arXiv subjects

Rahul Sharma

Publications and source records attributed to Rahul Sharma.

At least 37 records · Page 2Linked to original sources

arXiv2025

Probing accretion dynamics and spin evolution in the X-ray pulsar RX J0520.5-6932 during its 2024 Outburst

After nearly a decade of quiescence, the transient Be/X-ray binary pulsar RX J0520.5-6932 underwent an outburst in 2024. We performed X-ray monitoring of the source with NICER and AstroSat near the peak of the event. Our primary objective is to investigate the energy and luminosity dependence of the pulsed emission, characterize the spin evolution, and study the broadband X-ray spectral properties of RX J0520.5-6932 during the outburst. The AstroSat/LAXPC and NICER light curves reveal pronounced short-duration flaring activity lasting ~400-700 s, with enhancements by a factor of ~2. The pulse profile exhibits a strong dependence on both energy and intensity, evolving from a simple single-peaked structure at low energies to complex multi-peaked shapes at intermediate energies, and reverting to simpler morphologies at higher energies. Pulse profiles during the flares differ significantly from those in the persistent state, indicating changes in the pulsed beam pattern with a change in the intensity on a short timescale. Broadband spectral analysis reveals a soft excess and an emission feature at ~1 keV, likely arising from reprocessed emission in the accretion disc and fluorescence from Ne K and Fe L ions. Continuous NICER monitoring over nearly one orbital cycle enabled us to track spin evolution with accretion-driven spin-up and spectral variability in the soft X-ray band. Additionally, a declining spin-up rate is observed during the outburst, likely due to a gradual reduction in mass accretion rate. Our results provide a comprehensive view of the complex accretion dynamics in RX J0520.5-6932 during its 2024 outburst. The strong variability in pulse shape and spin behaviour highlights rapid changes in the accretion geometry and torque as a function of accretion rate. [Abridged]

astro-ph.HE↗

arXiv2025

Integrated physics-informed learning and resonance process signature for the prediction of fatigue crack growth for laser-fused alloys

Fatigue behaviors of metal components by laser fusion suffer from scattering due to random geometrical defects (e.g., porosity, lack of fusion). Monitoring fatigue crack initiation and growth is critical, especially for laser-fused components with significant inherent fatigue scattering. Conventional statistics-based curve-fitting fatigue models have difficulty incorporating significant scattering in their fatigue life due to the random geometrical defects. A scattering-informed predictive method is needed for laser-fused materials' crack size and growth. Current data-driven machine learning could circumvent the issue of deterministic modeling, but results in a black-box function that lacks interpretability. To address these challenges, this study explores a novel nondimensionalized physics-informed machine learning (PIML) model to predict fatigue crack growth of laser-fused SS-316L by integrating fatigue laws and constraints with small data to ensure a realistic and interpretable prediction. Resonance process signature data were leveraged with Paris's law to train the PIML model without experimental crack growth data. The results show that Paris's law constants can be learned with good similarity to comparable data from the literature, and the crack growth rate can be predicted to compute crack sizes.

cs.CE↗

arXiv2025

Crystal Growth, Band Structure, Magnetism and Electrochemical Properties of Hexavalent Strontium Ruthenium Oxyhydroxide

Ruthenates comprise an interesting class of materials with a wide range of extremely exciting properties, and thus the discovery of new stable ruthenates remains an active area of investigation. We report the crystal growth and comprehensive studies including crystal and electronic structure, magnetic and electrochemical properties of a hexavalent ruthenium oxyhydroxide Sr3Ru2O9H2 prepared through a low-temperature hydrothermal method. Single crystals and powder samples of this phase are isolated by optimising the Sr(OH)2 to KRuO4 ratio while maintaining a high base concentration. The new structure consists of a rare five-coordinated RuVI featuring isolated trigonal prisms and crystallising in a non-centrosymmetric tetragonal system. Isolated Ru polyhedra leading to a large spatial distance ~ 50 pm between the Ru metal centres render the compound paramagnetic despite strong antiferromagnetic correlation. Band structure calculation suggests a metal-like electronic ground state with mostly Ru d and O p orbitals contributing to the Fermi surface. The electrochemical performance of Sr3Ru2O9H2, though not as impressive as RuO2, remains relevant and is on par with other reported OER catalysts.

