF.

cs.LG↗

arXiv2025

Computational, Data-Driven, and Physics-Informed Machine Learning Approaches for Microstructure Modeling in Metal Additive Manufacturing

Metal additive manufacturing enables unprecedented design freedom and the production of customized, complex components. However, the rapid melting and solidification dynamics inherent to metal AM processes generate heterogeneous, non-equilibrium microstructures that significantly impact mechanical properties and subsequent functionality. Predicting microstructure and its evolution across spatial and temporal scales remains a central challenge for process optimization and defect mitigation. While conventional experimental techniques and physics-based simulations provide a physical foundation and valuable insights, they face critical limitations. In contrast, data-driven machine learning offers an alternative prediction approach and powerful pattern recognition but often operate as black-box, lacking generalizability and physical consistency. To overcome these limitations, physics-informed machine learning, including physics-informed neural networks, has emerged as a promising paradigm by embedding governing physical laws into neural network architectures, thereby enhancing accuracy, transparency, data efficiency, and extrapolation capabilities. This work presents a comprehensive evaluation of modeling strategies for microstructure prediction in metal AM. The strengths and limitations of experimental, computational, and data-driven methods are analyzed in depth, and highlight recent advances in hybrid PIML frameworks that integrate physical knowledge with ML. Key challenges, such as data scarcity, multi-scale coupling, and uncertainty quantification, are discussed alongside future directions. Ultimately, this assessment underscores the importance of PIML-based hybrid approaches in enabling predictive, scalable, and physically consistent microstructure modeling for site-specific, microstructure-aware process control and the reliable production of high-performance AM components.

cs.LG↗

arXiv2025

Square Kilometre Array Science Data Challenge 3a: foreground removal for an EoR experiment

We present and analyse the results of the Science data challenge 3a (SDC3a, https://sdc3.skao.int/challenges/foregrounds), an EoR foreground-removal community-wide exercise organised by the Square Kilometre Array Observatory (SKAO). The challenge ran for 8 months, from March to October 2023. Participants were provided with realistic simulations of SKA-Low data between 106 MHz and 196 MHz, including foreground contamination from extragalactic as well as Galactic emission, instrumental and systematic effects. They were asked to deliver cylindrical power spectra of the EoR signal, cleaned from all corruptions, and the corresponding confidence levels. Here we describe the approaches taken by the 17 teams that completed the challenge, and we assess their performance using different metrics. The challenge results provide a positive outlook on the capabilities of current foreground-mitigation approaches to recover the faint EoR signal from SKA-Low observations. The median error committed in the EoR power spectrum recovery is below the true signal for seven teams, although in some cases there are some significant outliers. The smallest residual overall is $4.2_{-4.2}^{+20} \times 10^{-4}\,\rm{K}^2h^{-3}$cMpc$^{3}$ across all considered scales and frequencies. The estimation of confidence levels provided by the teams is overall less accurate, with the true error being typically under-estimated, sometimes very significantly. The most accurate error bars account for $60 \pm 20$\% of the true errors committed. The challenge results provide a means for all teams to understand and improve their performance. This challenge indicates that the comparison between independent pipelines could be a powerful tool to assess residual biases and improve error estimation.

astro-ph.IM↗

arXiv2025

Monolayer control of spin-charge conversion in van der Waals heterostructures

The diversity of 2D materials and their van der Waals (vdW) stacking presents a fertile ground for engineering novel multifunctional materials and quantum states of matter. This permits unique opportunities to tailor the electronic properties of vdW heterostructures by the insertion of only a single 2D material layer. However, such vdW materials engineering at the atomic scale has yet to be investigated for spin-charge interconversion phenomena. Here, we report on the control of these effects at the monolayer level, where drastic increase in intensity and change in sign of THz spintronic emission are demonstrated by inserting a single layer of MoSe$_2$ between PtSe$_2$ and graphene in a fully epitaxial, large area stacked structure. By using a combination of spin and angle resolved photoemission and density functional theory to reveal the electronic and spin structures, we illustrate two different mechanisms relying on charge transfer and electronic hybridization for the formation of Rashba states, which are responsible for spin-charge conversion and hence the THz spintronic emission. These findings open new pathways to design, at the atomic scale, efficient THz spintronic emitters made of 2D materials and other spintronic devices based on spin-charge interconversion phenomena.

