Constraints on the rate of supernovae lasting for more than a year from Subaru/Hyper Suprime-Cam

Kavli Affiliate: Naoki Yasuda | First 5 Authors: Takashi J. Moriya, Ji-an Jiang, Naoki Yasuda, Mitsuru Kokubo, Kojiro Kawana | Summary: Some supernovae such as pair-instability supernovae are predicted to have the duration of more than a year in the observer frame. To constrain the rates of supernovae lasting for more than a year, we […]


Continue.. Constraints on the rate of supernovae lasting for more than a year from Subaru/Hyper Suprime-Cam

A Comprehensive X-ray Report on AT2019wey

Kavli Affiliate: Ronald A. Remillard | First 5 Authors: Yuhan Yao, S. R. Kulkarni, K. C. Gendreau, Gaurava K. Jaisawal, Teruaki Enoto | Summary: Here, we present MAXI, SWIFT, NICER, NuSTAR and Chandra observations of the X-ray transient AT2019wey (SRGA J043520.9+552226, SRGE J043523.3+552234). From spectral and timing analyses we classify it as a Galactic low-mass […]


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PSR J1709-4429’s Proper Motion and its Relationship to SNR G343.1-2.3

Kavli Affiliate: Roger W. Romani | First 5 Authors: Martijn de Vries, Roger W. Romani, Oleg Kargaltsev, George Pavlov, Bettina Posselt | Summary: We have obtained a deep 670 ks CXO ACIS image of the remarkable pulsar wind nebula (PWN) of PSR J1709-4429, in 4 epochs during 2018-2019. Comparison with an archival 2004 data set […]


Continue.. PSR J1709-4429’s Proper Motion and its Relationship to SNR G343.1-2.3

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

Kavli Affiliate: Philip J. Marshall | First 5 Authors: Ji Won Park, Sebastian Wagner-Carena, Simon Birrer, Philip J. Marshall, Joshua Yao-Yu Lin | Summary: We investigate the use of approximate Bayesian neural networks (BNNs) in modeling hundreds of time-delay gravitational lenses for Hubble constant ($H_0$) determination. Our BNN was trained on synthetic HST-quality images of […]


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

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

Kavli Affiliate: Philip J. Marshall | First 5 Authors: Ji Won Park, Sebastian Wagner-Carena, Simon Birrer, Philip J. Marshall, Joshua Yao-Yu Lin | Summary: We investigate the use of approximate Bayesian neural networks (BNNs) in modeling hundreds of time-delay gravitational lenses for Hubble constant ($H_0$) determination. Our BNN was trained on synthetic HST-quality images of […]


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

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

Kavli Affiliate: Philip J. Marshall | First 5 Authors: Ji Won Park, Sebastian Wagner-Carena, Simon Birrer, Philip J. Marshall, Joshua Yao-Yu Lin | Summary: We investigate the use of approximate Bayesian neural networks (BNNs) in modeling hundreds of time-delay gravitational lenses for Hubble constant ($H_0$) determination. Our BNN was trained on synthetic HST-quality images of […]


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

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

Kavli Affiliate: Philip J. Marshall | First 5 Authors: Ji Won Park, Sebastian Wagner-Carena, Simon Birrer, Philip J. Marshall, Joshua Yao-Yu Lin | Summary: We investigate the use of approximate Bayesian neural networks (BNNs) in modeling hundreds of time-delay gravitational lenses for Hubble constant ($H_0$) determination. Our BNN was trained on synthetic HST-quality images of […]


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

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

Kavli Affiliate: Philip J. Marshall | First 5 Authors: Ji Won Park, Sebastian Wagner-Carena, Simon Birrer, Philip J. Marshall, Joshua Yao-Yu Lin | Summary: We investigate the use of approximate Bayesian neural networks (BNNs) in modeling hundreds of time-delay gravitational lenses for Hubble constant ($H_0$) determination. Our BNN was trained on synthetic HST-quality images of […]


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

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

Kavli Affiliate: Philip J. Marshall | First 5 Authors: Ji Won Park, Sebastian Wagner-Carena, Simon Birrer, Philip J. Marshall, Joshua Yao-Yu Lin | Summary: We investigate the use of approximate Bayesian neural networks (BNNs) in modeling hundreds of time-delay gravitational lenses for Hubble constant ($H_0$) determination. Our BNN was trained on synthetic HST-quality images of […]


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