cond-mat.mtrl-sci↗

arXiv2025

Enterprise AI Must Enforce Participant-Aware Access Control

Large language models (LLMs) are increasingly deployed in enterprise settings where they interact with multiple users and are trained or fine-tuned on sensitive internal data. While fine-tuning enhances performance by internalizing domain knowledge, it also introduces a critical security risk: leakage of confidential training data to unauthorized users. These risks are exacerbated when LLMs are combined with Retrieval-Augmented Generation (RAG) pipelines that dynamically fetch contextual documents at inference time. We demonstrate data exfiltration attacks on AI assistants where adversaries can exploit current fine-tuning and RAG architectures to leak sensitive information by leveraging the lack of access control enforcement. We show that existing defenses, including prompt sanitization, output filtering, system isolation, and training-level privacy mechanisms, are fundamentally probabilistic and fail to offer robust protection against such attacks. We take the position that only a deterministic and rigorous enforcement of fine-grained access control during both fine-tuning and RAG-based inference can reliably prevent the leakage of sensitive data to unauthorized recipients. We introduce a framework centered on the principle that any content used in training, retrieval, or generation by an LLM is explicitly authorized for \emph{all users involved in the interaction}. Our approach offers a simple yet powerful paradigm shift for building secure multi-user LLM systems that are grounded in classical access control but adapted to the unique challenges of modern AI workflows. Our solution has been deployed in Microsoft Copilot Tuning, a product offering that enables organizations to fine-tune models using their own enterprise-specific data.

cs.CR↗

arXiv2025

Thermonuclear X-ray bursts across the eclipse transitions in the LMXBs EXO 0748-676 and XTE J1710-281

The primary radiation from thermonuclear X-ray bursts observed in the neutron star low-mass X-ray binary (LMXB) systems can interact with various parts of the binary system. This interaction gives rise to secondary radiation in different wavelength ranges, known as reprocessed emission. In eclipsing LMXBs, the reprocessed emission from the bursts can be examined during eclipses, as the primary emission is blocked and only the reprocessed emission is visible. We searched for bursts during eclipses in the archival RXTE data of the eclipsing LMXBs and found them in EXO 0748-676 and XTE J1710-281. In EXO 0748-676, seven bursts were found to occur near eclipse egress, with their tails extending beyond the eclipse, and one such burst was found for XTE J1710-281. We estimate the reprocessing fraction at orbital phases near eclipse egress by modeling the peculiar eclipse bursts detected in both systems, which have tails extending beyond the eclipses. We observe an increasing trend in reprocessing fraction as these eclipse bursts occur closer to the eclipse egress. We discuss the possibilities of reprocessing in the ablated wind from the companion star, the accretion disc, and the disc wind in EXO 0748-676 and XTE J1710-281. Additionally, we observe two decay components in the bursts in EXO 0748-676, which could suggest a complex composition of the accreting fuel. From the burst rise timescales, we place an upper limit on the size of the reprocessing regions in both EXO 0748-676 and XTE J1710-281, finding it comparable to the size of the respective X-ray binaries.