cond-mat.mtrl-sci↗

arXiv2024

Thermal-Mechanical Physics Informed Deep Learning For Fast Prediction of Thermal Stress Evolution in Laser Metal Deposition

Understanding thermal stress evolution in metal additive manufacturing (AM) is crucial for producing high-quality components. Recent advancements in machine learning (ML) have shown great potential for modeling complex multiphysics problems in metal AM. While physics-based simulations face the challenge of high computational costs, conventional data-driven ML models require large, labeled training datasets to achieve accurate predictions. Unfortunately, generating large datasets for ML model training through time-consuming experiments or high-fidelity simulations is highly expensive in metal AM. To address these challenges, this study introduces a physics-informed neural network (PINN) framework that incorporates governing physical laws into deep neural networks (NNs) to predict temperature and thermal stress evolution during the laser metal deposition (LMD) process. The study also discusses the enhanced accuracy and efficiency of the PINN model when supplemented with small simulation data. Furthermore, it highlights the PINN transferability, enabling fast predictions with a set of new process parameters using a pre-trained PINN model as an online soft sensor, significantly reducing computation time compared to physics-based numerical models while maintaining accuracy.

cs.LG↗

arXiv2024

Broadband study of the Be X-ray binary RX J0520.5-6932 during its outburst in 2024

A new giant outburst of the Be X-ray binary RX J0520.5-6932 was detected and subsequently observed with several space-borne and ground-based instruments. This study presents a comprehensive analysis of the optical and X-ray data, focusing on the spectral and timing characteristics of selected X-ray observations. A joint fit of spectra from simultaneous observations performed by the X-ray telescope (XRT) on the Neil Gehrels Swift Observatory (Swift) and Nuclear Spectroscopic Telescope ARray (NuSTAR) provides broadband parameter constraints, including a cyclotron resonant scattering feature (CRSF) at 32.2(+0.8/-0.7) keV with no significant energy change since 2014, and a weaker Fe line. Independent spectral analyses of observations by the Lobster Eye Imager for Astronomy (LEIA), Einstein Probe (EP), Swift-XRT, and NuSTAR demonstrate the consistency of parameters across different bands. Luminosity variations during the current outburst were tracked. The light curve of the Optical Gravitational Lensing Experiment (OGLE) aligns with the X-ray data in both 2014 and 2024. Spin evolution over 10 years is studied after adding Fermi Gamma-ray Burst Monitor (GBM) data, improving the orbital parameters, with an estimated orbital period of 24.39 days, slightly differing from OGLE data. Despite intrinsic spin-up during outbursts, a spin-down of ~0.04s over 10.3 years is suggested. For the new outburst, the pulse profiles indicate a complicated energy-dependent shape, with decreases around 15 keV and 25 keV in the pulsed fraction, a first for an extragalactic source. Phase-resolved NuSTAR data indicate variations in parameters such as flux, photon index, and CRSF energy with rotation phase.

astro-ph.HE↗

arXiv2024

Superheavy Magic Nuclei: Ground-State Properties, Bubble Structure and {\alpha}-Decay Chains

A systematic investigation of superheavy nuclei in the isotopic chains of proton numbers Z=106, 114, 120, and 126 together with isotonic chains of neutron numbers N=162, 172, and 184 is presented in the theoretical framework of relativistic mean-field density functionals based on density-dependent meson-nucleon couplings. Ground-state properties, including binding energy, shape, deformation, density profile, and radius, are estimated to provide compelling evidence of magicity in these even-even nuclei, aligning with the concept of the 'island of stability'. The analysis reveals central depletion in the charge density, indicating a bubble-like structure, primarily attributed to the substantial repulsive Coulomb field and the influence of higher l-states. A thorough examination of potential decay modes, employing various semi-empirical formulas, is presented. The probable alpha-decay chains are evaluated, demonstrating excellent agreement with available experimental data

nucl-th↗

arXiv2024

The STAR Forward Silicon Tracker

The Forward Silicon Tracker (FST) is a pivotal component of the forward upgrade of the Solenoidal Tracker at RHIC (STAR), designed to discern hadron charge signs with a momentum resolution better than 30% for $0.2 < p_T < 2$ GeV/c in the $2.5 < \eta < 4$ pseudorapidity range. Its compact design features three disks along the beam direction, minimized material budget, and scattering effects. The FST uses Hamamatsu's p-in-n silicon strip sensors with a double metal layer that enables efficient signal routing to the readout electronics, enhancing overall detector performance. The flexible hybrid boards, essential for the readout system, are constructed with Kapton and copper layers to optimize signal handling and power distribution. These boards connect silicon strips to analogue pipeline ASIC APV25-S1 chips, which read up to 128 channels each. A cooling system with nonconducting, volatile NOVEC 7200 coolant at 22.2{\deg}C mitigates ASIC-generated heat. The FST enhances forward tracking performance at STAR as an integral part of the forward upgrade.

