arXiv subjects

Arnab Sarker

Publications and source records attributed to Arnab Sarker.

16 recordsLinked to original sources

arXiv2026

Interplay of CPT-Violating and CPT-Conserving Lorentz Invariance Violation at DUNE

We present a study of Lorentz invariance violation (LIV) in neutrino oscillations, with primary emphasis on the interplay between CPT-violating and CPT-conserving Standard Model Extension (SME) coefficients. We find that $a_{ee}$ and $a_{\tau\tau}$ dominate among the diagonal LIV coefficients, whereas $a_{e\mu}$ and $a_{e\tau}$ provide the most significant off-diagonal contributions. In contrast, the corresponding $c_{\alpha\beta}$ coefficients have comparatively sub-leading effects. Taking DUNE as a representative long-baseline experiment, we show that the sensitivity to CP violation is modified by LIV in a parameter-specific manner. We find correlations between off-diagonal LIV parameters, and non-trivial dependence on the new phases. In absence of LIV, DUNE is expected to establish CP violation at $5\sigma$ for a limited fraction of $\delta_{CP}$ values. We find that the presence of $a_{ee}$, $a_{\tau\tau}$, $a_{e\mu}$ and $a_{e\tau}$ weakens the CP discovery potential, reducing the achievable significance to below $3\sigma$ over a substantial fraction of $\delta_{\rm CP}$. The independent impact of $c_{\alpha\beta}$ terms also results in suppression, but in combination with $a_{\alpha\beta}$, they introduce degeneracies, complicating the extraction of $\delta_{CP}$. This generally results in a deterioration of CP violation sensitivities below $5\sigma$. These findings emphasize the importance of incorporating LIV effects in precision oscillation studies at upcoming long-baseline experiments.

hep-ph↗

arXiv2026

Diffuse Supernova Neutrinos with Secret Neutrino Interactions

The Diffuse Supernova Neutrino Background (DSNB), an isotropic flux arising from the cumulative neutrino emission of all stellar core-collapse events throughout cosmic history, is expected to be detected by next-generation neutrino observatories. As DSNB neutrinos propagate over cosmological distances through the cosmic neutrino background (C$\nu$B), they may undergo non-standard neutrino self-interactions ($\nu$SI), leaving distinct spectral imprints on the observed flux. In this work, we investigate the impact of scalar ($\phi$)-mediated $\nu$SI on the DSNB within a full three-flavor framework that retains the complete PMNS structure. We consider four representative flavor-diagonal coupling structures--universal, $e$-, $\mu$-, and $\tau$-specific. The resonant scattering $\nu_i\nu_k\to\phi\to\nu_j\nu_l$ off the lightest, relativistic C$\nu$B state produces broad spectral depletion whose pattern depends on the coupling structure and the neutrino mass ordering, generating distinctive signatures across the six flavor fluxes. We compute the resulting event spectra at JUNO, Hyper-Kamiokande with gadolinium loading, and DUNE, and derive projected $3\sigma$ sensitivities in the $(m_{\phi},~g)$ parameter plane. We find that these experiments can probe couplings as low as $g\sim10^{-8}$ for $m_\phi\sim100$--$300$ eV, surpassing existing bounds by up to a few orders of magnitude in the sub-100 eV mass range. Moreover, unlike the flavor-blind cosmological and supernova bounds, the DSNB sensitivity is flavor-discriminating, offering a unique opportunity to identify the underlying flavor structure of $\nu$SI in the event of a detection.

hep-ph↗

arXiv2026

Network and Risk Analysis of Surety Bonds

Surety bonds are financial agreements between a contractor (principal) and obligee (project owner) to complete a project. However, most large-scale projects involve multiple contractors, creating a network and introducing the possibility of incomplete obligations to propagate and result in project failures. Typical models for risk assessment assume independent failure probabilities within each contractor. However, we take a network approach, modeling the contractor network as a directed graph where nodes represent contractors and project owners and edges represent contractual obligations with associated financial records. To understand risk propagation throughout the contractor network, we extend the celebrated Friedkin-Johnsen model and introduce a stochastic process to simulate principal failures across the network. From a theoretical perspective, we show that under natural monotonicity conditions on the contractor network, incorporating network effects leads to increases in the average risk for the surety organization. We further use data from a partnering insurance company to validate our findings, estimating an approximately 2% higher exposure when accounting for network effects.

