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Soumya Jana

Publications and source records attributed to Soumya Jana.

At least 19 recordsLinked to original sources
arXiv2026

Gravitational lensing and deflection angles of generalised Ellis-Bronnikov wormhole embedded in a warped braneworld background

We investigate null trajectories, deflection angles, and gravitational lensing in the spacetime of generalized Ellis-Bronnikov (GEB) wormholes and their embedding in a five-dimensional warped braneworld background (WGEB). The GEB geometry extends the standard Ellis-Bronnikov (EB) wormhole by introducing a steepness parameter $m \geq 2$, which controls the shape of the wormhole throat while partially improving on the violation of classical energy conditions. We compare the lensing properties of the four-dimensional GEB geometry with those of its warped five-dimensional counterpart, where the effect of the extra dimension is encoded through the parameter $δ$, associated with the photon momentum along the extra dimension. Analytic expressions for the deflection angle are obtained in both weak and strong-lensing regimes, and the known EB results are recovered for $m = 2$. For $m > 2$, analytic approximation and numerical analysis is used where exact analytic solutions are not available. We show that the parameter $m$ leaves clear and distinguishable signatures in the deflection angle, Einstein ring radius, and image positions, while the presence of the warped extra dimension modifies the effective impact parameter and leads to a broadening of the photon sphere and lensed images.

gr-qc↗
arXiv2026

Gravitational wave radiation from periodic orbits in regular black holes

Gravitational wave radiation from periodic orbits in some standard regular black hole spacetimes is studied, primarily using known methods (numerical and analytic). We demonstrate specific differences with the singular Schwarzschild geometry by analysing orbit characteristics, gravitational wave strain profiles, and the corresponding power spectrum density, for different values of the regularising parameter `$g$'. Further, we assess our results vis-a-vis the LISA sensitivity curves and show how our results may be useful while developing templates for detecting regular black holes as viable alternatives to the singular ones. The appendices to our article contain details on errors in our estimates and provide for the first time, some exact analytical expressions on gravitational wave radiation from different types of periodic orbits in Schwarzschild spacetime.

gr-qc↗
arXiv2025

New rotating Lorentzian wormhole spacetime

A rotating version of a known static, spherically symmetric, zero Ricci scalar Lorentzian wormhole is constructed. It turns out that for this given non-rotating geometry, the standard Newman-Janis algorithm does not produce a rotating wormhole and, therefore, the method pioneered by Azreg-Aïnou has to be used. The rotating spacetime thus obtained is shown to be regular with wormhole features, though it is no longer a $R=0$ spacetime. The required matter is found to violate the energy conditions, as expected. A few other characteristic properties of this new rotating spacetime are mentioned. Finally, we calculate the shadow for this geometry and discuss its features {\em vis-a-vis} the Kerr geometry and available event horizon telescope observations.

gr-qc↗
arXiv2025

Gravitational radiation from binary systems in Unimodular gravity

Unimodular gravity (UG) is classically considered identical to General Relativity (GR). However, due to restricted diffeomorphism symmetry, the Bianchi identites do not lead to the conservation of energy-momentum tensor. Thus, the conservation of energy-momentum tensor needs to be separately assumed in order to reconcile with GR. Relaxing this assumption, one finds that the conservation violation can lead to differences with GR, which can be subsequently examined in astrophysical and cosmological scenarios. To this end, we examine the predictions of UG in the context of binary systems emitting gravitational radiation. Primarily, we show how the field equations involve a diffusion function which quantifies the measure of non-conservation. Due to this violation, the dispersion relation is modified. Incorporating these changes, we provide an expression for the energy loss by the binaries, which reduces to Peters-Mathews result in the GR limit. Using binary pulsar data, we constrain the theory parameter $ζ$ (which signifies non-conservation) by determining the rate of orbital decay. The strongest constrain on $ζ$ comes out to be $\vert ζ\vert \leq 5\times 10^{-4}$ which is better by an order of magnitude than an existing equivalent constraint coming from the tidal deformability of the neutron stars.

gr-qc↗
arXiv2024

AI-based 3-Lead to 12-Lead ECG Reconstruction: Towards Smartphone-based Public Healthcare

