arXiv subjects

Kalyani Desikan

Publications and source records attributed to Kalyani Desikan.

7 recordsLinked to original sources

arXiv2026

Physics-Informed Neural Networks for Solving Two-Flavor Neutrino Oscillations in Vacuum and Matter Environments for Atmospheric and Reactor Neutrinos

Neutrino oscillations provide crucial insights into fundamental particle physics, with two-flavor approximations effectively describing reactor and atmospheric phenomena. This paper investigates the application of Physics-Informed Neural Networks (PINNs), which have several advantages over traditional solvers. Traditional methods typically depend on mesh-based techniques or dimensionality reduction approaches to solve the governing differential equations for neutrino evolution in vacuum and matter environments. We review the theoretical framework, including vacuum mixing and the Mikheyev-Smirnov-Wolfenstein (MSW) effect in matter, and demonstrate PINN implementations for vacuum and constant-density profiles. This Machine learning based approach for reactor (low-energy) and atmospheric (high-energy) neutrinos shows high precision similar to analytical solutions, with mean squared errors of the order of 10^{-3}~10^{-4}. We have also discussed the robustness of PINNs in solving coupled ODE systems, along with future extensions to three-flavor effects.

hep-ph↗

arXiv2026

Conditional Wasserstein GAN for Simulating Neutrino Event Summaries using Incident Energy of Electron Neutrinos

Event simulation for electron neutrino interactions plays a foundational role in precision measurements in particle physics experiments, yet the computational demand of traditional Monte Carlo methods remains a significant challenge, especially for complete, high-dimensional event reconstruction. In this study, we present a generative model based on the Conditional Wasserstein Generative Adversarial Network (CW-GAN) framework. This architecture is conditioned on the input neutrino energy. It utilizes a Wasserstein loss function, stabilized by a gradient penalty, to learn the complex mapping from a latent space to structured kinematic data. Our model is tailored to replicate the full multidimensional kinematics of electron neutrino interactions as described by the GENIE event generator. Our focus is specifically on the Inverse Beta Decay (IBD-CC), Neutral Current (NC), and nue-e-elastic scattering processes (NuEElastic), spanning an energy window of 10-31 MeV. Our approach abandons variable reduction schemes and instead generates the entire summary ntuple, enabling holistic event-by-event modeling. Training is performed separately for each of the three interaction types, with rigorous convergence monitoring over 100-300 epochs per channel. We perform a rigorous quantitative validation against held-out GENIE test datasets. The generated samples demonstrate fidelity, reproducing the 1D marginal distributions for all kinematic variables with statistical compatibility, and successfully capturing the complex non-linear correlations between them. This work offers a scalable and efficient alternative to traditional MC event generation, providing full-spectrum kinematic simulation for key electron neutrino interaction channels while drastically reducing computational overhead.

hep-ph↗

arXiv2023

Eigenvalue Interlacing of Bipartite Graphs and Construction of Expander Code using Vertex-split of a Bipartite Graph

The second largest eigenvalue of a graph is an important algebraic parameter which is related with the expansion, connectivity and randomness properties of a graph. Expanders are highly connected sparse graphs. In coding theory, Expander codes are Error Correcting codes made up of bipartite expander graphs. In this paper, first we prove the interlacing of the eigenvalues of the adjacency matrix of the bipartite graph with the eigenvalues of the bipartite quotient matrices of the corresponding graph matrices. Then we obtain bounds for the second largest and second smallest eigenvalues. Since the graph is bipartite, the results for Laplacian will also hold for Signless Laplacian matrix. We then introduce a new method called vertex-split of a bipartite graph to construct asymptotically good expander codes with expansion factor $\frac{D}{2}<α< D$ and $ε<\frac{1}{2}$ and prove a condition for the vertex-split of a bipartite graph to be $k-$connected with respect to $λ_{2}.$ Further, we prove that the vertex-split of $G$ is a bipartite expander. Finally, we construct an asymptotically good expander code whose factor graph is a graph obtained by the vertex-split of a bipartite graph.

math.CO↗

arXiv2018

A Maximal Heterogeneity Based Clustering Approach for Obtaining Samples

Medical and social sciences demand sampling techniques which are robust, reliable, replicable and have the least dissimilarity between the samples obtained. Majority of the applications of sampling use randomized sampling, albeit with stratification where applicable. The randomized technique is not consistent, and may provide different samples each time, and the different samples themselves may not be similar to each other. In this paper, we introduce a novel non-statistical no-replacement sampling technique called Wobbly Center Algorithm, which relies on building clusters iteratively based on maximizing the heterogeneity inside each cluster. The algorithm works on the principle of stepwise building of clusters by finding the points with the maximal distance from the cluster center. The obtained results are validated statistically using Analysis of Variance tests by comparing the samples obtained to check if they are representative of each other. The obtained results generated from running the Wobbly Center algorithm on benchmark datasets when compared against other sampling algorithms indicate the superiority of the Wobbly Center Algorithm.

cs.LG↗

arXiv2018

Dynamic Feature Scaling for K-Nearest Neighbor Algorithm

Nearest Neighbors Algorithm is a Lazy Learning Algorithm, in which the algorithm tries to approximate the predictions with the help of similar existing vectors in the training dataset. The predictions made by the K-Nearest Neighbors algorithm is based on averaging the target values of the spatial neighbors. The selection process for neighbors in the Hermitian space is done with the help of distance metrics such as Euclidean distance, Minkowski distance, Mahalanobis distance etc. A majority of the metrics such as Euclidean distance are scale variant, meaning that the results could vary for different range of values used for the features. Standard techniques used for the normalization of scaling factors are feature scaling method such as Z-score normalization technique, Min-Max scaling etc. Scaling methods uniformly assign equal weights to all the features, which might result in a non-ideal situation. This paper proposes a novel method to assign weights to individual feature with the help of out of bag errors obtained from constructing multiple decision tree models.

cs.LG↗

arXiv2017

Language properties and Grammar of Parallel and Series Parallel Languages

In this paper we have defined the language theoretical properties of Parallel languages and series parallel languages. Parallel languages and Series parallel languages play vital roles in parallel processing and many applications in computer programming. We have defined regular expressions and context free grammar for parallel and series parallel languages based on sequential languages [2]. We have also discussed the recognizability of parallel and series parallel languages using regular expression and regular grammar.

cs.FL↗

arXiv2015

Experimental Estimation of Number of Clusters Based on Cluster Quality

Text Clustering is a text mining technique which divides the given set of text documents into significant clusters. It is used for organizing a huge number of text documents into a well-organized form. In the majority of the clustering algorithms, the number of clusters must be specified apriori, which is a drawback of these algorithms. The aim of this paper is to show experimentally how to determine the number of clusters based on cluster quality. Since partitional clustering algorithms are well-suited for clustering large document datasets, we have confined our analysis to a partitional clustering algorithm.

cs.IR↗