arXiv subjects

Amitesh Singh

Publications and source records attributed to Amitesh Singh.

7 recordsLinked to original sources

arXiv2026

Path-dependency and emergent computing under vectorial driving

The sequential response of frustrated materials-ranging from crumpled sheets and amorphous media to metamaterials-reveals their memory effects and emergent computational potential. Despite their spatial extension, most studies rely on a single global stimulus, such as compression, effectively reducing the problem to scalar driving. Here, we introduce vectorial driving of frustrated materials by applying multiple spatially localized stimuli to explore path-dependent, sequential responses. We uncover a wealth of phenomena absent in scalar driving, including non-Abelian responses, mixed-mode behavior, and chiral loop transients. We show that fold singularities connect three states -- ancestor, descendant, and sibling. This recurring pattern serves as the elementary building block of all sequential paths. We then introduce three levels of description of sequential, path-dependent responses. At the most fundamental level, path-dependent transition graphs (pt-graphs) and strain maps capture the response under arbitrary vectorial driving and connect pathways to the underlying singularities. They provide a complete description analogous to transition graphs (t-graphs) for scalar driving. However, as pt-graphs and strain maps become unwieldy for high-dimensional driving, we introduce b-graphs -- graphs whose nodes and transitions encode the systems response to binarized vectorial driving. These present a less complete but much simpler second-level description by restricting attention to binary input and their induced transitions. Finally, we introduce graph-based motifs that enable a systematic analysis of b-graphs. As statistical measures of pathway complexity, these motifs can be obtained in systems of any size or complexity. Our work paves the way for strategies to explore, harness, and understand complex materials and memory, while advancing embodied intelligence and in-materia computing.

cond-mat.soft↗

arXiv2025

Evolution of precessing binary black holes on eccentric orbits using orbit-averaged evolution equations

The most general bound binary black hole (BBH) system has an eccentric orbit and precessing spins. The detection of such a system with significant eccentricity close to the merger would be a clear signature of dynamical formation. In order to study such systems, it is important to be able to evolve their spins and eccentricity from the larger separations at which the binary formed to the smaller separations at which it is detected, or vice versa. Knowledge of the precessional evolution of the binary's orbital angular momentum can also be used to twist up aligned-spin eccentric waveform models to create a spin-precessing eccentric waveform model. In this paper, we present a new publicly available code to evolve eccentric, precessing BBHs using orbit-averaged post-Newtonian (PN) equations from the literature. The spin-precession dynamics is 2PN accurate, i.e., with the leading spin-orbit and spin-spin corrections. The evolution of orbital parameters (orbital frequency, eccentricity, and periastron precession), which follow the quasi-Keplerian parametrization, is 3PN accurate in the point particle terms and includes the leading order spin-orbit and spin-spin effects. All the spin-spin terms include the quadrupole-monopole interaction. The eccentricity enhancement functions in the fluxes use the high-accuracy hyperasymptotic expansions from Loutrel and Yunes [Classical Quantum Gravity {\bf 34} 044003 (2017)]. We discuss various features of the code and study the evolution of the orbital and spin-precession parameters of eccentric, precessing BBHs. In particular, we study the dependence of the spin morphologies on eccentricity, where we find that the transition point from one spin morphology to another can depend nonmonotonically on eccentricity, and the fraction of binaries in a given morphology at a given point in the evolution of a population depends on the instantaneous eccentricity.

gr-qc↗

arXiv2025

Tracing the evolution of eccentric precessing binary black holes: a hybrid approach

To describe a general bound binary black hole system, we need to consider orbital eccentricity and the misalignment of black holes' spin vectors with respect to the orbital angular momentum. While binary black holes produced through many formation channels have negligible eccentricity close to merger, they often have a non-negligible eccentricity at formation, and dynamical interactions could produce binaries with non-negligible eccentricity in the bands of current and proposed gravitational-wave (GW) detectors. Another quantity that carries information about the formation channel is the angle between each black hole's spin vector and the binary's orbital angular momentum (referred to as the spin tilt) at formation. The spin tilts inferred in GW astronomy are usually those when the binary is in the band of a GW detector, but these can differ significantly from those at formation. Therefore, it is necessary to evolve the binary back in time to compute the tilts at formation. For many formation scenarios, the tilts in the formal limit of infinite orbital angular momentum, also known as tilts at infinity, are a good approximation to those at formation. We thus generalize the publicly available \texttt{tilts\_at\_infinity} code to compute the tilts at infinity for eccentric, spin-precessing binaries. This code employs hybrid post-Newtonian evolution, starting with orbit-averaged evolution for higher frequencies and then transitioning to precession-averaged evolution to compute the tilts at infinity. We find that the transition frequency used in the quasicircular case still gives acceptably small errors in the eccentric case, and show that eccentricity and hybrid evolution both have a significant effect on the tilts at infinity for many binaries. Finally, we give examples of cases where the tilts at infinity are and are not a good approximation to the tilts at formation in the eccentric case.

