Young isolated neutron stars (INS) most commonly manifest themselves as rotationally powered pulsars (RPPs) which involve conventional radio pulsars as well as gamma-ray pulsars (GRPs) and rotating radio transients (RRATs). Some other young INS families manifest themselves as anomalous X-ray pulsars (AXPs) and soft gam…
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9 results for “pulsars”
Quantum machine learning improves pulsar classification in radio astronomy.
problem Improving classification of pulsars in radio astronomy.
method Used a Born machine (quantum neural network) with a single-qubit architecture.
result Comparable accuracies to classical machine learning methods achieved.
Novel LSTM network predicts pulsar timing residuals with few-shot data.
problem Predicting pulsar timing residuals with limited data.
method Long Short-Term Memory (LSTM) network optimized with model-agnostic meta-learning and particle swarm optimization.
result Robust generalization and accurate predictions across high-frequency test domains with minimal data.
New method uses SBI to infer magnetorotational properties of isolated pulsars.
problem Constrain magnetorotational properties of isolated Galactic radio pulsars.
method Combines population synthesis with SBI to model neutron star birth and evolution.
result Inferred , for lognormal distributions.
New findings on algebraic structure of hyperbolic graph braid groups.
problem Classifying and understanding the algebraic structure of hyperbolic graph braid groups.
method Analyzing specific graph types (sun and pulsar graphs) and proving theorems about their braid groups.
result 3-strand braid groups of sun graphs are free, while most pulsar graphs contain surface subgroups.
Accelerates pulsar light curve inference with learned representations and optimization.
problem Computational expense of Markov chain Monte Carlo methods for posterior inference.
method Combining U-Net latent representations with local simulator-guided optimization.
result 120x reduction in inference time (24 hours to 12 minutes) with accuracy preserved.
Unified Bayesian framework for PTA data analysis tackles hierarchical model issues.
problem Hierarchical Bayesian modeling challenges in PTA data analysis.
method Reparameterization strategy using Normalizing Flows (NFs) and i-nessai nested sampler.
result Improved statistical robustness and computational efficiency in PTA analysis.
Machine learning models classify celestial objects like pulsars and black holes.
problem Classifying high-energy celestial objects using photometric data.
method Applied tree-based models and RNN to classify pulsars and black holes.
result RNN showed potential for real-time object discrimination and classification.
Kichenassamy's work spans theoretical physics, from relativity to applications.
problem Clarifying the postulational basis of relativity theories.
method Introducing the C-equivalence principle to replace the strong equivalence principle.
result New insights into measurements in both special and general relativity.