physics.ins-det↗

arXiv2024

The variable radio jet of the accreting neutron star the Rapid Burster

The Rapid Burster is a unique neutron star low-mass X-ray binary system, showing both thermonuclear Type-I and accretion-driven Type-II X-ray bursts. Recent studies have demonstrated how coordinated observations of X-ray and radio variability can constrain jet properties of accreting neutron stars - particularly when the X-ray variability is dominated by discrete changes. We present a simultaneous VLA, Swift, and INTEGRAL observing campaign of the Rapid Burster to investigate whether its jet responds to Type-II bursts. We observe the radio counterpart of the X-ray binary at its faintest-detected radio luminosity, while the X-ray observations reveal prolific, fast X-ray bursting. A time-resolved analysis reveals that the radio counterpart varies significantly between observing scans, displaying a fractional variability of $38 \pm 5$%. The radio faintness of the system prevents the robust identification of a causal relation between individual Type-II bursts and the evolution of the radio jet. However, based on a comparison of its low radio luminosity with archival Rapid Burster observations and other accreting neutron stars, and on a qualitative assessment of the X-ray and radio light curves, we explore the presence of a tentative connection between bursts and jet: i.e., the Type-II bursts may weaken or strengthen the jet. The former of those two scenarios would fit with magneto-rotational jet models; we discuss three lines of future research to establish this potential relation between Type-II bursts and jets more confidently.

astro-ph.HE↗

arXiv2024

Anisotropic star with a linear equation of state (EOS)

A family of solutions defining the interior of a static, spherically symmetric, compact anisotropic star is described by considering a new form of the equation of state (EOS). The analytic solution is derived by using the Finch and Skea ansatz for the metric potential g_rr, which has a clear geometric interpretation for the related background spacetime. The model parameters are fixed by smooth matching of the interior solution to the Schwarzschild exterior metric over the bounding surface of the compact star, together with the requirement that the radial pressure vanishes at the boundary. Data available for the pulsar 4U1802030 has been utilized to analyze the physical viability of the developed model. The model is shown to be stable.

gr-qc↗

arXiv2024

Investigation on the impact of solar flares on the Martian atmospheric emissions in the dayside near-terminator region: Case Studies

Solar transient events like flares can cause sudden changes in planetary plasma and neutral environment. Here, we present an investigation of the variability of the Martian atmospheric emissions viz. OI 130.4 nm, 135.6 nm, CO2+ ultraviolet doublet (UVD), and CO Cameron band (CB) in the less explored dayside near-terminator region during solar flare events. The two X8.2 and M6 class flares during September 2017 on Mars have been selected from existing catalogs. Using data from the imaging ultraviolet spectrograph (IUVS) aboard the MAVEN spacecraft, we examined limb radiance profiles. We observed a significant increase in radiance for major emissions around the peak with a more pronounced impact below the peak during flares compared to quiet time. During solar flares, for 130.4 nm and 135.6 nm emission, the maximum deviation in radiance beneath peak approaches to ~63% and ~123%, respectively. Whereas, for CO2+ UVD and CO CB, it is ~64% and ~50%, respectively. Additionally, we have presented an average scenario of dayside near-terminator (SZA~70-90{\deg}) and observed a notable trend of higher percentage deviation for atomic emissions compared to molecular emissions during flares. Further, our analysis depicts a higher percentage deviation during X8.2 compared to M6 class flare. This study underscores that during flares, higher photoelectron impact and irradiance flux drive the production processes, leading to enhanced emissions. The case studies emphasize, for the first time, the significant influence of flares on the Martian dayside near-terminator region, advancing our deeper understanding the impact of varying solar flare intensities to the planetary atmosphere.

