31 citations · 43 across the 8 of their papers we have counts for
8 papers
Multipolar Magnetic-Field Inference for PSR J0740+6620 with Neural-Network-Accelerated NICER Pulse-Profile Modeling
Farhana Taiyebah, Constantinos Kalapotharakos, Greg Olmschenk +6
We investigate the multipolar surface magnetic-field structure of the high-mass millisecond pulsar PSR J0740+6620 using the 32-bin bolometric NICER pulse profile of Dittmann et al.…
Accelerating Posterior Inference from Pulsar Light Curves via Learned Latent Representations and Local Simulator-Guided Optimization
Farhana Taiyebah, Abu Bucker Siddik, Indronil Bhattacharjee +4
Posterior inference from pulsar observations in the form of light curves is commonly performed using Markov chain Monte Carlo methods, which are accurate but computationally expens…
Degeneracy-Aware Pulsar Parameter Estimation from Light Curves via Deep Learning and Test-Time Optimization
Abu Bucker Siddik, Diane Oyen, Soumi De +2
Probing properties of neutron stars from photometric observations of these objects helps us answer crucial questions at the forefront of multi-messenger astronomy, such as, what is…
SPUS: A Lightweight and Parameter-Efficient Foundation Model for PDEs
Abu Bucker Siddik, Diane Oyen, Alexander Most +2
We introduce Small PDE U-Net Solver (SPUS), a compact and efficient foundation model (FM) designed as a unified neural operator for solving a wide range of partial differential equ…
Estimation of non-uniform motion blur using a patch-based regression convolutional neural network (CNN)
Luis G. Varela, Laura E. Boucheron, Steven Sandoval +2
The non-uniform blur of atmospheric turbulence can be modeled as a superposition of linear motion blur kernels at a patch level. We propose a regression convolutional neural networ…
Estimation of motion blur kernel parameters using regression convolutional neural networks
Luis G. Varela, Laura E. Boucheron, Steven Sandoval +2
Many deblurring and blur kernel estimation methods use a maximum a posteriori (MAP) approach or deep learning-based classification techniques to sharpen an image and/or predict the…
Deep Learning Estimation of Modified Zernike Coefficients and Recovery of Point Spread Functions in Turbulence
Abu Bucker Siddik, Steven Sandoval, David Voelz +2
Recovering the turbulence-degraded point spread function from a single intensity image is important for a variety of imaging applications. Here, a deep learning model based on a co…
WIDEFT: A Corpus of Radio Frequency Signals for Wireless Device Fingerprint Research
Abu Bucker Siddik, Dawson Drake, Thomas Wilkinson +3
Wireless network security may be improved by identifying networked devices via traits that are tied to hardware differences, typically related to unique variations introduced in th…