arXiv subjects

Anirban Bhattacharjee

Publications and source records attributed to Anirban Bhattacharjee.

At least 19 recordsLinked to original sources

arXiv2026

Dynamics of finger-type convection in double-diffusive instability

Finger-type convection in double-diffusive instability (DDI) controls mixing and scalar transport in many stratified flows, yet a quantitative, finger-resolved description of the transient growth, transport, and saturation pathways has been limited. Here, finger-type DDI is analyzed in a sealed-surface laboratory facility using synchronized planar laser-induced fluorescence (PLIF) and particle image velocimetry (PIV) at fixed thermal contrast $ΔT=5^\circ$C and three salinity contrasts, $ΔS=350$, 450, and 550 ppm, complemented by a matched high-resolution three-dimensional DNS. A systematic fingertip detection and tracking framework generates ensemble growth curves. Fingertip growth follows a sequence of three stages (acceleration, quasi-steady propagation, and decay). The peak growth rates increase monotonically with $ΔS$, and nondimensional fingertip-height histories collapse onto a common trend. The peak growth rates are reproduced by DNS and agree with linear stability analysis, establishing experiment--DNS--theory consistency in the intermediate regime. The mixed-material area increases with time, initially following a common nondimensional trend before transitioning to $ΔS$-dependent interaction and breakdown. Finger-scale measurements reveal the formation of a symmetric vortex ring at the fingertips for $ΔS=450$ ppm, inducing vertical-aligned transport. At $ΔS=550$ ppm the roll-up becomes asymmetric: stronger buoyancy amplifies shear, destabilizes the vortex ring, and produces a zig-zag/lateral-drift mode that enhances the lateral transport. Finally, the evolution of the buoyancy anomaly links the growth-rate phases to a time-dependent force balance in which increasing buoyancy drives acceleration, shear-induced resistance regulates quasi-steady propagation, and dilution with top-boundary influence yields late-stage fingertip deceleration.

physics.flu-dyn↗

arXiv2026

A Compact Broadband Purcell Filter for Superconducting Quantum Circuits in a 3D Flip-Chip Architecture

Fast and high-fidelity qubit readout requires strong coupling between the readout resonator and the feedline. However, such coupling unavoidably enhances qubit decay through the Purcell effect. We present a four-pole broadband Purcell filter implemented on a 3D flip-chip platform to overcome this trade-off. The filter provides a flat 1 GHz passband centered at 7.68 GHz and achieves more than 45 dB suppression at typical qubit frequencies. We demonstrate the filter's compatibility with multiplexed readout using a test chip that integrates six floating readout resonators strongly coupled within the passband. The chip is fabricated using a 150 nm Niobium (Nb) thin-film process and characterized at 20 mK in a cryogenic measurement setup. We also develop an analytical model that accurately captures the filter response and determines the resonance frequencies and external quality factors of the floating resonators directly from their physical geometry, enabling rapid circuit synthesis and design optimization. The proposed design is compact and fabrication-tolerant, making it a practical solution for large-scale superconducting quantum processors.

quant-ph↗

arXiv2026

Learning subgrid interfacial area in two-phase flows with regime-dependent inductive biases

The reliability of machine learning in multiscale physical systems depends on how physical structure is embedded into the learning process. We investigate this in the context of turbulent multiphase flows, focusing on the prediction of subgrid interfacial area density, a key quantity governing interphase transport that remains unresolved in large-eddy simulations. In this work, we develop and evaluate two machine learning subgrid closure models to predict the three-dimensional subgrid interfacial area density: a purely data-driven 3D encoder-decoder network, and a physics-constrained variant regularized by a fractal geometric prior. Across a range of Weber numbers, the physics-based model improves predictive accuracy, reduces error variance, and suppresses nonphysical artifacts relative to purely data-driven approaches. We also show that these gains are regime-dependent: the embedded inductive bias enhances generalization in corrugation-dominated regimes where its underlying assumptions hold, but becomes ineffective in fragmentation-dominated regimes characterized by topology change and droplet breakup. These results reveal a broader principle for scientific machine learning: the utility of physics-informed models depends not only on the presence of inductive bias, but on its alignment with the governing physical regime. This suggests a path toward regime-aware learning frameworks for modeling of complex multiscale systems.