Clinicians generally diagnose cardiovascular diseases (CVDs) using standard 12-Lead electrocardiogram (ECG). However, for smartphone-based public healthcare systems, a reduced 3-lead system may be preferred because of (i) increased portability, and (ii) reduced requirement for power, storage and bandwidth. Subsequently, clinicians require accurate 3-lead to 12-Lead ECG reconstruction, which has so far been studied only in the personalized setting. When each device is dedicated to one individual, artificial intelligence (AI) methods such as temporal long short-term memory (LSTM) and a further improved spatio-temporal LSTM-UNet combine have proven effective. In contrast, in the current smartphone-based public health setting where a common device is shared by many, developing an AI lead-reconstruction model that caters to the extensive ECG signal variability in the general population appears a far greater challenge. In this direction, we take a first step, and observe that the performance improvement achieved by a generative model, specifically, 1D Pix2Pix GAN (generative adversarial network), over LSTM-UNet is encouraging.

eess.SP↗
arXiv2024

Next-Generation Teleophthalmology: AI-enabled Quality Assessment Aiding Remote Smartphone-based Consultation

Blindness and other eye diseases are a global health concern, particularly in low- and middle-income countries like India. In this regard, during the COVID-19 pandemic, teleophthalmology became a lifeline, and the Grabi attachment for smartphone-based eye imaging gained in use. However, quality of user-captured image often remained inadequate, requiring clinician vetting and delays. In this backdrop, we propose an AI-based quality assessment system with instant feedback mimicking clinicians' judgments and tested on patient-captured images. Dividing the complex problem hierarchically, here we tackle a nontrivial part, and demonstrate a proof of the concept.

cs.HC↗
arXiv2024

Shadows of a generic class of spherically symmetric, static spacetimes

We explore the characteristics of shadows for a general class of spherically symmetric, static spacetimes, which may arise in general relativity or in modified theories of gravity. The chosen line element involves a sum (with constant but different coefficients) of integer powers of $\frac{1}{\text{r}}$ in $\text{g}_\text{tt}$ and $\text{g}_\text{rr}$, in the Schwarzschild gauge. We begin our discussion by motivating the line element through a study of the energy conditions (null and weak) and the extent to which they are satisfied/violated for diverse choices of the parameters appearing in the metric functions. Subsequently, we construct the circular shadows and analyse the dependence of the shadow radius on the metric parameters. We find that with specific choices of the metric parameters (within the ranges allowed by the energy conditions) one can, in principle, obtain values that conform with recent observations on shadows, as available in the literature. We also mention where such metrics may arise (i.e., in which theory of gravity and the physical scenario therein), thereby proposing that the observed shadows may be representative signatures of different theoretical contexts.

gr-qc↗
arXiv2023

3-Lead to 12-Lead ECG Reconstruction: A Novel AI-based Spatio-Temporal Method

Diagnosis of cardiovascular diseases usually relies on the widely used standard 12-Lead (S12) ECG system. However, such a system could be bulky, too resource-intensive, and too specialized for personalized home-based monitoring. In contrast, clinicians are generally not trained on the alternative proposal, i.e., the reduced lead (RL) system. This necessitates mapping RL to S12. In this context, to improve upon traditional linear transformation (LT) techniques, artificial intelligence (AI) approaches like long short-term memory (LSTM) networks capturing non-linear temporal dependencies, have been suggested. However, LSTM does not adequately interpolate spatially (in 3D). To fill this gap, we propose a combined LSTM-UNet model that also handles spatial aspects of the problem, and demonstrate performance improvement. Evaluated on PhysioNet PTBDB database, our LSTM-UNet achieved a mean R^2 value of 94.37%, surpassing LSTM by 0.79% and LT by 2.73%. Similarly, for PhysioNet INCARTDB database, LSTM-UNet achieved a mean R^2 value of 93.91%, outperforming LSTM by 1.78% and LT by 12.17%.