q-fin.RM↗

arXiv2024

Neutrino mass ordering sensitivities at DUNE, HK and KNO in presence of scalar NSI

The limitations of the Standard Model in explaining neutrino masses and neutrino mixing lead to the exploration of frameworks beyond the Standard Model (BSM). The possibility of neutrinos interacting with fermions via a scalar mediator is one of the interesting prospects. The study of neutrino non-standard interactions (NSI) is a well-motivated phenomenological scenario to explore new physics beyond the Standard Model. These new interactions may alter the standard neutrino oscillation probabilities, potentially leading to observable effects in experiments. It also allows for the exploration of absolute neutrino masses via oscillation experiments. It can modify the oscillation probabilities, which in turn can affect the physics sensitivities in long-baseline experiments. The linear scaling of the effects of scalar NSI with matter density also motivates its exploration in long-baseline (LBL) experiments. We will present our study on the impact of a scalar-mediated NSI on the mass ordering (MO) sensitivities of three long-baseline neutrino experiments, i.e., DUNE, HK and KNO. We study the impact on MO sensitivities at these experiments assuming that scalar NSI parameters are present in nature and are known from other non-LBL experiments. The presence of scalar NSI can notably impact the MO sensitivities of these experiments. Furthermore, we analyze the potential synergy by combining data from DUNE with HK and HK+KNO, thereby exploring a broader parameter space.

hep-ph↗

arXiv2024

Scalar NSI: A unique tool for constraining absolute neutrino masses via $ν$-oscillations

In the standard interaction scenario, a direct measurement of absolute neutrino masses via neutrino oscillations is not feasible, as the oscillations depend only on the mass-squared differences. However, scalar non-standard interactions (SNSI) can introduce sub-dominant terms in the neutrino oscillation Hamiltonian that can directly affect the neutrino mass matrix, thereby making SNSI a unique tool for neutrino mass measurements. In this work, for the first time, we constrain the absolute masses of neutrinos by probing SNSI. We have explored the constraints on the lightest neutrino mass with different choices of $δ_{CP}$ and $θ_{23}$ for both neutrino mass hierarchies. We show that a bound on the neutrino mass can be induced in the presence of SNSI at DUNE. We find that the lightest neutrino mass can be constrained with $η_{ττ}$ for normal mass hierarchy irrespective of the octant of $θ_{23}$ and the value of the CP phase $δ_{CP}$. This study suggests that SNSI can serve as an interesting avenue to constrain the absolute neutrino masses in long-baseline neutrino experiments via neutrino oscillations.

hep-ph↗

arXiv2024

Neutrino Oscillations in Presence of Diagonal Elements of Scalar NSI: An Analytic Approach

Scalar Non-Standard Interactions (SNSI) in neutrinos can arise when a scalar mediator couples to both neutrinos and standard model fermions. This beyond the Standard Model (BSM) scenario is particularly interesting as the SNSI contribution appears as a density-dependent perturbation to the neutrino mass, rather than appearing as a matter-induced potential, and the neutrino oscillation probabilities uniquely depend on the absolute neutrino masses. In this work, we show the complex dependence of the SNSI contributions on the neutrino masses and discuss how the mass of the lightest neutrino would regulate any possible SNSI contribution in both mass ordering scenarios. We derive the analytic expressions for neutrino oscillation probabilities, employing the Cayley-Hamilton theorem, in the presence of diagonal elements of SNSI. The expressions are compact and shows explicit dependence on matter effects and the absolute neutrino masses. The analytic expressions calculated here allow us to obtain the dependence of the SNSI contribution on mass terms of the form $m_1 + m_2$, $m_2 - m_1$, $m_1c_{12}^2 + m_2s_{12}^2,$ $ m_1s_{12}^2 + m_2c_{12}^2$, and $m_3$. We then explore the non-trivial impact of neutrino mass ordering on the SNSI contribution. The dependence of the SNSI contribution on the 3$ν$ parameters is then thoroughly explored using our analytic expressions.