physics.comp-ph↗

arXiv2025

Kerr non-linearity enhances the response of a graphene Josephson bolometer

Highly sensitive, broadband bolometers are of great interest because of their versatile usage in wide areas starting from dark matter search, radio astronomy, material science, and qubit readouts in cQED experiments. There have been different realizations of bolometers using superconducting thin films, nanowires, quantum dots, and various 2D materials in the recent past. The challenge is to have a single device that combines high sensitivity, broad bandwidth, a fast readout mechanism, and low noise. Here we demonstrate the first usage of a Josephson parametric amplifier (JPA) as a highly sensitive bolometer. Our key finding is the Kerr non-linearity of the JPA boosts the device's sensitivity. When the bolometer is biased in the non-linear regime, it enhances the sideband signals (~100 times), resulting in an order of magnitude improvement in sensitivity compared to the linear regime. In the non-linear biasing of the device, we achieve a NEP~500 aW/sqrt(Hz). Our bolometer offers a fast detection scheme with a thermal time constant of 4.26 us and an intrinsic JPA time constant of 70 ns. Our device's broadband and fast operation are key and new compared to previously studied graphene-based bolometers. In our device, the gate voltage tunability and the possibility of multiplexing combined with the sensitive bolometric performance offer an opportunity for integrated quantum sensor arrays. Our work demonstrates a way forward to enhance the performance of quantum sensors based on 2D materials by leveraging the inherent non-linear response.

cond-mat.mes-hall↗

arXiv2024

Superconducting Cavity-Based Sensing of Band Gaps in 2D Materials

The superconducting coplanar waveguide (SCPW) cavity plays an essential role in various areas like superconducting qubits, parametric amplifiers, radiation detectors, and studying magnon-photon and photon-phonon coupling. Despite its wide-ranging applications, the use of SCPW cavities to study various van der Waals 2D materials is relatively unexplored. The resonant modes of the SCPW cavity exquisitely sense the dielectric environment. In this work, we measure the charge compressibility of bilayer graphene coupled to a half-wavelength SCPW cavity. Our approach provides a means to detect subtle changes in the capacitance of the bilayer graphene heterostructure, which depends on the compressibility of bilayer graphene, manifesting as shifts in the resonant frequency of the cavity. This method holds promise for exploring a wide class of van der Waals 2D materials, including transition metal dichalcogenides (TMDs) and their moiré where DC transport measurement is challenging.

cond-mat.mes-hall↗

arXiv2023

Best arm identification in rare events

We consider the best arm identification problem in the stochastic multi-armed bandit framework where each arm has a tiny probability of realizing large rewards while with overwhelming probability the reward is zero. A key application of this framework is in online advertising where click rates of advertisements could be a fraction of a single percent and final conversion to sales, while highly profitable, may again be a small fraction of the click rates. Lately, algorithms for BAI problems have been developed that minimise sample complexity while providing statistical guarantees on the correct arm selection. As we observe, these algorithms can be computationally prohibitive. We exploit the fact that the reward process for each arm is well approximated by a Compound Poisson process to arrive at algorithms that are faster, with a small increase in sample complexity. We analyze the problem in an asymptotic regime as rarity of reward occurrence reduces to zero, and reward amounts increase to infinity. This helps illustrate the benefits of the proposed algorithm. It also sheds light on the underlying structure of the optimal BAI algorithms in the rare event setting.

cs.LG↗

arXiv2021

Properties of Very Broad Line MgII Radio-Loud and Radio-Quiet Quasars

We perform an analysis of the properties of radio-loud (RL) and radio-quiet (RQ) quasars with MgII broad emission line (i-band magnitude $\leq 19.1$ and z $\leq 1.9$), selected from the parent sample of SDSS DR7 catalogue. For sources with full-width half maxima (FWHM) greater than \mbox{15,000 km s$^{-1}$} (very broad line sample; VBL) we find the radio loud fraction (RLF) to be about 40\%. To further investigate this result we compare the bolometric luminosity, optical continuum luminosity, black hole (BH) mass and Eddington ratios of our VBL sample of RL and RQ quasars. Our analysis shows that in our VBL sample space, RL quasars have higher luminosities and BH mass than RQ quasars. The similarity in the distribution of their covering fraction (CF) shows that there is no difference in dust distribution between VBL RL and RQ quasars and hence dust is not affecting our results. We also find that there is no correlation of RL quasar properties with optical continuum luminosity and BH mass.

