arXiv subjects

Sandip Roy

Publications and source records attributed to Sandip Roy.

At least 19 recordsLinked to original sources

arXiv2026

Strongest constraints on dark acoustic oscillations from the Lyman-alpha forest

We set the first constraints on a small-scale dark acoustic oscillation (DAO) in the linear matter power spectrum arising from dark sector interactions, with a full forward model of the Ly-$\alpha$ forest. No more than 30\% of dark matter can form DAOs if they peak at wavenumbers $< 50\,h\,\mathrm{Mpc}^{-1}$ (95\% c.l.), probing scales $25 \times$ smaller than the cosmic microwave background (CMB). Given the complex covariance of DAO and nuisance parameters, we use a deep kernel learning emulator of hydrodynamical simulations to capture imprints of linear oscillations in the Ly-$\alpha$ forest.

astro-ph.CO↗

arXiv2026

Semiflexible Ring Polymers on Active Motor Beds: Nonequilibrium Dynamics and Conformations

A semiflexible ring polymer on a motor-protein bed exhibits activity- and processivity-dependent rotational and conformational dynamics that are not captured by linear-chain behavior. Using coarse-grained Langevin simulations with bending elasticity, excluded-volume interactions, and stochastic motor attachment, stepping, and detachment, we vary activity (Peclet number), motor processivity, and chain stiffness to map the nonequilibrium response. The mean-squared displacement shows crossover dynamics, with semiflexible rings displaying subdiffusive-to-diffusive behavior at low activity and an intermediate ballistic regime at higher activity, while increasing flexibility shifts the short-time response toward a Rouse-like limit. Diameter autocorrelations exhibit damped oscillations associated with coherent rotation; the rotational frequency increases with activity and processivity, whereas the decorrelation time is non-monotonic at high processivity. Fourier mode analysis identifies competition between the radius (k=0) and elliptic (k=2) modes as the origin of the non-monotonic asphericity.

cond-mat.soft↗

arXiv2026

Averaging principle for a slow-fast stochastic nonlinear fractional Schr\"odinger equation

We establish an averaging principle for a structural multiscale stochastic nonlinear fractional Schr\"odinger system on the one-dimensional torus driven by a multiplicative Wiener noise. The slow component is governed by a fractional Schr\"odinger operator with a general polynomial nonlinearity, while the fast component evolves on a shorter time scale and exhibits dissipative diffusion, nonlinear interactions, and stochastic forcing. Under suitable dissipative assumptions, we have shown that, as the scale separation parameter tends to zero, the slow component converges strongly to an effective stochastic fractional Schr\"odinger equation. The effective drift is obtained by averaging the coupling term with respect to the unique invariant measure of the frozen fast dynamics. The proof relies on uniform a priori estimates, ergodicity of the fast equation, H\"older time regularity of the slow component obtained via a vanishing viscosity method, and a Khasminskii-type time discretization argument adapted to fractional dispersive operators. The analysis is technically challenging due to limited smoothing of the fractional Schr\"odinger semigroup and the presence of general polynomial nonlinearities, which are handled through refined estimates and viscosity approximation.

math.AP↗

arXiv2026

Probing Atomic Dark Matter with Stellar Streams in Milky Way-Mass Galaxies

We present the first detailed analysis of the effects of dissipative dark matter on stellar streams. As a concrete example, we generate a cosmological hydrodynamic zoom-in simulation of a Milky Way-mass galaxy, assuming that the dark matter consists of Cold Dark Matter (CDM) with a sub-component ($\sim6\%$) of Atomic Dark Matter (ADM). The ADM subcomponent behaves as collisional, efficiently dissipative gas and allows for the formation of dense compact objects that enhance the central density of satellite galaxies, making them more resistant to tidal disruption. We show that stellar streams with stellar mass $M_{\rm{tot}, \star} \gtrsim 10^{5.5} \ \text{M}_\odot$ form later and exhibit prolonged star formation throughout their evolution, as compared to their CDM counterparts. Changes to star formation history are reflected on the chemical tracks of the stellar stream stars, where the youngest have enhanced [Fe/H] and [Mg/Fe] in the presence of ADM. Furthermore, a population of low-mass satellites with high ADM mass fractions is identified at low pericenter distances, which may affect the population of streams at $M_{\rm{tot}, \star} \lesssim 10^{5.5} \ \text{M}_\odot$. The results of this study should generalize to other dark matter models that lead to inner-density enhancements in satellites, such as elastic self-interacting dark matter in the gravothermal collapse regime.

