Search Results for author: Tim Schrabback

Found 4 papers, 3 papers with code

An improved model of Charge Transfer Inefficiency and correction algorithm for the Hubble Space Telescope

1 code implementation6 Jan 2014 Richard Massey, Tim Schrabback, Oliver Cordes, Ole Marggraf, Holger Israel, Lance Miller, David Hall, Mark Cropper, Thibaut Prod'homme, Sami-Matias Niemi

Read noise is added after charge transfer so does not get trailed - but it is incorrectly untrailed during post-processing.

Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics

CFHTLenS: A Weak Lensing Shear Analysis of the 3D-Matched-Filter Galaxy Clusters

1 code implementation11 Sep 2014 Jes Ford, Ludovic van Waerbeke, Martha Milkeraitis, Clotilde Laigle, Hendrik Hildebrandt, Thomas Erben, Catherine Heymans, Henk Hoekstra, Thomas Kitching, Yannick Mellier, Lance Miller, Ami Choi, Jean Coupon, Liping Fu, Michael J. Hudson, Konrad Kuijken, Naomi Robertson, Barnaby Rowe, Tim Schrabback, Malin Velander

We present the cluster mass-richness scaling relation calibrated by a weak lensing analysis of >18000 galaxy cluster candidates in the Canada-France-Hawaii Telescope Lensing Survey (CFHTLenS).

Cosmology and Nongalactic Astrophysics

Weak-lensing shear measurement with machine learning: teaching artificial neural networks about feature noise

1 code implementation5 Jul 2018 Malte Tewes, Thibault Kuntzer, Reiko Nakajima, Frédéric Courbin, Hendrik Hildebrandt, Tim Schrabback

A challenging peculiarity of this ML application is the combination of the noisiness of the input features and the requirements on the accuracy of the inverse regression.

Cosmology and Nongalactic Astrophysics Machine Learning

Probabilistic Mass Mapping with Neural Score Estimation

no code implementations14 Jan 2022 Benjamin Remy, Francois Lanusse, Niall Jeffrey, Jia Liu, Jean-Luc Starck, Ken Osato, Tim Schrabback

We introduce a novel methodology allowing for efficient sampling of the high-dimensional Bayesian posterior of the weak lensing mass-mapping problem, and relying on simulations for defining a fully non-Gaussian prior.

Uncertainty Quantification

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