astro-ph.HE↗

arXiv2025

Reprocessing of X-rays emission in Ultra-Luminous X-ray sources

With the discovery of pulsations in some of the ultra-luminous X-ray sources (ULXs), it is quite clear that most of the ULXs harbor either a neutron star or a stellar mass black hole as a compact object accreting at super-Eddington rates. In spite of having such a high accretion rate, the reprocessed emission in the ULX sources is quite meagre compared to that observed in Galactic X-ray binaries, except for some absorption lines in the winds. In this work, we investigate the extent of reprocessed emission in ULXs using three diagnostics: (i) searches for Fe $\rm Kα$ lines in bright well-known ULXs and Ultra luminous X-ray Pulsars (ULXPs), (ii) evolution of hardness ratio around the eclipse transitions in the eclipsing ULXs, and (iii) the flux ratio between eclipse and out-of-eclipse (OOE) phases in eclipsing ULXs. We placed the most stringent constraints to date on the upper limits on EW of the iron line, 11--20 eV. Furthermore, we have not observed any significant changes in the hardness ratio during the ingress or egress, while in Galactic eclipsing X-ray binaries, an increase in the hardness ratio is observed during the transitions. Finally, the reprocessing efficiency (eclipse to OOE flux ratio) is found to be larger in ULXs compared to Galactic eclipsing X-ray binaries. Based on these results, we discuss the possibility of a metal-poor or highly ionized environment surrounding the ULXs, which suppresses reprocessed emission features.

astro-ph.HE↗

arXiv2025

Spectral analysis of ultraluminous X-ray pulsars with models of X-ray pulsars

A fraction of the Ultra Luminous X-ray (ULX) sources are known to be accreting neutron stars as they show coherent X-ray pulsations with pulse periods ranging from ~1-30 seconds. While initially thought to host intermediate-mass black holes, ULXs have since been recognized as a diverse class of objects, including ULX pulsars. These pulsars require models specifically tailored to account for their unique accretion physics, distinct from those used for Galactic black hole binaries. The X-ray spectra of all Galactic accreting X-ray pulsars (including sources in the Magellanic Clouds) are dominated by a high energy cut-off power-law and some of the sources show a soft excess, some emission lines, cyclotron absorption features, etc. In this work, we undertake a comprehensive analysis of the broadband X-ray spectra of five ULX pulsars using simultaneous XMM-Newton and NuSTAR observations and show that their X-ray spectra can be effectively described by spectral models, similar to those used for the local accretion-powered X-ray pulsars. A soft excess is detected in all the sources which is also consistent with the local X-ray pulsars that have low absorption column density. We have marginal detection or low upper limit on the presence of the iron K-alpha emission line from these sources, which is a key difference of the ULX pulsars with the local accreting X-ray pulsars. We discuss the implication of this on the nature of the binary companion and the accretion mechanism in the ULX pulsars.

astro-ph.HE↗

arXiv2025

Solar Alfvenic Pulses and Mesoscale Solar Wind

Large-scale solar ejections are well understood, but the extent to which small-scale solar features directly influence the solar wind remains an open question, primarily due to the challenges of tracing these small-scale ejections and their impact. Here, we measure the fine-scale motions of network bright points along a coronal hole boundary in high-resolution H-alpha images from the 1.6m Goode Solar Telescope at Big Bear Solar Observatory to quantify the agitation of open flux tubes into generating Alfvenic pulses. We combine the motion, magnetic flux, and activity duration of the flux tubes to estimate the energy content carried by individual Alfvenic pulses, which is ~10+25 erg, adequately higher than the energies ~10+23 erg estimated for the magnetic switchbacks observed by the Parker Solar Probe (PSP). This implies the possibility that the surface-generated Alfvenic pulses could reach the solar wind with sufficient energy to generate switchbacks, even though some of then are expected to be reflected back in the stratified solar atmosphere. Alfvenic pulses further reproduce for the first time other properties of switchbacks, including the filling factor above ~8% at granular and supergranular scales, which correspond best to the lower end of the mesoscale structure. This quantitative result for solar energy output in the form of Alfvenic pulses through magnetic funnels provides a crucial clue to the ongoing debate about the dynamic cycle of energy exchange between the Sun and the mesoscale solar wind that has been raised, but has not been adequately addressed, by PSP near-Sun observations.