eess.SP↗
arXiv2023

Shadows in dyonic Kerr-Sen black holes

Black holes with dyonic charges in Einstein-Maxwell-dilaton-axion supergravity theory are revisited in the context of black hole shadows. We consider static as well as rotating (namely the dyonic Kerr-Sen) black holes. The matter stress-energy tensor components, sourced by the Maxwell, axion and dilaton fields satisfy the standard energy conditions. The analytical expressions for the horizon and the shadow radius of the static spacetimes demonstrate their dependence on $P^2+Q^2$ ($P$, $Q$ the magnetic and electric charges, respectively) and the mass parameter $M$. The shadow radius lies in the range $2M <R_{shadow}<3\sqrt{3} M$ and there is no stable photon orbit outside the horizon. Further, shadows cast by the rotating dyonic Kerr-Sen black holes are also studied and compared graphically with their Kerr-Newman and Kerr-Sen counterparts. Deviation of the shadow boundary is prominent with the variation of the magnetic charge, for the relatively slowly rotating dyonic Kerr-Sen spacetimes. We test any possible presence of a magnetic monopole charge in the backdrop of recent EHT observations for the supermassive black holes M87$^*$ and Sgr A$^*$. Deviation from circularity of the shadow boundary ($ΔC$) and deviation of the average shadow radius from the Schwarzschild shadow radius (quantified as the fractional deviation parameter $δ$) are the two observables used here. Observational bound on $ΔC$ (available only for M87$^*$) is satisfied for all theoretically allowed regions of parameter space and thus cannot constrain the parameters. The observational bound on $δ$ available for Sgr A$^*$ translates into an upper limit on any possible magnetic monopole charge linked to Sgr A$^*$ and is given as $P\lesssim 0.873\, M$. Such a constraint on $P$ is however expected to be far more stringent for other astrophysical tests.

gr-qc↗
arXiv2023

Gravitational radiation from binary systems in $f(R)$ gravity: A semi-classical approach

The rate of energy loss and orbital period decay of quasi-stable compact binary systems are derived in $f(R)$ theory of gravity using the method of a single vertex graviton emission process from a classical source. After linearising the $f(R)$ action written in an equivalent scalar-tensor format in the Einstein frame, we identify the appropriate interaction terms between the massless spin-2 tensor mode, massive scalar mode, and the energy momentum tensor. The definition of the scalar field is related to the $f(R)$ models. Then using the interaction vertex we compute the rate of energy loss due to spin-2 quadrupole radiation, which comes out to be the same as the Peter-Mathews formula with a multiplication factor, and also the energy loss due to the scalar dipole radiation. The total energy loss is the sum of these two contributions. Our derivation is most general as it is applicable for both arbitrary eccentricity of the binary orbits and arbitrary mass of the scalar field. Using the derived theoretical formula for the period decay of the binary systems, we compare the predictions of $f(R)$ gravity and general relativity for the observations of four binary systems, i.e. Hulse-Taylor Binary, PSR J1141-6545, PSR J1738+0333, and PSR J0348+0432. Thus we put bound on three well-known $f(R)$ dark energy models, namely the Hu-Sawicki, the Starobinsky, and the Tsujikawa model. We get the best constraint on $f'(R_0)-1$ (where $R_0$ is the scalar curvature of the Universe at the present epoch) from the Tsujikawa model, i.e $\vert f'(R_0)-1\vert < 2.09\times 10^{-4}$. This bound is stronger than those from most of the astrophysical observations and even some cosmological observations.

gr-qc↗
arXiv2023

Diagnostic Quality Assessment of Fundus Photographs: Hierarchical Deep Learning with Clinically Significant Explanations

Fundus photography (FP) remains the primary imaging modality in screening various retinal diseases including age-related macular degeneration, diabetic retinopathy and glaucoma. FP allows the clinician to examine the ocular fundus structures such as the macula, the optic disc (OD) and retinal vessels, whose visibility and clarity in an FP image remain central to ensuring diagnostic accuracy, and hence determine the diagnostic quality (DQ). Images with low DQ, resulting from eye movement, improper illumination and other possible causes, should obviously be recaptured. However, the technician, often unfamiliar with DQ criteria, initiates recapture only based on expert feedback. The process potentially engages the imaging device multiple times for single subject, and wastes the time and effort of the ophthalmologist, the technician and the subject. The burden could be prohibitive in case of teleophthalmology, where obtaining feedback from the remote expert entails additional communication cost and delay. Accordingly, a strong need for automated diagnostic quality assessment (DQA) has been felt, where an image is immediately assigned a DQ category. In response, motivated by the notional continuum of DQ, we propose a hierarchical deep learning (DL) architecture to distinguish between good, usable and unusable categories. On the public EyeQ dataset, we achieve an accuracy of 89.44%, improving upon existing methods. In addition, using gradient based class activation map (Grad-CAM), we generate a visual explanation which agrees with the expert intuition. Future FP cameras equipped with the proposed DQA algorithm will potentially improve the efficacy of the teleophthalmology as well as the traditional system.