astro-ph.GA↗

arXiv2021

A scalable spectral Stokes solver for simulation of time-periodic flows in complex geometries

Simulation of unsteady creeping flows in complex geometries has traditionally required the use of a time-stepping procedure, which is typically costly and unscalable. To reduce the cost and allow for computations at much larger scales, we propose an alternative approach that is formulated based on the unsteady Stokes equation expressed in the time-spectral domain. This transformation results in a boundary value problem with an imaginary source term proportional to the computed mode that is discretized and solved in a complex-valued finite element solver using Bubnov-Galerkin formulation. This transformed spatio-spectral formulation presents several advantages over the traditional spatio-temporal techniques. Firstly, for cases with boundary conditions varying smoothly in time, it provides a significant saving in computational cost as it can resolve time-variation of the solution using a few modes rather than thousands of time steps. Secondly, in contrast to the traditional time integration scheme with a finite order of accuracy, this method exhibits a super convergence behavior versus the number of computed modes. Thirdly, in contrast to the stabilized finite element methods for fluid, no stabilization term is employed in our formulation, producing a solution that is consistent and more accurate. Fourthly, the proposed approach is embarrassingly parallelizable owing to the independence of the solution modes, thus enabling scalable calculations at a much larger number of processors. The comparison of the proposed technique against a standard stabilized finite element solver is performed using two- and three-dimensional canonical and complex geometries. The results show that the proposed method can produce more accurate results at 1% to 11% of the cost of the standard technique for the studied cases.

cs.CE↗

arXiv2020

Long-range connectivity in a superconducting quantum processor using a ring resonator

Qubit coherence and gate fidelity are typically considered the two most important metrics for characterizing a quantum processor. An equally important metric is inter-qubit connectivity as it minimizes gate count and allows implementing algorithms efficiently with reduced error. However, inter-qubit connectivity in superconducting processors tends to be limited to nearest neighbour due to practical constraints in the physical realization. Here, we introduce a novel superconducting architecture that uses a ring resonator as a multi-path coupling element with the qubits uniformly distributed throughout its circumference. Our planar design provides significant enhancement in connectivity over state of the art superconducting processors without any additional fabrication complexity. We theoretically analyse the qubit connectivity and experimentally verify it in a device capable of supporting up to twelve qubits where each qubit can be connected to nine other qubits. Our concept is scalable, adaptable to other platforms and has the potential to significantly accelerate progress in quantum computing, annealing, simulations and error correction.

quant-ph↗

arXiv2020

BH Mass, Jet and Accretion Disk Connection: An Analysis of Radio-loud and Radio-quiet Quasars

Surveys have shown radio-loud (RL) quasars constitute $10 \%$-$15 \%$ of the total quasar population. However, it is unknown if the radio-loud fraction (RL quasars/Total quasars) remains consistent among different parameter spaces. This study shows that radio-loud fraction increases for increasing full width half maximum (FWHM) velocity of the H$β$ and MgII broad emission line. Our data has been obtained from Shen et al. (2011) catalogue. To investigate the reason, in this preliminary study we analyse various properties like bolometric luminosity, optical continuum luminosity, black hole (BH) mass and accretion rate of RL quasars and RQ quasars sample which have FWHM greater than 15000km/s (High broad line). From the distributions we can conclude for all the properties in high broad line, RL quasars are having higher values than RQ quasars. We have predicted RL quasars are intrinsically brighter than RQ quasars and also predicted BH mass-jet connection and accretion disk-jet connection from our results but to conclude anything more analysis is needed.

astro-ph.GA↗

arXiv2020

Deep-Edge: An Efficient Framework for Deep Learning Model Update on Heterogeneous Edge