hep-ph↗

arXiv2024

Capturing Tie Strength with Algebraic Topology

The association between tie strength and social structure is a fundamental topic in the social sciences. We study this association by analyzing tie strength in higher-order networks, an increasingly relevant model which can encode group interactions between three or more individuals. First, we introduce three measures based on algebraic topology which characterize the network context and influence of an edge. Our experimental results across 15 datasets indicate that these measures outperform standard network proxies in estimating tie strength. We further find that these measures can replicate and explain a puzzle wherein certain bridging ties are surprisingly strong. We then consider a single centrality measure which combines the three initial measures, is highly inversely related to tie strength, and can be interpreted through an information exchange process which highlights ties that have access to useful information. In this sense, we are able to illuminate the information advantages of weak ties due to their network position.

cs.SI↗

arXiv2024

Impact of scalar NSI on the neutrino mass ordering sensitivity at DUNE, HK and KNO

The study of neutrino non-standard interactions (NSI) is a well-motivated phenomenological scenario to explore new physics beyond the Standard Model. The possible scalar coupling of neutrinos ($ν$) with matter is one of such new physics scenarios that appears as a sub-dominant effect that can impact the $ν$-oscillations in matter. The presence of scalar NSI introduces an additional contribution directly to the $ν$-mass matrix in the interaction Hamiltonian and subsequently to the $ν$-oscillations. This indicates that scalar NSI may have a significant impact on measurements related to $ν$-oscillations e.g. leptonic CP phase $(δ_{CP})$, $θ_{23}$ octant and neutrino mass ordering (MO). The linear scaling of the effects of scalar NSI with matter density also motivates its exploration in long-baseline (LBL) experiments. In this paper, we study the impact of a scalar-mediated NSI on the MO sensitivity of DUNE, HK and HK+KNO, which are upcoming LBL experiments. We study the impact on MO sensitivities at these experiments assuming that scalar NSI parameters are present in nature and is known from other non-LBL experiments. We observe that the presence of diagonal scalar NSI elements can significantly affect the $ν$-mass ordering sensitivities. We then also combine the data from DUNE with HK and HK+KNO to explore possible synergy among these experiments in a wider parameter space. We also observe a significant enhancement in the MO sensitivities for the combined analysis.

hep-ph↗

arXiv2024

The Sensitivity of DUNE in Presence of Off-Diagonal Scalar NSI Parameters

Scalar non-standard interactions (NSI) presents an exciting pathway for probing potential new physics that extends beyond the Standard Model (BSM). The scalar coupling of neutrinos with matter can appear as a sub-dominant effect that can impact the neutrino oscillation probabilities. The uniqueness of these interactions is that it can directly affect the neutrino mass matrix. This makes oscillations sensitive to the absolute neutrino mass. The effects of scalar NSI scales linearly with matter density which motivates its exploration in long-baseline sector. The presence of scalar NSI can influence the key measurements in the field of neutrino physics, including the precise determination of the leptonic CP phase ($δ_{CP}$), neutrino mass ordering and the octant of $θ_{23}$. The precise determination of $δ_{CP}$ is one of the major goals of DUNE, which is an upcoming long-baseline experiment. A better understanding of the impact of scalar NSI on CP measurement sensitivities is crucial for accurate interpretation of $δ_{CP}$ phase. In this work, we have explored the impact of the complex off-diagonal scalar NSI elements $η_{αβ}$ and their associated phases $ϕ_{αβ}$ on the CP-measurement sensitivities at DUNE. We have explored the impact of the neutrino mass scale on these sensitivities. We look for constraining these off-diagonal elements for different neutrino mass scales. We also explore their correlation with $δ_{CP}$, investigating potential degeneracies that can arise due to additional phases. We also perform a correlation study among different scalar NSI elements. We show that the inclusion of the complex scalar NSI elements can significantly modify the CP phase measurements.

hep-ph↗

arXiv2023

Investigating the effects of Lorentz Invariance Violation on the CP-sensitivities of the Deep Underground Neutrino Experiment