eess.IV↗
arXiv2022

Gravitational radiation from binary systems in massive graviton theories

Theories with massive gravitons have peculiarity called the van Dam-Veltman-Zakharov discontinuity in that the massive theory propagator does not go to the massless graviton propagator in the zero graviton mass limit. This results in large deviation in Newtons law for massive graviton theories even when the graviton mass vanishes. We test the vDVZ in massive graviton theories for single graviton vertex process namely the gravitational radiation from a classical source. We calculate the gravitational radiation from compact binaries using the perturbative Feynman diagram method. We perform this calculation for Einstein's gravity with massless gravitons and verify that the Feynman diagram calculation reproduces the quadrupole formula. Using the same procedure we calculate the gravitational radiation for three massive graviton theories: (1) the Fierz-Pauli theory (2) the modified Fierz-Pauli theory without the vDVZ discontinuity and (3) the Dvali-Gabadadze-Porrati theory with a momentum dependent graviton mass. We put limits on the graviton mass in each of these theories from observations of binary pulsar timings.

gr-qc↗
arXiv2021

Correlation in Neuronal Calcium Spiking: Quantification based on Empirical Mutual Information Rate

Quantification of neuronal correlations in neuron populations helps us to understand neural coding rules. Such quantification could also reveal how neurons encode information in normal and disease conditions like Alzheimer's and Parkinson's. While neurons communicate with each other by transmitting spikes, there would be a change in calcium concentration within the neurons inherently. Accordingly, there would be correlations in calcium spike trains and they could have heterogeneous memory structures. In this context, estimation of mutual information rate in calcium spike trains assumes primary significance. However, such estimation is difficult with available methods which would consider longer blocks for convergence without noticing that neuronal information changes in short time windows. Against this backdrop, we propose a faster method that exploits the memory structures in pair of calcium spike trains to quantify mutual information shared between them. Our method has shown superior performance with example Markov processes as well as experimental spike trains. Such mutual information rate analysis could be used to identify signatures of neuronal behavior in large populations in normal and abnormal conditions.

q-bio.NC↗
arXiv2021

Efficient Screening of Diseased Eyes based on Fundus Autofluorescence Images using Support Vector Machine

A variety of vision ailments are associated with geographic atrophy (GA) in the foveal region of the eye. In current clinical practice, the ophthalmologist manually detects potential presence of such GA based on fundus autofluorescence (FAF) images, and hence diagnoses the disease, when relevant. However, in view of the general scarcity of ophthalmologists relative to the large number of subjects seeking eyecare, especially in remote regions, it becomes imperative to develop methods to direct expert time and effort to medically significant cases. Further, subjects from either disadvantaged background or remote localities, who face considerable economic/physical barrier in consulting trained ophthalmologists, tend to seek medical attention only after being reasonably certain that an adverse condition exists. To serve the interest of both the ophthalmologist and the potential patient, we plan a screening step, where healthy and diseased eyes are algorithmically differentiated with limited input from only optometrists who are relatively more abundant in number. Specifically, an early treatment diabetic retinopathy study (ETDRS) grid is placed by an optometrist on each FAF image, based on which sectoral statistics are automatically collected. Using such statistics as features, healthy and diseased eyes are proposed to be classified by training an algorithm using available medical records. In this connection, we demonstrate the efficacy of support vector machines (SVM). Specifically, we consider SVM with linear as well as radial basis function (RBF) kernel, and observe satisfactory performance of both variants. Among those, we recommend the latter in view of its slight superiority in terms of classification accuracy (90.55% at a standard training-to-test ratio of 80:20), and practical class-conditional costs.

cs.CV↗
arXiv2021

Heterogeneity in Neuronal Calcium Spike Trains based on Empirical Distance

Statistical similarities between neuronal spike trains could reveal significant information on complex underlying processing. In general, the similarity between synchronous spike trains is somewhat easy to identify. However, the similar patterns also potentially appear in an asynchronous manner. However, existing methods for their identification tend to converge slowly, and cannot be applied to short sequences. In response, we propose Hellinger distance measure based on empirical probabilities, which we show to be as accurate as existing techniques, yet faster to converge for synthetic as well as experimental spike trains. Further, we cluster pairs of neuronal spike trains based on statistical similarities and found two non-overlapping classes, which could indicate functional similarities in neurons. Significantly, our technique detected functional heterogeneity in pairs of neuronal responses with the same performance as existing techniques, while exhibiting faster convergence. We expect the proposed method to facilitate large-scale studies of functional clustering, especially involving short sequences, which would in turn identify signatures of various diseases in terms of clustering patterns.