Deep Learning (DL) model-based AI services are increasingly offered in a variety of predictive analytics services such as computer vision, natural language processing, speech recognition. However, the quality of the DL models can degrade over time due to changes in the input data distribution, thereby requiring periodic model updates. Although cloud data-centers can meet the computational requirements of the resource-intensive and time-consuming model update task, transferring data from the edge devices to the cloud incurs a significant cost in terms of network bandwidth and are prone to data privacy issues. With the advent of GPU-enabled edge devices, the DL model update can be performed at the edge in a distributed manner using multiple connected edge devices. However, efficiently utilizing the edge resources for the model update is a hard problem due to the heterogeneity among the edge devices and the resource interference caused by the co-location of the DL model update task with latency-critical tasks running in the background. To overcome these challenges, we present Deep-Edge, a load- and interference-aware, fault-tolerant resource management framework for performing model update at the edge that uses distributed training. This paper makes the following contributions. First, it provides a unified framework for monitoring, profiling, and deploying the DL model update tasks on heterogeneous edge devices. Second, it presents a scheduler that reduces the total re-training time by appropriately selecting the edge devices and distributing data among them such that no latency-critical applications experience deadline violations. Finally, we present empirical results to validate the efficacy of the framework using a real-world DL model update case-study based on the Caltech dataset and an edge AI cluster testbed.

cs.DC↗

arXiv2020

City-Scale Agent-Based Simulators for the Study of Non-Pharmaceutical Interventions in the Context of the COVID-19 Epidemic

We highlight the usefulness of city-scale agent-based simulators in studying various non-pharmaceutical interventions to manage an evolving pandemic. We ground our studies in the context of the COVID-19 pandemic and demonstrate the power of the simulator via several exploratory case studies in two metropolises, Bengaluru and Mumbai. Such tools become common-place in any city administration's tool kit in our march towards digital health.

q-bio.PE↗

arXiv2020

Spectroscopic properties of radio-loud and radio-quiet quasars

Surveys have shown radio-loud (RL) quasars constitute 10%-15% of the total quasar population and rest are radio-quiet (RQ). However, it is unknown if this radio-loud fraction (RLF) remains consistent among different parameter spaces. This study shows that RLF increases for increasing full width half maximum (FWHM) velocity of the H$β$ broad emission line (z < 0.75). To analyse the reason, we compared bolometric luminosity of RL and RQ quasars sample which have FWHM of H$β$ broad emission line greater than 15000km/s (High Broad Line or HBL) with which have FWHM of H$β$ emission line less than 2500km/s (Low Broad Line or LBL). From the distributions we can conclude for the HBL, RQ and RL quasars are peaking separately and RL quasars are having higher values whereas for the LBL the peaks are almost indistinguishable. We predicted selection effects could be the possible reason but to conclude anything more analysis is needed. Then we compared our result with Wills & Browne (1986) and have shown that some objects from our sample do not follow the pattern of the logR vs FWHM plot where R is the ratio of 5 GHz radio core flux density with the extended radio lobe flux density.

astro-ph.GA↗

arXiv2019

Engineering Cross Resonance Interaction in Multi-modal Quantum Circuits

Existing scalable superconducting quantum processors have only nearest-neighbor coupling. This leads to reduced circuit depth, requiring large series of gates to perform an arbitrary unitary operation in such systems. Recently, multi-modal devices have been demonstrated as a promising candidate for small quantum processor units. Always on longitudinal coupling in such circuits leads to implementation of native high fidelity multi-qubit gates. We propose an architecture using such devices as building blocks for a highly connected larger quantum circuit. To demonstrate a quantum operation between such blocks, a standard transmon is coupled to the multi-modal circuit using a 3D bus cavity giving rise to small exchange interaction between the transmon and one of the modes. We study the cross resonance interaction in such systems and characterize the entangling operation as well as the unitary imperfections and cross-talk as a function of device parameters. Finally, we tune up the cross resonance drive to implement multi-qubit gates in this architecture.

quant-ph↗

arXiv2019

FECBench: A Holistic Interference-aware Approach for Application Performance Modeling