astro-ph.SR↗

arXiv2025

X Hacking: The Threat of Misguided AutoML

Explainable AI (XAI) and interpretable machine learning methods help to build trust in model predictions and derived insights, yet also present a perverse incentive for analysts to manipulate XAI metrics to support pre-specified conclusions. This paper introduces the concept of X-hacking, a form of p-hacking applied to XAI metrics such as SHAP values. We show how easily an automated machine learning pipeline can be adapted to exploit model multiplicity at scale: searching a Rashomon set of 'defensible' models with similar predictive performance to find a desired explanation. We formulate the trade-off between explanation and accuracy as a multi-objective optimisation problem, and illustrate empirically on familiar real-world datasets that, on average, Bayesian optimisation accelerates X-hacking 3-fold for features susceptible to it, versus random sampling. We show the vulnerability of a dataset to X-hacking can be determined by information redundancy among features. Finally, we suggest possible methods for detection and prevention, and discuss ethical implications for the credibility and reproducibility of XAI.

cs.LG↗

arXiv2025

RE-IMAGINE: Symbolic Benchmark Synthesis for Reasoning Evaluation

Recent Large Language Models (LLMs) have reported high accuracy on reasoning benchmarks. However, it is still unclear whether the observed results arise from true reasoning or from statistical recall of the training set. Inspired by the ladder of causation (Pearl, 2009) and its three levels (associations, interventions and counterfactuals), this paper introduces RE-IMAGINE, a framework to characterize a hierarchy of reasoning ability in LLMs, alongside an automated pipeline to generate problem variations at different levels of the hierarchy. By altering problems in an intermediate symbolic representation, RE-IMAGINE generates arbitrarily many problems that are not solvable using memorization alone. Moreover, the framework is general and can work across reasoning domains, including math, code, and logic. We demonstrate our framework on four widely-used benchmarks to evaluate several families of LLMs, and observe reductions in performance when the models are queried with problem variations. These assessments indicate a degree of reliance on statistical recall for past performance, and open the door to further research targeting skills across the reasoning hierarchy.

cs.CL↗

arXiv2025

Investigating the effects of acceptor removal mechanism and impact ionization on proton irradiated 300 $μ$m thick LGAD

Low-Gain Avalanche Detectors (LGADs) are the leading 4D sensing technology selected for use in the High Luminosity Large Hadron Collider (HL-LHC). However, their proximity to the interaction point makes them highly susceptible to radiation-induced damage. Such degradation effects can be effectively studied through TCAD simulations. In this work, we extend the validation of a previously developed proton damage model for transitional sensors. The enhanced model for LGAD also incorporates an acceptor removal mechanism and modifications in impact ionization behavior, resulting in a more comprehensive and reliable tool for fabrication and performance analysis.

physics.ins-det↗

arXiv2025

Intriguing nature of AM Her type candidate CXOU J204734.8+300105

The detection and characterization of periodic X-ray signals are crucial for identifying new compact objects and studying the mechanisms powering their emission. We report on the timing and spectral variability of CXOU J204734.8+300105, a proposed eclipsing polar-type cataclysmic variable (CV) candidate. This source has been observed once with Chandra and twice with XMM-Newton, revealing several intriguing and conflicting features in its X-ray emission. The Chandra observation showed a periodicity of $\sim$6000 s with an eclipse-like feature. The X-ray light curve from 2017 XMM-Newton observation showed a period of $\sim$2000 seconds without any apparent eclipse, while the simultaneous optical light curve from OM showed a period of $\sim$6000 seconds. This variability raises questions about the true nature of the source. Spectral analysis indicates a multi-component emission and emission lines due to Fe. The spectral characteristics are consistent with those observed in other CV systems. Additionally, we identified optical and near-infrared counterparts from various catalogues. Our findings suggest a dynamic and evolving accretion environment of CXOU J204734.8+300105.