astro-ph.GA↗

arXiv2025

Constraining Dark Acoustic Oscillations with the High-Redshift UV Luminosity Function

Dark acoustic oscillations (DAOs) in the matter power spectrum can arise in many different dark sector models, and can imprint on a variety of cosmological observables. In this work we use measurements of the galactic UV luminosity function (UVLF) at high redshifts to constrain the dark acoustic oscillation feature at small scales in a model-agnostic way. We introduce a phenomenological transfer function model for a dark sector with a species undergoing DAOs which can accommodate sub-dominant dark matter abundances, and obtain constraints on its parameters. In order to predict the UVLF, we employ an Extended Press-Schechter formalism which we calibrate using N-body simulations with initial conditions featuring DAOs. Using measurements from the Hubble Space Telescope, James Webb Space Telescope, Subaru Telescope, and Canada-France-Hawaii Telescope, we constrain the wave number of the first DAO peak to be at $k \gtrsim 50\ h/\mathrm{Mpc}$, unless the fraction of dark matter undergoing DAOs is less than $0.07$.

astro-ph.CO↗

arXiv2025

The DREAMS Project: Disentangling the Impact of Halo-to-Halo Variance and Baryonic Feedback on Milky Way Satellite Galaxies

We analyze the properties of satellite galaxies around 1,024 Milky Way-mass hosts from the DREAMS Project, simulated within a $\Lambda$CDM cosmology. Utilizing the TNG galaxy-formation model, the DREAMS simulations incorporate both baryonic physics and cosmological uncertainties for a large sample of galaxies with diverse environments and formation histories. We investigate the relative impact of the physical uncertainty from the galaxy-formation model on predicted satellite properties using four metrics: the satellite stellar mass function, radial distribution, inner slope of dark matter density profile, and stellar half-light radius. We compare these predictions to observations from the SAGA Survey and the DREAMS N-body simulations and find that uncertainties from baryonic physics modeling are subdominant to the scatter arising from halo-to-halo variance. Where baryonic modeling does affect satellites, the supernova wind energy has the largest effect on the satellite properties that we investigate. Specifically, increased supernova wind energy suppresses the stellar mass of satellites and results in more extended stellar half-light radii. The adopted wind speed has only a minor impact, and other astrophysical and cosmological parameters show no measurable effect. Our findings highlight the robustness of satellite properties against uncertainties in baryonic physics modeling.

astro-ph.GA↗

arXiv2025

The DREAMS Project: A New Suite of 1,024 Simulations to Contextualize the Milky Way and Assess Physics Uncertainties

We introduce a new suite of 1,024 cosmological and hydrodynamical zoom-in simulations of Milky Way-mass halos, run with Cold Dark Matter, as part of the DREAMS Project. Each simulation in the suite has a unique set of initial conditions and combination of cosmological and astrophysical parameters. The suite is designed to quantify theoretical uncertainties from halo-to-halo variance, as well as stellar and black hole feedback. We develop a novel weighting scheme that prioritizes regions of the input parameter space, yielding galaxies consistent with the observed present-day stellar mass--halo mass relation. The resulting galaxy population exhibits a wide diversity in structural properties that encompasses those of the actual Milky Way, providing a powerful statistical sample for galactic archaeology. To demonstrate the suite's scientific utility, we investigate the connection between a galaxy's merger history, focusing on Gaia-Sausage-Enceladus~(GSE) analogs, and its present-day properties. We find that galaxies with a GSE analog have lower star formation rates, more compact disks, and more spherical stellar halos. Crucially, significant halo-to-halo scatter remains, demonstrating that matching more than the most significant events in the Milky Way's past is necessary to recover its present-day properties. Our results highlight the necessity for large statistical samples to disentangle the stochastic nature of galaxy formation and robustly model the Milky Way's unique history.

astro-ph.GA↗

arXiv2025

Inertia-chirality interplay in active Brownian motion: exact dynamics and phase maps

We present an exact, time-resolved theory for a two-dimensional chiral active Brownian particle (cABP) with translational inertia. Using a Laplace-transform moment hierarchy, we derive closed-form expressions for the mean velocity, velocity-orientation projections, velocity autocorrelation, mean-squared velocity, mean-squared displacement, and the fourth moment of velocity. These results agree quantitatively with simulations over all masses, activities, and chiralities. We show that the velocity autocorrelation factorizes into an inertial envelope and a chiral envelope. Despite rich transients in the velocity sector, the long-time positional diffusion equals the overdamped cABP value, independent of mass. From the steady mean-squared velocity, we define a kinetic temperature and a modified fluctuation-dissipation relation whose violation vanishes in two limits: large mass or large chirality, identifying chirality as an additional route to equilibrium-like behavior. The steady-state velocity excess kurtosis gives a phase map that exhibits a (Gaussian-like)-active(bimodal)-(Gaussian-like) re-entrance with mass; chirality confines activity and shrinks the active sector. A narrow positive-kurtosis window emerges at large mass and intermediate chirality, with analytic boundaries consistent with the heavy-mass asymptote.