q-bio.NC↗
arXiv2021

Horndeski theories and beyond from higher dimensions

The Einstein-Hilbert action with a cosmological constant is the most general local four-dimensional action leading to second-order derivative equations of motion that are symmetric and divergence free. In higher dimensions, additional terms can appear. We investigate a generalised metric decomposition involving a scalar degree of freedom to express the higher-dimensional action as an effective four-dimensional scalar-tensor theory. From the higher-dimensional Ricci scalar alone and a subclass of our metric ansatz, we recover the subset of Horndeski theories with luminal speed of gravitational waves. More generally, beyond-Horndeski terms appear. When including a Gauss-Bonnet scalar in the higher-dimensional action, we generate contributions to all cubic-order second-derivative terms present in the degenerate higher-order scalar-tensor theory as well as higher-derivative terms beyond that. We discuss this technique as a way to generate healthy four-dimensional gravity theories with an extra scalar degree of freedom and outline further generalisations of our method.

gr-qc↗
arXiv2021

Information Content in Neuronal Calcium Spike Trains: Entropy Rate Estimation based on Empirical Probabilities

Quantification of information content and its temporal variation in intracellular calcium spike trains in neurons helps one understand functions such as memory, learning, and cognition. Such quantification could also reveal pathological signaling perturbation that potentially leads to devastating neurodegenerative conditions including Parkinson's, Alzheimer's, and Huntington's diseases. Accordingly, estimation of entropy rate, an information-theoretic measure of information content, assumes primary significance. However, such estimation in the present context is challenging because, while entropy rate is traditionally defined asymptotically for long blocks under the assumption of stationarity, neurons are known to encode information in short intervals and the associated spike trains often exhibit nonstationarity. Against this backdrop, we propose an entropy rate estimator based on empirical probabilities that operates within windows, short enough to ensure approximate stationarity. Specifically, our estimator, parameterized by the length of encoding contexts, attempts to model the underlying memory structures in neuronal spike trains. In an example Markov process, we compared the performance of the proposed method with that of versions of the Lempel-Ziv algorithm as well as with that of a certain stationary distribution method and found the former to exhibit higher accuracy levels and faster convergence. Also, in experimentally recorded calcium responses of four hippocampal neurons, the proposed method showed faster convergence. Significantly, our technique detected structural heterogeneity in the underlying process memory in the responses of the aforementioned neurons. We believe that the proposed method facilitates large-scale studies of such heterogeneity, which could in turn identify signatures of various diseases in terms of entropy rate estimates.

eess.SP↗
arXiv2020

Constraints on long range force from perihelion precession of planets in a gauged $L_e-L_{μ,τ}$ scenario

The standard model particles can be gauged in an anomaly free way by three possible gauge symmetries namely ${L_e-L_μ}$, ${L_e-L_τ}$, and ${L_μ-L_τ}$. Of these, ${L_e-L_μ}$ and ${L_e-L_τ}$ forces can mediate between the Sun and the planets and change the perihelion precession of planetary orbits. It is well known that a deviation from the $1/r^2$ Newtonian force can give rise to a perihelion advancement in the planetary orbit, for instance, as in the well known case of Einstein's gravity which was tested from the observation of the perihelion advancement of the Mercury. We consider the long range Yukawa potential which arises between the Sun and the planets if the mass of the gauge boson is $M_{Z^{\prime}}\leq \mathcal{O}(10^{-19})\rm {eV}$. We derive the formula of perihelion advancement for Yukawa type fifth force due to the mediation of such $U(1)_{L_e-L_{μ,τ}}$ gauge bosons. The perihelion advancement for Yukawa potential is proportional to the square of the semi major axis of the orbit for small $M_{Z^{\prime}}$, unlike GR, where it is largest for the nearest planet. However for higher values of $M_{Z^{\prime}}$, an exponential suppression of the perihelion advancement occurs. We take the observational limits for all planets for which the perihelion advancement is measured and we obtain the upper bound on the gauge boson coupling $g$ for all the planets. The Mars gives the stronger bound on $g$ for the mass range $\leq 10^{-19}\rm{eV}$ and we obtain the exclusion plot. This mass range of gauge boson can be a possible candidate of fuzzy dark matter whose effect can therefore be observed in the precession measurement of the planetary orbits.

hep-ph↗