Search Results for author: Joshua Yao-Yu Lin

Found 11 papers, 5 papers with code

Strong Gravitational Lensing Parameter Estimation with Vision Transformer

1 code implementation9 Oct 2022 Kuan-Wei Huang, Geoff Chih-Fan Chen, Po-Wen Chang, Sheng-Chieh Lin, Chia-Jung Hsu, Vishal Thengane, Joshua Yao-Yu Lin

Quantifying the parameters and corresponding uncertainties of hundreds of strongly lensed quasar systems holds the key to resolving one of the most important scientific questions: the Hubble constant ($H_{0}$) tension.

VLBInet: Radio Interferometry Data Classification for EHT with Neural Networks

no code implementations14 Oct 2021 Joshua Yao-Yu Lin, Dominic W. Pesce, George N. Wong, Ajay Uppili Arasanipalai, Ben S. Prather, Charles F. Gammie

Combined with other astronomical data, these images constrain the mass and spin of the hole as well as the accretion rate and magnetic flux trapped on the hole.

Classification Image Reconstruction +1

AGNet: Weighing Black Holes with Deep Learning

1 code implementation17 Aug 2021 Joshua Yao-Yu Lin, Sneh Pandya, Devanshi Pratap, Xin Liu, Matias Carrasco Kind, Volodymyr Kindratenko

We find a 1$\sigma$ scatter of 0. 37 dex between the predicted SMBH mass and the fiducial virial mass estimate based on SDSS single-epoch spectra, which is comparable to the systematic uncertainty in the virial mass estimate.

Time Series Time Series Analysis

A Deep Learning Approach for Active Anomaly Detection of Extragalactic Transients

1 code implementation22 Mar 2021 V. Ashley Villar, Miles Cranmer, Edo Berger, Gabriella Contardo, Shirley Ho, Griffin Hosseinzadeh, Joshua Yao-Yu Lin

There is a shortage of multi-wavelength and spectroscopic followup capabilities given the number of transient and variable astrophysical events discovered through wide-field, optical surveys such as the upcoming Vera C. Rubin Observatory.

Anomaly Detection

Large-Scale Gravitational Lens Modeling with Bayesian Neural Networks for Accurate and Precise Inference of the Hubble Constant

2 code implementations30 Nov 2020 Ji Won Park, Sebastian Wagner-Carena, Simon Birrer, Philip J. Marshall, Joshua Yao-Yu Lin, Aaron Roodman

The computation time for the entire pipeline -- including the training set generation, BNN training, and $H_0$ inference -- translates to 9 minutes per lens on average for 200 lenses and converges to 6 minutes per lens as the sample size is increased.

Learning Principle of Least Action with Reinforcement Learning

no code implementations24 Nov 2020 Zehao Jin, Joshua Yao-Yu Lin, Siao-Fong Li

In the case of classical mechanics, nature favors the object to move along the path according to the integral of the Lagrangian, called the action $\mathcal{S}$.

Q-Learning reinforcement-learning +1

Hunting for Dark Matter Subhalos in Strong Gravitational Lensing with Neural Networks

no code implementations24 Oct 2020 Joshua Yao-Yu Lin, Hang Yu, Warren Morningstar, Jian Peng, Gilbert Holder

Dark matter substructures are interesting since they can reveal the properties of dark matter.

Cosmology and Nongalactic Astrophysics Computational Physics

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