cond-mat.stat-mech↗

arXiv2025

Cooperative Local Differential Privacy: Securing Time Series Data in Distributed Environments

The rapid growth of smart devices such as phones, wearables, IoT sensors, and connected vehicles has led to an explosion of continuous time series data that offers valuable insights in healthcare, transportation, and more. However, this surge raises significant privacy concerns, as sensitive patterns can reveal personal details. While traditional differential privacy (DP) relies on trusted servers, local differential privacy (LDP) enables users to perturb their own data. However, traditional LDP methods perturb time series data by adding user-specific noise but exhibit vulnerabilities. For instance, noise applied within fixed time windows can be canceled during aggregation (e.g., averaging), enabling adversaries to infer individual statistics over time, thereby eroding privacy guarantees. To address these issues, we introduce a Cooperative Local Differential Privacy (CLDP) mechanism that enhances privacy by distributing noise vectors across multiple users. In our approach, noise is collaboratively generated and assigned so that when all users' perturbed data is aggregated, the noise cancels out preserving overall statistical properties while protecting individual privacy. This cooperative strategy not only counters vulnerabilities inherent in time-window-based methods but also scales effectively for large, real-time datasets, striking a better balance between data utility and privacy in multiuser environments.

cs.CR↗

arXiv2025

Large deviation principle for a stochastic nonlinear damped Schrodinger equation

The present paper focuses on the stochastic nonlinear Schrodinger equation with polynomial nonlinearity, and a zero-order (no derivatives involved) linear damping. Here, the random forcing term appears as a mix of a nonlinear noise in the Ito sense and a linear multiplicative noise in the Stratonovich sense. We prove the Laplace principle for the family of solutions to the stochastic system in a suitable Polish space, using the weak convergence framework of Budhiraja and Dupuis. This analysis is nontrivial, since it requires uniform estimates for the solutions of the associated controlled stochastic equation in the underlying solution space in order to verify the weak convergence criterion. The Wentzell Freidlin type large deviation principle is proved using Varadhan's lemma and Bryc's converse to Varadhan's lemma. The local well-posedness of the skeleton equation (deterministic controlled system) is established by employing the Banach fixed point theorem, and the global well posedness is established via Yosida approximation. We show that the conservation law holds in the absence of the linear damping and Ito noise. The well posedness of the stochastic controlled equation is also nontrivial in this case. We use a truncation method, a stopping time argument, and the Yosida technique to get the global well-posedness of the stochastic controlled equation.

math.PR↗

arXiv2025

From spirals to flagellar beating: How pivot-like defects control semiflexible filament dynamics in motility assays

We demonstrate that internal pivot-like defects, arising from rigor mutant motor proteins that bind without stepping, fundamentally reshape the dynamics of semiflexible filaments in two-dimensional motility assays. Using large-scale numerical simulations, we show that such internal pivots establish a previously unrecognized boundary condition, intermediate between free and clamped filaments, that decisively governs filament behavior. Strikingly, by tuning the pivot position, motor activity, and processivity, filaments undergo sharp transitions from tightly wound spiral states to extended, flagella-like beating. Spiral formation is stabilized by a balance between motor-driven forces and bending rigidity, with intermediate stiffness yielding the most robust spirals. Unlike generic active polymer models, our framework isolates the distinct role of rigor-bound motor proteins, revealing how they function as internal control elements governing the transition between spiral and flagellar dynamics. This minimal yet physically grounded model yields experimentally testable predictions and reveals how localized defects can act as key regulators of cytoskeletal organization and dynamics.

physics.bio-ph↗

arXiv2025

Unconditionally Secure Wireless-Wired Ground-Satellite-Ground Communication Networks Utilizing Classical and Quantum Noise