Services hosted in multi-tenant cloud platforms often encounter performance interference due to contention for non-partitionable resources, which in turn causes unpredictable behavior and degradation in application performance. To grapple with these problems and to define effective resource management solutions for their services, providers often must expend significant efforts and incur prohibitive costs in developing performance models of their services under a variety of interference scenarios on different hardware. This is a hard problem due to the wide range of possible co-located services and their workloads, and the growing heterogeneity in the runtime platforms including the use of fog and edge-based resources, not to mention the accidental complexity in performing application profiling under a variety of scenarios. To address these challenges, we present FECBench, a framework to guide providers in building performance interference prediction models for their services without incurring undue costs and efforts. The contributions of the paper are as follows. First, we developed a technique to build resource stressors that can stress multiple system resources all at once in a controlled manner to gain insights about the interference on an application's performance. Second, to overcome the need for exhaustive application profiling, FECBench intelligently uses the design of experiments (DoE) approach to enable users to build surrogate performance models of their services. Third, FECBench maintains an extensible knowledge base of application combinations that create resource stresses across the multi-dimensional resource design space. Empirical results using real-world scenarios to validate the efficacy of FECBench show that the predicted application performance has a median error of only 7.6% across all test cases, with 5.4% in the best case and 13.5% in the worst case.

cs.DC↗

arXiv2019

BARISTA: Efficient and Scalable Serverless Serving System for Deep Learning Prediction Services

Pre-trained deep learning models are increasingly being used to offer a variety of compute-intensive predictive analytics services such as fitness tracking, speech and image recognition. The stateless and highly parallelizable nature of deep learning models makes them well-suited for serverless computing paradigm. However, making effective resource management decisions for these services is a hard problem due to the dynamic workloads and diverse set of available resource configurations that have their deployment and management costs. To address these challenges, we present a distributed and scalable deep-learning prediction serving system called Barista and make the following contributions. First, we present a fast and effective methodology for forecasting workloads by identifying various trends. Second, we formulate an optimization problem to minimize the total cost incurred while ensuring bounded prediction latency with reasonable accuracy. Third, we propose an efficient heuristic to identify suitable compute resource configurations. Fourth, we propose an intelligent agent to allocate and manage the compute resources by horizontal and vertical scaling to maintain the required prediction latency. Finally, using representative real-world workloads for urban transportation service, we demonstrate and validate the capabilities of Barista.

cs.DC↗

arXiv2019

CloudCAMP: Automating Cloud Services Deployment and Management

Users of cloud platforms often must expend significant manual efforts in the deployment and orchestration of their services on cloud platforms due primarily to having to deal with the high variabilities in the configuration options for virtualized environment setup and meeting the software dependencies for each service. Despite the emergence of many DevOps cloud automation and orchestration tools, users must still rely on specifying low-level scripting details for service deployment and management using Infrastructure-as-Code (IAC). Using these tools required domain expertise along with a steep learning curve. To address these challenges in a tool-and-technology agnostic manner, which helps promote interoperability and portability of services hosted across cloud platforms, we present initial ideas on a GUI based cloud automation and orchestration framework called CloudCAMP. It incorporates domain-specific modeling so that the specifications and dependencies imposed by the cloud platform and application architecture can be specified at an intuitive, higher level of abstraction without the need for domain expertise using Model-Driven Engineering(MDE) paradigm. CloudCAMP transforms the partial specifications into deployable Infrastructure-as-Code (IAC) using the Transformational-Generative paradigm and by leveraging an extensible and reusable knowledge base. The auto-generated IAC can be handled by existing tools to provision the services components automatically. We validate our approach quantitatively by showing a comparative study of savings in manual and scripting efforts versus using CloudCAMP.

cs.SE↗

arXiv2019

Stratum: A Serverless Framework for Lifecycle Management of Machine Learning based Data Analytics Tasks

With the proliferation of machine learning (ML) libraries and frameworks, and the programming languages that they use, along with operations of data loading, transformation, preparation and mining, ML model development is becoming a daunting task. Furthermore, with a plethora of cloud-based ML model development platforms, heterogeneity in hardware, increased focus on exploiting edge computing resources for low-latency prediction serving and often a lack of a complete understanding of resources required to execute ML workflows efficiently, ML model deployment demands expertise for managing the lifecycle of ML workflows efficiently and with minimal cost. To address these challenges, we propose an end-to-end data analytics, a serverless platform called Stratum. Stratum can deploy, schedule and dynamically manage data ingestion tools, live streaming apps, batch analytics tools, ML-as-a-service (for inference jobs), and visualization tools across the cloud-fog-edge spectrum. This paper describes the Stratum architecture highlighting the problems it resolves.

cs.SE↗