The neutrino oscillations offer great potential for probing new physics effects beyond the Standard Model. Any additional effect on neutrino oscillations can help understand the nature of these non-standard effects. The violation of fundamental symmetries may appear as new physics effects in various neutrino experiments. Lorentz symmetry is one such fundamental symmetry in nature, the violation of which implies a breakdown of space-time symmetry. The Lorentz Invariance Violation (LIV) is intrinsic in nature and its effects exist even in a vacuum. Neutrinos can be an intriguing probe for exploring such violations of Lorentz symmetry. The effect of violation of Lorentz Invariance can be explored through the impact on the neutrino oscillation probabilities. The effect of LIV is treated as a perturbation to the standard neutrino Hamiltonian considering the Standard Model Extension (SME) framework. In this work, we have probed the effect of LIV on the neutrino oscillation measurements considering the Deep Underground Neutrino Experiment (DUNE) as a case study. The inclusion of LIV affects various neutrino oscillation parameters as it modifies the standard neutrino oscillation probabilities. We looked into the capability of DUNE in constraining the LIV parameters and then explored the impact of CPT-violating LIV terms on the mass-induced neutrino oscillation probabilities. We have also probed the influence of LIV parameters on the CP-measurement sensitivity at DUNE.

hep-ph↗

arXiv2022

Link Partitioning on Simplicial Complexes Using Higher-Order Laplacians

Link partitioning is a popular approach in network science used for discovering overlapping communities by identifying clusters of strongly connected links. Current link partitioning methods are specifically designed for networks modelled by graphs representing pairwise relationships. Therefore, these methods are incapable of utilizing higher-order information about group interactions in network data which is increasingly available. Simplicial complexes extend the dyadic model of graphs and can model polyadic relationships which are ubiquitous and crucial in many complex social and technological systems. In this paper, we introduce a link partitioning method that leverages higher-order (i.e. triadic and higher) information in simplicial complexes for better community detection. Our method utilizes a novel random walk on links of simplicial complexes defined by the higher-order Laplacian--a generalization of the graph Laplacian that incorporates polyadic relationships of the network. We transform this random walk into a graph-based random walk on a lifted line graph--a dual graph in which links are nodes while nodes and higher-order connections are links--and optimize for the standard notion of modularity. We show that our method is guaranteed to provide interpretable link partitioning results under mild assumptions. We also offer new theoretical results on the spectral properties of simplicial complexes by studying the spectrum of the link random walk. Experiment results on real-world community detection tasks show that our higher-order approach significantly outperforms existing graph-based link partitioning methods.

cs.SI↗

arXiv2022

Generalizing Homophily to Simplicial Complexes

Group interactions occur frequently in social settings, yet their properties beyond pairwise relationships in network models remain unexplored. In this work, we study homophily, the nearly ubiquitous phenomena wherein similar individuals are more likely than random to form connections with one another, and define it on simplicial complexes, a generalization of network models that goes beyond dyadic interactions. While some group homophily definitions have been proposed in the literature, we provide theoretical and empirical evidence that prior definitions mostly inherit properties of homophily in pairwise interactions rather than capture the homophily of group dynamics. Hence, we propose a new measure, $k$-simplicial homophily, which properly identifies homophily in group dynamics. Across 16 empirical networks, $k$-simplicial homophily provides information uncorrelated with homophily measures on pairwise interactions. Moreover, we show the empirical value of $k$-simplicial homophily in identifying when metadata on nodes is useful for predicting group interactions, whereas previous measures are uninformative.

cs.SI↗

arXiv2022

Current Implicit Policies May Not Eradicate COVID-19

Successful predictive modeling of epidemics requires an understanding of the implicit feedback control strategies which are implemented by populations to modulate the spread of contagion. While this task of capturing endogenous behavior can be achieved through intricate modeling assumptions, we find that a population's reaction to case counts can be described through a second order affine dynamical system with linear control which fits well to the data across different regions and times throughout the COVID-19 pandemic. The model fits the data well both in and out of sample across the 50 states of the United States, with comparable $R^2$ scores to state of the art ensemble predictions. In contrast to recent models of epidemics, rather than assuming that individuals directly control the contact rate which governs the spread of disease, we assume that individuals control the rate at which they vary their number of interactions, i.e. they control the derivative of the contact rate. We propose an implicit feedback law for this control input and verify that it correlates with policies taken throughout the pandemic. A key takeaway of the dynamical model is that the "stable" point of case counts is non-zero, i.e. COVID-19 will not be eradicated under the current collection of policies and strategies, and additional policies are needed to fully eradicate it quickly. Hence, we suggest alternative implicit policies which focus on making interventions (such as vaccinations and mobility restrictions) a function of cumulative case counts, for which our results suggest a better possibility of eradicating COVID-19.