In this paper, we introduce the Kirchhoff-Law-Johnson-Noise (KLJN) as an approach to securing satellite communications. KLJN has the potential to revolutionize satellite communication security through its combination of simplicity, cost-effectiveness, and resilience with unconditional security. Unlike quantum key distribution (QKD), which requires complex, fragile, and expensive infrastructure like photon detectors and dedicated optical links, KLJN operates using standard electronic components and wires, significantly reducing implementation costs and logistical hurdles. KLJN's security, grounded in the fundamental laws of classical physics, is impervious to environmental and radiation-induced noise, making it highly reliable in the harsh conditions of satellite communications. This robustness, coupled with its ability to integrate seamlessly with existing infrastructure, positions KLJN as a revolutionary alternative to quantum solutions for ensuring secure, resilient satellite communications. The authors explore the value of achieving unconditionally secure communications in strategic ground-to-satellite networks which address vulnerabilities posed by advanced computational threats, including quantum computing. Our team has examined two leading approaches to unconditional security - the KLJN scheme and QKD - and analyzed the potential use of each for space systems. While QKD leverages quantum mechanics for security, it faces challenges related to cost, complexity, and environmental sensitivity. In contrast, the KLJN scheme utilizes classical physics principles to provide a simpler, more cost-effective, and resilient alternative, particularly for ground-based systems. The study concludes that KLJN offers significant advantages in simplicity, cost-efficiency, and robustness, making it a practical choice for many secure communication applications.

cs.CR↗

arXiv2025

Searching for Axion Dark Matter Near Relaxing Magnetars

Axion dark matter passing through the magnetospheres of magnetars can undergo hyper-efficient resonant mixing with low-energy photons, leading to the production of narrow spectral lines that could be detectable on Earth. Since this is a resonant process triggered by the spatial variation in the photon dispersion relation, the luminosity and spectral properties of the emission are highly sensitive to the charge and current densities permeating the magnetosphere. To date, a majority of the studies investigating this phenomenon have assumed a perfectly dipolar magnetic field structure with a near-field plasma distribution fixed to the minimal charge-separated force-free configuration. While this {may} be a reasonable treatment for the closed field lines of conventional radio pulsars, the strong magnetic fields around magnetars are believed to host processes that drive strong deviations from this minimal configuration. In this work, we study how realistic magnetar magnetospheres impact the electromagnetic emission produced from axion dark matter. Specifically, we construct charge and current distributions that are consistent with magnetar observations, and use these to recompute the prospective sensitivity of radio and sub-mm telescopes to axion dark matter. We demonstrate that the two leading models yield vastly different predictions for the frequency and amplitude of the spectral line, indicating systematic uncertainties in the plasma structure are significant. Finally, we discuss various observational signatures that can be used to differentiate the local plasma loading mechanism of an individual magnetar, which will be necessary if there is hope of using such objects to search for axions.

hep-ph↗

arXiv2025

RADEP: A Resilient Adaptive Defense Framework Against Model Extraction Attacks

Machine Learning as a Service (MLaaS) enables users to leverage powerful machine learning models through cloud-based APIs, offering scalability and ease of deployment. However, these services are vulnerable to model extraction attacks, where adversaries repeatedly query the application programming interface (API) to reconstruct a functionally similar model, compromising intellectual property and security. Despite various defense strategies being proposed, many suffer from high computational costs, limited adaptability to evolving attack techniques, and a reduction in performance for legitimate users. In this paper, we introduce a Resilient Adaptive Defense Framework for Model Extraction Attack Protection (RADEP), a multifaceted defense framework designed to counteract model extraction attacks through a multi-layered security approach. RADEP employs progressive adversarial training to enhance model resilience against extraction attempts. Malicious query detection is achieved through a combination of uncertainty quantification and behavioral pattern analysis, effectively identifying adversarial queries. Furthermore, we develop an adaptive response mechanism that dynamically modifies query outputs based on their suspicion scores, reducing the utility of stolen models. Finally, ownership verification is enforced through embedded watermarking and backdoor triggers, enabling reliable identification of unauthorized model use. Experimental evaluations demonstrate that RADEP significantly reduces extraction success rates while maintaining high detection accuracy with minimal impact on legitimate queries. Extensive experiments show that RADEP effectively defends against model extraction attacks and remains resilient even against adaptive adversaries, making it a reliable security framework for MLaaS models.

cs.CR↗

arXiv2025

Evaluating Query Efficiency and Accuracy of Transfer Learning-based Model Extraction Attack in Federated Learning

