no code implementations • 13 Dec 2023 • Timothy D. Gebhard, Jonas Wildberger, Maximilian Dax, Daniel Angerhausen, Sascha P. Quanz, Bernhard Schölkopf
Atmospheric retrievals (AR) characterize exoplanets by estimating atmospheric parameters from observed light spectra, typically by framing the task as a Bayesian inference problem.
1 code implementation • 6 Sep 2023 • Timothy D. Gebhard, Daniel Angerhausen, Björn S. Konrad, Eleonora Alei, Sascha P. Quanz, Bernhard Schölkopf
When training and evaluating our method on two publicly available datasets of self-consistent PT profiles, we find that our method achieves, on average, better fit quality than existing baseline methods, despite using fewer parameters.
1 code implementation • 7 Apr 2022 • Timothy D. Gebhard, Markus J. Bonse, Sascha P. Quanz, Bernhard Schölkopf
Our HSR-based method provides an alternative, flexible and promising approach to the challenge of modeling and subtracting the stellar PSF and systematic noise in exoplanet imaging data.
no code implementations • 12 Oct 2020 • Timothy D. Gebhard, Markus J. Bonse, Sascha P. Quanz, Bernhard Schölkopf
The detection of exoplanets in high-contrast imaging (HCI) data hinges on post-processing methods to remove spurious light from the host star.
2 code implementations • 18 Apr 2019 • Timothy D. Gebhard, Niki Kilbertus, Ian Harry, Bernhard Schölkopf
In the last few years, machine learning techniques, in particular convolutional neural networks, have been investigated as a method to replace or complement traditional matched filtering techniques that are used to detect the gravitational-wave signature of merging black holes.