q-bio.PE↗

arXiv2022

Accurate Parameter Estimation for Risk-aware Autonomous Systems

Analysis and synthesis of safety-critical autonomous systems are carried out using models which are often dynamic. Two central features of these dynamic systems are parameters and unmodeled dynamics. This paper addresses the use of a spectral lines-based approach for estimating parameters of the dynamic model of an autonomous system. Existing literature has treated all unmodeled components of the dynamic system as sub-Gaussian noise and proposed parameter estimation using Gaussian noise-based exogenous signals. In contrast, we allow the unmodeled part to have deterministic unmodeled dynamics, which are almost always present in physical systems, in addition to sub-Gaussian noise. In addition, we propose a deterministic construction of the exogenous signal in order to carry out parameter estimation. We introduce a new tool kit which employs the theory of spectral lines, retains the stochastic setting, and leads to non-asymptotic bounds on the parameter estimation error. Unlike the existing stochastic approach, these bounds are tunable through an optimal choice of the spectrum of the exogenous signal leading to accurate parameter estimation. We also show that this estimation is robust to unmodeled dynamics, a property that is not assured by the existing approach. Finally, we show that under ideal conditions with no unmodeled dynamics, the proposed approach can ensure a $\tilde{O}(\sqrt{T})$ regret, matching existing literature. Experiments are provided to support all theoretical derivations, which show that the spectral lines-based approach outperforms the Gaussian noise-based method when unmodeled dynamics are present, in terms of both parameter estimation error and Regret obtained using the parameter estimates with a Linear Quadratic Regulator in feedback.

eess.SY↗

arXiv2022

Unifying Epidemic Models with Mixtures

The COVID-19 pandemic has emphasized the need for a robust understanding of epidemic models. Current models of epidemics are classified as either mechanistic or non-mechanistic: mechanistic models make explicit assumptions on the dynamics of disease, whereas non-mechanistic models make assumptions on the form of observed time series. Here, we introduce a simple mixture-based model which bridges the two approaches while retaining benefits of both. The model represents time series of cases and fatalities as a mixture of Gaussian curves, providing a flexible function class to learn from data compared to traditional mechanistic models. Although the model is non-mechanistic, we show that it arises as the natural outcome of a stochastic process based on a networked SIR framework. This allows learned parameters to take on a more meaningful interpretation compared to similar non-mechanistic models, and we validate the interpretations using auxiliary mobility data collected during the COVID-19 pandemic. We provide a simple learning algorithm to identify model parameters and establish theoretical results which show the model can be efficiently learned from data. Empirically, we find the model to have low prediction error. The model is available live at covidpredictions.mit.edu. Ultimately, this allows us to systematically understand the impacts of interventions on COVID-19, which is critical in developing data-driven solutions to controlling epidemics.

stat.ML↗

arXiv2020

Two Burning Questions on COVID-19: Did shutting down the economy help? Can we (partially) reopen the economy without risking the second wave?

As we reach the apex of the COVID-19 pandemic, the most pressing question facing us is: can we even partially reopen the economy without risking a second wave? We first need to understand if shutting down the economy helped. And if it did, is it possible to achieve similar gains in the war against the pandemic while partially opening up the economy? To do so, it is critical to understand the effects of the various interventions that can be put into place and their corresponding health and economic implications. Since many interventions exist, the key challenge facing policy makers is understanding the potential trade-offs between them, and choosing the particular set of interventions that works best for their circumstance. In this memo, we provide an overview of Synthetic Interventions (a natural generalization of Synthetic Control), a data-driven and statistically principled method to perform what-if scenario planning, i.e., for policy makers to understand the trade-offs between different interventions before having to actually enact them. In essence, the method leverages information from different interventions that have already been enacted across the world and fits it to a policy maker's setting of interest, e.g., to estimate the effect of mobility-restricting interventions on the U.S., we use daily death data from countries that enforced severe mobility restrictions to create a "synthetic low mobility U.S." and predict the counterfactual trajectory of the U.S. if it had indeed applied a similar intervention. Using Synthetic Interventions, we find that lifting severe mobility restrictions and only retaining moderate mobility restrictions (at retail and transit locations), seems to effectively flatten the curve. We hope this provides guidance on weighing the trade-offs between the safety of the population, strain on the healthcare system, and impact on the economy.

econ.EM↗