Federated Learning (FL) is a collaborative learning framework designed to protect client data, yet it remains highly vulnerable to Intellectual Property (IP) threats. Model extraction (ME) attacks pose a significant risk to Machine Learning as a Service (MLaaS) platforms, enabling attackers to replicate confidential models by querying black-box (without internal insight) APIs. Despite FL's privacy-preserving goals, its distributed nature makes it particularly susceptible to such attacks. This paper examines the vulnerability of FL-based victim models to two types of model extraction attacks. For various federated clients built under the NVFlare platform, we implemented ME attacks across two deep learning architectures and three image datasets. We evaluate the proposed ME attack performance using various metrics, including accuracy, fidelity, and KL divergence. The experiments show that for different FL clients, the accuracy and fidelity of the extracted model are closely related to the size of the attack query set. Additionally, we explore a transfer learning based approach where pretrained models serve as the starting point for the extraction process. The results indicate that the accuracy and fidelity of the fine-tuned pretrained extraction models are notably higher, particularly with smaller query sets, highlighting potential advantages for attackers.

cs.CR↗

arXiv2025

RESTRAIN: Reinforcement Learning-Based Secure Framework for Trigger-Action IoT Environment

Internet of Things (IoT) platforms with trigger-action capability allow event conditions to trigger actions in IoT devices autonomously by creating a chain of interactions. Adversaries exploit this chain of interactions to maliciously inject fake event conditions into IoT hubs, triggering unauthorized actions on target IoT devices to implement remote injection attacks. Existing defense mechanisms focus mainly on the verification of event transactions using physical event fingerprints to enforce the security policies to block unsafe event transactions. These approaches are designed to provide offline defense against injection attacks. The state-of-the-art online defense mechanisms offer real-time defense, but extensive reliability on the inference of attack impacts on the IoT network limits the generalization capability of these approaches. In this paper, we propose a platform-independent multi-agent online defense system, namely RESTRAIN, to counter remote injection attacks at runtime. RESTRAIN allows the defense agent to profile attack actions at runtime and leverages reinforcement learning to optimize a defense policy that complies with the security requirements of the IoT network. The experimental results show that the defense agent effectively takes real-time defense actions against complex and dynamic remote injection attacks and maximizes the security gain with minimal computational overhead.

cs.CR↗

arXiv2025

Confinement and Activity-Driven Dynamics of Semiflexible Polymers in Motility Assays

We investigate the nonequilibrium dynamics of semiflexible polymers driven by motor proteins (MPs) in two-dimensional motility assays under harmonic confinement. Using a coarse-grained agent-based model that incorporates stochastic motor attachment, detachment, and force generation, we study how activity, filament rigidity, and confinement interact to control polymer behavior. We construct dynamical behavior maps as a function of P\'eclet number, motor processivity, and trap strength. We find a two-state transition from a trapped to a free polymer, with an intermediate coexistence region, and obtain a scaling relation for the critical P\'eclet number, which is supported by simulation data across a range of parameters. Polymer flexibility strongly influences confinement: flexible filaments are more easily trapped, while increasing rigidity destabilizes confinement. Processivity of MPs can also induce a change in the effective rigidity of the polymer and, therefore, influence confinement by the trap. Under moderate confinement and activity, we observe the emergence of stable spiral conformations. The center-of-mass dynamics is analyzed through the mean square displacement, showing diffusive, ballistic, and diffusive regimes that depend on the trap strength and activity. Additionally, time series analysis of the excess kurtosis shows the variation of the non-Gaussian fluctuations with trap strength and activity. Our results provide a minimal physical framework to understand the dynamic organization of active filaments under confinement, with relevance to in vitro motility assays, cytoskeletal filament manipulation by optical traps, and synthetic active polymer systems.

cond-mat.soft↗

arXiv2024

Privacy Drift: Evolving Privacy Concerns in Incremental Learning

In the evolving landscape of machine learning (ML), Federated Learning (FL) presents a paradigm shift towards decentralized model training while preserving user data privacy. This paper introduces the concept of ``privacy drift", an innovative framework that parallels the well-known phenomenon of concept drift. While concept drift addresses the variability in model accuracy over time due to changes in the data, privacy drift encapsulates the variation in the leakage of private information as models undergo incremental training. By defining and examining privacy drift, this study aims to unveil the nuanced relationship between the evolution of model performance and the integrity of data privacy. Through rigorous experimentation, we investigate the dynamics of privacy drift in FL systems, focusing on how model updates and data distribution shifts influence the susceptibility of models to privacy attacks, such as membership inference attacks (MIA). Our results highlight a complex interplay between model accuracy and privacy safeguards, revealing that enhancements in model performance can lead to increased privacy risks. We provide empirical evidence from experiments on customized datasets derived from CIFAR-100 (Canadian Institute for Advanced Research, 100 classes), showcasing the impact of data and concept drift on privacy. This work lays the groundwork for future research on privacy-aware machine learning, aiming to achieve a delicate balance between model accuracy and data privacy in decentralized environments.

cs.LG↗