astro-ph.HE↗

arXiv2025

DeduCE: Deductive Consistency as a Framework to Evaluate LLM Reasoning

Despite great performance on Olympiad-level reasoning problems, frontier large language models can still struggle on high school math when presented with novel problems outside standard benchmarks. Going beyond final accuracy, we propose a deductive consistency metric to analyze chain-of-thought output from language models (LMs).Formally, deductive reasoning involves two subtasks: understanding a set of input premises and inferring the conclusions that follow from them. The proposed metric studies LMs' performance on these subtasks, with the goal of explaining LMs' reasoning errors on novel problems: how well do LMs understand input premises with increasing context lengths, and how well can they infer conclusions over multiple reasoning hops? Since existing benchmarks may be memorized, we develop a pipeline to evaluate LMs' deductive consistency on novel, perturbed versions of benchmark problems. On novel grade school math problems (GSM-8k), we find that LMs are fairly robust to increasing number of input premises, but suffer significant accuracy decay as the number of reasoning hops is increased. Interestingly, these errors are masked in the original benchmark as all models achieve near 100% accuracy. As we increase the number of solution steps using a synthetic dataset, prediction over multiple hops still remains the major source of error compared to understanding input premises. Other factors, such as shifts in language style or natural propagation of early errors do not explain the trends. Our analysis provides a new view to characterize LM reasoning -- as computations over a window of input premises and reasoning hops -- that can provide unified evaluation across problem domains.

cs.CL↗

arXiv2025

Sidebands to mHz QPOs in 4U 1626$-$67 in the second spin-down state

We report results from an $AstroSat$ Target-of-Opportunity (ToO) observation of 4U 1626$-$67, performed on 2023 May 18, soon after the discovery of torque reversal to spin-down in the source. The X-ray emission exhibited significant dependence on both energy and torque state. This work highlights the comparison of timing features of 4U 1626$-$67 with a previous $AstroSat$ observation from 2018, when the neutron star was in the spin-up state. The power density spectrum (PDS) of the 2023 observation comprised a sharp peak corresponding to $ν_{\rm NS}\sim$130 mHz X-ray pulsations along with a prominent quasi-periodic oscillation (QPO) feature at $ν_{\rm QPO}\sim$46 mHz with $\sim$20\% rms amplitude, which was positively correlated with energy. We also report the detection of sidebands to QPO occurring at a beat frequency ($ν_{\rm NS}-ν_{\rm QPO}$) of $\sim$83 mHz with $\sim$8\% rms amplitude, having $>3σ$ detection significance. Additionally, we utilized $Nuclear ~Spectroscopic ~Telescope ~ARray$ ($NuSTAR$) observations from the same torque state (2023 May-July) to analogize the presence and energy dependence of sidebands. The source retains timing properties in this spin-down torque state, similar to those seen in the previous spin-down phase. In sharp contrast, PDS from the 2018 observation was dominated by red noise, an absence of QPOs and a broadening in the wings of the pulse frequency peak, indicating a coupling between periodic and low-frequency aperiodic variability. Furthermore, we detected the known cyclotron resonance scattering feature (CRSF) at 37 keV in the Large Area X-ray Proportional Counter (LAXPC) spectrum. We explore various mechanisms that could possibly explain the presence of QPOs exclusively during the spin-down state.

astro-ph.HE↗

arXiv2024

Probing the energy and luminosity-dependent spectro-timing properties of RX J0440.9+4431 with AstroSat

The Be/X-ray binary pulsar RX J0440.9+4431 went through a giant outburst in December 2022 with a peak flux of $\sim$2.3 Crab in 15--50 keV. We studied the broad-band timing and spectral properties of RX J0440.9+4431 using four $AstroSat$ observations, where the source transited between subcritical and supercritical accretion regimes. Pulsations were detected significantly above 100 keV. The pulse profiles were found to be highly luminosity- and energy-dependent. A significant evolution in the pulse profile shape near the peak of the outburst indicates a possible change in the accretion mode and beaming patterns of RX J0440.9+4431. The rms pulsed fraction was luminosity- and energy-dependent, with a concave-like feature around 20--30 keV. The depth of this feature varied with luminosity, indicating changes in the accretion column height and proportion of reflected photons. The broad-band continuum spectra were best fitted with a two-component Comptonization model with a blackbody component or a two-blackbody component model with a thermal Comptonization component. A quasi-periodic oscillation at 60 mHz was detected at a luminosity of $2.6 \times 10^{37}$ erg s$^{-1}$, which evolved into 42 mHz at $1.5 \times 10^{37}$ erg s$^{-1}$. The QPO rms were found to be energy dependent with an overall increasing trend with energy. For the first time, we found the QPO frequency varying with photon energy in an X-ray pulsar, which poses a challenge in explaining the QPO with current models such as the Keplarian and beat frequency model. Hence, more physically motivated models are required to understand the physical mechanism behind the mHz QPOs.