physics.space-ph↗

arXiv2023

Shaping the CO snowline in protoplanetary disks

Characterizing the dust thermal structure in protoplanetary disks is a fundamental task as the dust surface temperature can affect both the planetary formation and the chemical evolution. Since the temperature is dependent on many parameters, including the grain size, properly modeling the grain temperature structure can be challenging. Many chemistry disk models usually employ a sophisticated single dust structure designed to reproduce the effect of a realistic population presumably composed of a large diversity of sizes. This generally represents a good approximation in most cases. Nonetheless, this dilutes the effects of the complex radiative interactions between the different grain populations on the resulting dust temperature, and thus the chemistry. We seek to show that the radiative interactions between dust grains of different sizes can induce a non-trivial dust temperature structure that cannot be reproduced by a single dust population and that can significantly affect the chemical outcome. The disk thermal structures are computed using the Monte-Carlo radiative transfer code RADMC-3D. The thermal structures are post-processed using the gas-grain code NAUTILUS to calculate the evolution of the chemical abundance. We find that simultaneously using at least two independent dust grain populations in disk models produces a complex temperature structure due to the starlight intercepted by the upper layers of the disk. In particular, we find that micron-sized dust grains are warmer than larger grains and can even show a radial temperature bump in some conditions. This dust temperature spread between the grains populations results in the segregation of the CO snowline and the presence of an unexpected CO gas hole along the midplane. We compare the results with observed close to edge-on class I/II disks.

astro-ph.SR↗

arXiv2023

Physics-Informed Machine Learning of Argon Gas-Driven Melt Pool Dynamics

Melt pool dynamics in metal additive manufacturing (AM) is critical to process stability, microstructure formation, and final properties of the printed materials. Physics-based simulation including computational fluid dynamics (CFD) is the dominant approach to predict melt pool dynamics. However, the physics-based simulation approaches suffer from the inherent issue of very high computational cost. This paper provides a physics-informed machine learning (PIML) method by integrating neural networks with the governing physical laws to predict the melt pool dynamics such as temperature, velocity, and pressure without using any training data on velocity. This approach avoids solving the highly non-linear Navier-Stokes equation numerically, which significantly reduces the computational cost. The difficult-to-determine model constants of the governing equations of the melt pool can also be inferred through data-driven discovery. In addition, the physics-informed neural network (PINN) architecture has been optimized for efficient model training. The data-efficient PINN model is attributed to the soft penalty by incorporating governing partial differential equations (PDEs), initial conditions, and boundary conditions in the PINN model.

cs.LG↗

arXiv2023

The JWST FRESCO Survey: Legacy NIRCam/Grism Spectroscopy and Imaging in the two GOODS Fields

We present the JWST Cycle 1 53.8hr medium program FRESCO, short for "First Reionization Epoch Spectroscopically Complete Observations". FRESCO covers 62 arcmin$^2$ in each of the two GOODS/CANDELS fields for a total area of 124 arcmin$^2$ exploiting JWST's powerful new grism spectroscopic capabilities at near-infrared wavelengths. By obtaining ~2 hr deep NIRCam/grism observations with the F444W filter, FRESCO yields unprecedented spectra at R~1600 covering 3.8 to 5.0 $\mu$m for most galaxies in the NIRCam field-of-view. This setup enables emission line measurements over most of cosmic history, from strong PAH lines at z~0.2-0.5, to Pa$\alpha$ and Pa$\beta$ at z~1-3, HeI and [SIII] at z~2.5-4.5, H$\alpha$ and [NII] at z~5-6.5, up to [OIII] and H$\beta$ for z~7-9 galaxies, and possibly even [OII] at z~10-12. FRESCO's grism observations provide total line fluxes for accurately estimating galaxy stellar masses and calibrating slit-loss corrections of NIRSpec/MSA spectra in the same field. Additionally, FRESCO results in a mosaic of F182M, F210M, and F444W imaging in the same fields to a depth of ~28.2 mag (5 $\sigma$ in 0.32" diameter apertures). Together with this publication, the v1 imaging mosaics are released as high-level science products via MAST. Here, we describe the overall survey design and the key science goals that can be addressed with FRESCO. We also highlight several, early science results, including: spectroscopic redshifts of Lyman break galaxies that were identified almost 20 years ago, the discovery of broad-line active galactic nuclei at z>4, and resolved Pa$\alpha$ maps of galaxies at z~1.4. These results demonstrate the enormous power for serendipitous discovery of NIRCam/grism observations.

astro-ph.GA↗