9 citations · 17 across the 9 of their papers we have counts for
10 papers
Machine Learning and the SKA for Cosmic Dawn and the Epoch of Reionization
Anshuman Acharya, Michele Bianco, Daniela Breitman +18
When operational, the SKA will generate unprecedented amounts of data and provide exquisite sensitivity for 21 cm tomography of Cosmic Dawn (CD) and the Epoch of Reionization (EoR)…
Can Microcanonical Langevin Dynamics Leverage Mini-Batch Gradient Noise?
Emanuel Sommer, Kangning Diao, Jakob Robnik +2
Scaling inference methods such as Markov chain Monte Carlo to high-dimensional models remains a central challenge in Bayesian deep learning. A promising recent proposal, microcanon…
Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps
Kangning Diao, Biwei Dai, Uros Seljak
Simulation-based inference provides a powerful framework for extracting rich information from nonlinear scales in current and upcoming cosmological surveys, and ensuring its robust…
Square Kilometre Array Science Data Challenge 3a: foreground removal for an EoR experiment
A. Bonaldi, P. Hartley, R. Braun +179
We present and analyse the results of the Science data challenge 3a (SDC3a, https://sdc3.skao.int/challenges/foregrounds), an EoR foreground-removal community-wide exercise organis…
Multi-fidelity emulator for large-scale 21 cm lightcone images: a few-shot transfer learning approach with generative adversarial network
Kangning Diao, Yi Mao
Emulators using machine learning techniques have emerged to efficiently generate mock data matching the large survey volume for upcoming experiments, as an alternative approach to…
: A Differentiable and GPU-accelerated Synchrotron Simulation Package
Kangning Diao, Zack Li, Richard D. P. Grumitt +1
We introduce synax, a novel library for automatically differentiable simulation of Galactic synchrotron emission. Built on the JAX framework, synax leverages JAX's capabilities, in…
Modeling Foreground Spatial Variations in 21 cm Gaussian Process Component Separation
Kangning Diao, Richard D. P. Grumitt, Yi Mao
Gaussian processes (GPs) have been extensively utilized as nonparametric models for component separation in 21 cm data analyses. This exploits the distinct spectral behavior of the…
Reionization Parameter Inference from 3D Minkowski Functionals of the 21 cm Signals
Kangning Diao, Zhaoting Chen, Xuelei Chen +1
The Minkowski Functionals (MFs), a set of topological summary statistics, have emerged as a powerful tool for extracting non-Gaussian information. We investigate the prospect of co…
Multi-fidelity Emulator for Cosmological Large Scale 21 cm Lightcone Images: a Few-shot Transfer Learning Approach with GAN
Kangning Diao, Yi Mao
Large-scale numerical simulations () of cosmic reionization are required to match the large survey volume of the upcoming Square Kilometre Array (SKA). We pres…
Can Diffusion Model Conditionally Generate Astrophysical Images?
Xiaosheng Zhao, Yuan-Sen Ting, Kangning Diao +1
Generative adversarial networks (GANs) are frequently utilized in astronomy to construct an emulator of numerical simulations. Nevertheless, training GANs can prove to be a precari…