astro-ph.HE↗

arXiv2022

Search for Cosmological time dilation from Gamma-Ray Bursts -- A 2021 status update

We carry out a search for signatures of cosmological time dilation in the light curves of Gamma Ray Bursts (GRBs), detected by the Neil Gehrels Swift Observatory. For this purpose, we calculate two different durations ($T_{50}$ and $T_{90}$) for a sample of 247 GRBs in the fixed rest frame energy interval of 140-350 keV, similar to Zhang et al. We then carry out a power law-based regression analysis between the durations and redshifts. This search is done using both the unbinned as well as the binned data, where both the weighted mean and the geometric mean was used. For each analysis, we also calculate the intrinsic scatter to determine the tightness of the relation. We find that weighted mean-based binned data for long GRBs and the geometric mean-based binned data is consistent with the cosmological time dilation signature, whereas the analyses using unbinned durations show a very large scatter. We also make our analysis codes and the procedure for obtaining the light curves and estimation of $T_{50}$/$T_{90}$ publicly available.

astro-ph.HE↗

arXiv2021

Estimating dynamical parameters of two interacting galaxies using Deep Learning

The science behind galaxy interaction and mergers has a fundamental role and gives us an insight into galaxy formation and its evolution. Fluctuating angular momentum is responsible for extraordinary events like polar rings, tidal tails, and ripples. To study different phenomena related to galaxy interactions, various parameters like the mass ratio of the interacting galaxy, orbital parameters, mass distribution, morphologies are required. Convolutional Neural Networks (CNN) are widely used to classify image data. Thus, we used CNN as our approach to the problem. In this work, we will be using data from state-of-the-art magneto-hydrodynamic simulations of galaxy mergers from the GalMer database at different dynamical parameters using image snapshots of merging pairs of galaxies and feeding them to our Deep Learning model (ResNet). The dynamical parameters we are aiming for; would be spin, relative inclination ($i$), viewing angle ($θ$), and azimuthal angle ($ϕ$). We aim to download bulk data using the web scraping method. The first approach is to create different combinations of these parameters to form 60 classes. Feeding the data into the model, we achieved 93.63% accuracy. As we received good results in minute classification, we moved to our second approach, regression. Here the model can predict the continuous and exact values of the dynamical parameters. We have achieved a 99.86% R-squared value and the mean squared error of 0.0833 on testing data. In the end, we used data from Sloan Digital Sky Survey to test our trained model on some real images.

astro-ph.GA↗

arXiv2021

Deep Learning for estimating parameters of Gravitational Waves

In recent years, improvements in Deep Learning (DL) techniques towards Gravitational Wave (GW) astronomy have led to a significant rise in the development of various classification algorithms that have been successfully employed to extract GWs of binary blackhole merger events from noisy time-series data. However, the success of these models is constrained by the length of time-sample and the class of GW source: binary blackhole and neutron star binaries to some extent. In this work, we intended to advance the boundaries of DL techniques using Convolutional Neural Networks, to go beyond binary classification and predict the physical parameters of the events. We aim to propose an alternative method that can be employed for realtime detection and parameter prediction. The DL model we present has been trained on 12s of data to predict the GW source parameters if detected. During training, the maximum accuracy attained was 90.93%, with a validation accuracy of 89.97%.

astro-ph.HE↗

arXiv2021

A machine learning-based approach towards the improvement of SNR of pulsar signals

Many pulsar folding algorithms are currently deployed to generate strong SNRs for the total intensity profiles. But they require large observation times to improve the SNR effectively. New approaches to de-noise the pulsar total intensity data have sprung up over the years, powered by Machine learning and Deep learning algorithms. In the current work, efforts are made to implement the currently proposed supervised machine learning models, such as ensembling techniques like Decision Tree Regressor, Random Forest Regressor, Adaboost Regressor, Gradient Boosting Regressor (GBR), K-Nearest Neighbours(KNN), and Support Vector Regressor (SVR) to find out the best possible algorithm which can work over a variety of pulsars from the EPN database of pulsars. All the data used in this work is extracted from the European Pulsar Network (EPN) database of pulsar profiles. The training dataset is obtained by post-processing the pulsar profile data from the EPN database hand testing is performed on a preselected portion of the original data. The results are obtained by testing the above algorithms for 10 different pulsars, including some historically significant ones, and the predicted profiles are plotted. We find that Gradient boosting regressor works the best in denoising pulsar data, followed closely by KNN regressor. This work also emphasizes that there is a reduction in the number of periods of folding by 35-40\% when a combination of machine learning models with the existing pulsar folding techniques like Fast Folding Algorithm(FFA) is employed, which in turn can further reduce the pulsar observation times for the telescopes hunting for pulsars today.

astro-ph.HE↗