astro-ph.HE↗

arXiv2024

A Prototype Model of Zero-Trust Architecture Blockchain with EigenTrust-Based Practical Byzantine Fault Tolerance Protocol to Manage Decentralized Clinical Trials

The COVID-19 pandemic necessitated the emergence of decentralized Clinical Trials (DCTs) due to patient retention, accelerate trials, improve data accessibility, enable virtual care, and facilitate seamless communication through integrated systems. However, integrating systems in DCTs exposes clinical data to potential security threats, making them susceptible to theft at any stage, a high risk of protocol deviations, and monitoring issues. To mitigate these challenges, blockchain technology serves as a secure framework, acting as a decentralized ledger, creating an immutable environment by establishing a zero-trust architecture, where data are deemed untrusted until verified. In combination with Internet of Things (IoT)-enabled wearable devices, blockchain secures the transfer of clinical trial data on private blockchains during DCT automation and operations. This paper proposes a prototype model of the Zero-Trust Architecture Blockchain (z-TAB) to integrate patient-generated clinical trial data during DCT operation management. The EigenTrust-based Practical Byzantine Fault Tolerance (T-PBFT) algorithm has been incorporated as a consensus protocol, leveraging Hyperledger Fabric. Furthermore, the Internet of Things (IoT) has been integrated to streamline data processing among stakeholders within the blockchain platforms. Rigorous evaluation has been done to evaluate the quality of the system.

cs.CR↗

arXiv2024

Physics-Informed Machine Learning for Smart Additive Manufacturing

Compared to physics-based computational manufacturing, data-driven models such as machine learning (ML) are alternative approaches to achieve smart manufacturing. However, the data-driven ML's "black box" nature has presented a challenge to interpreting its outcomes. On the other hand, governing physical laws are not effectively utilized to develop data-efficient ML algorithms. To leverage the advantages of ML and physical laws of advanced manufacturing, this paper focuses on the development of a physics-informed machine learning (PIML) model by integrating neural networks and physical laws to improve model accuracy, transparency, and generalization with case studies in laser metal deposition (LMD).

cs.LG↗

arXiv2024

TRUCE: Private Benchmarking to Prevent Contamination and Improve Comparative Evaluation of LLMs

Benchmarking is the de-facto standard for evaluating LLMs, due to its speed, replicability and low cost. However, recent work has pointed out that the majority of the open source benchmarks available today have been contaminated or leaked into LLMs, meaning that LLMs have access to test data during pretraining and/or fine-tuning. This raises serious concerns about the validity of benchmarking studies conducted so far and the future of evaluation using benchmarks. To solve this problem, we propose Private Benchmarking, a solution where test datasets are kept private and models are evaluated without revealing the test data to the model. We describe various scenarios (depending on the trust placed on model owners or dataset owners), and present solutions to avoid data contamination using private benchmarking. For scenarios where the model weights need to be kept private, we describe solutions from confidential computing and cryptography that can aid in private benchmarking. We build an end-to-end system, TRUCE, that enables such private benchmarking showing that the overheads introduced to protect models and benchmark are negligible (in the case of confidential computing) and tractable (when cryptographic security is required). Finally, we also discuss solutions to the problem of benchmark dataset auditing, to ensure that private benchmarks are of sufficiently high quality.

cs.CR↗