Publications
Here is a list of all my publications (excluding conference abstracts). Alternatively, you can also take a lookt at my Google Scholar profile, or search for me on NASA/ADS.
Flow matching for atmospheric retrieval of exoplanets: Where reliability meets adaptive noise levels
Timothy D. Gebhard, Jonas Wildberger, Maximilian Dax, Annalena Kofler, Daniel Angerhausen, Sascha P. Quanz, Bernhard Schölkopf
Accepted for publication in Astronomy & Astrophysics,
Use the 4S (Signal-Safe Speckle Subtraction): Explainable Machine Learning reveals the Giant Exoplanet AF Lep b in High-Contrast Imaging Data from 2011
Markus J. Bonse, Timothy D. Gebhard, Felix A. Dannert, Olivier Absil, Faustine Cantalloube, Valentin Christiaens, Gabriele Cugno, Emily O. Garvin, Jean Hayoz, Markus Kasper, Elisabeth Matthews, Bernhard Schölkopf, Sascha P. Quanz
Under review,
Parameterizing pressure-temperature profiles of exoplanet atmospheres with neural networks
Timothy D. Gebhard, Daniel Angerhausen, Björn S. Konrad, Eleonora Alei, Sascha P. Quanz, Bernhard Schölkopf
Astronomy & Astrophysics, 681 (A3),
Inferring Atmospheric Properties of Exoplanets with Flow Matching and Neural Importance Sampling
Timothy D. Gebhard, Jonas Wildberger, Maximilian Dax, Daniel Angerhausen, Sascha P. Quanz, Bernhard Schölkopf
Accepted at the AI to Accelerate Science and Engineering (AI2ASE) workshop at AAAI 2024,
CROCODILE: Incorporating medium-resolution spectroscopy of close-in directly imaged exoplanets into atmospheric retrievals via cross-correlation
Jean Hayoz, Gabriele Cugno, Sascha P. Quanz, Polychronis Patapis, Eleonora Alei, Markus J. Bonse, Felix A. Dannert, Emily O. Garvin, Timothy D. Gebhard, Björn S. Konrad, Lia F. Sartori
Astronomy & Astrophysics, 678 (A178),
Chasing rainbows and ocean glints: Inner working angle constraints for the Habitable Worlds Observatory
Sophia R. Vaughan, Timothy D. Gebhard, Kimberly Bott, Sarah L. Casewell, Nicolas B. Cowan, David S. Doelman, Matthew Kenworthy, Johan Mazoyer, Maxwell A. Millar-Blanchaer, Victor Trees, Daphne M. Stam, Olivier Absil, Lisa Altinier, Pierre Baudoz, Ruslan Belikov, Alexis Bidot, Jayne L. Birkby, Markus J. Bonse, Bernhard Brandl, Alexis Carlotti, Elodie Choquet, Dirk van Dam, Niyati Desai, Kevin Fogarty, J. Fowler, Kyle van Gorkom, Yann Gutierrez, Olivier Guyon, Sebastiaan Y. Haffert, Olivier Herscovici-Schiller, Adrien Hours, Roser Juanola-Parramon, Evangelia Kleisioti, Lorenzo König, Maaike van Kooten, Mariya Krasteva, Iva Laginja, Rico Landman, Lucie Leboulleux, David Mouillet, Mamadou N’Diaye, Emiel H. Por, Laurent Pueyo, Frans Snik
Monthly Notices of the Royal Astronomical Society, 524 (4),
Comparing Apples with Apples: Robust Detection Limits for Exoplanet High-Contrast Imaging in the Presence of non-Gaussian Noise
Markus J. Bonse, Emily O. Garvin, Timothy D. Gebhard, Felix A. Dannert, Faustine Cantalloube, Gabriele Cugno, Olivier Absil, Jean Hayoz, Julien Milli, Markus Kasper, Sascha P. Quanz
The Astronomical Journal, 166 (71),
Inferring molecular complexity from mass spectrometry data using machine learning
Timothy D. Gebhard*, Aaron C. Bell*, Jian Gong*, Jaden J. A. Hastings*, G. Matthew Fricke, Nathalie Cabrol, Scott Sandford, Michael Phillips, Kimberley Warren-Rhodes, Atılım Güneş Baydin
Accepted at the Machine Learning and the Physical Sciences workshop at NeurIPS 2022,
Atmospheric retrievals of exoplanets using learned parameterizations of pressure-temperature profiles
Timothy D. Gebhard, Daniel Angerhausen, Björn Konrad, Eleonora Alei, Sascha P. Quanz, Bernhard Schölkopf
Accepted at the Machine Learning and the Physical Sciences workshop at NeurIPS 2022,
Half-sibling regression meets exoplanet imaging: PSF modeling and subtraction using a flexible, domain knowledge-driven, causal framework
Timothy D. Gebhard, Markus J. Bonse, Sascha P. Quanz, Bernhard Schölkopf
Astronomy & Astrophysics, 666 (A9),
Physically constrained causal noise models for high-contrast imaging of exoplanets
Timothy D. Gebhard, Markus J. Bonse, Sascha P. Quanz, Bernhard Schölkopf
Accepted at the Machine Learning and the Physical Sciences workshop at NeurIPS 2020,
Enhancing Gravitational-Wave Science with Machine Learning
Elena Cuoco, Jade Powell, Marco Cavaglià, Kendall Ackley, Michał Bejger, Chayan Chatterjee, Michael Coughlin, Scott Coughlin, Paul Easter, Reed Essick, Hunter Gabbard, Timothy Gebhard, Shaon Ghosh, Leïla Haegel, Alberto Iess, David Keitel, Zsuzsa Márka, Szabolcs Márka, Filip Morawski, Tri Nguyen, Rich Ormiston, Michael Puerrer, Massimiliano Razzano, Kai Staats, Gabriele Vajente, Daniel Williams
Machine Learning: Science and Technology, 2 (1),
Convolutional neural networks: A magic bullet for gravitational-wave detection?
Timothy D. Gebhard*, Niki Kilbertus*, Ian Harry, Bernhard Schölkopf
Physical Review D, 100 (6),
ConvWave: Searching for Gravitational Waves with Fully Convolutional Neural Nets
Timothy Gebhard*, Niki Kilbertus*, Giambattista Parascandolo, Ian Harry, Bernhard Schölkopf
Accepted at the Deep Learning for Physical Sciences workshop at NeurIPS 2017,
Software Quality Control at Belle II
Martin Ritter, Thomas Kuhr, Thomas Hauth, Timothy Gebhard, Michal Kristof, Christian Pulvermacher
Journal of Physics: Conference Series, Volume 898,
Sample Size Estimation for Outlier Detection
Timothy Gebhard, Inga Koerte, Sylvain Bouix
18th International Conference on Medical Image Computing and Computer-Assisted Interventions (MICCAI 2015),