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.

@article{Gebhard_2023,
  author    = {{Gebhard}, Timothy D. and {Angerhausen}, Daniel and {Konrad}, Björn S. and {Alei}, Eleonora and {Quanz}, Sascha P. and {Schölkopf}, Bernhard},
  eprint    = {2309.03075},
  journal   = {arXiv preprints},
  title     = {Parameterizing pressure-temperature profiles of exoplanet atmospheres with neural networks},
  year      = {2023}
}

@article{Gebhard_2023,
  title         = {{Inferring Atmospheric Properties of Exoplanets with Flow Matching and Neural Importance Sampling}},
  author        = {{Gebhard}, Timothy D. and {Wildberger}, Jonas and {Dax}, Maximilian and {Angerhausen}, Daniel and {Quanz}, Sascha P. and {Schölkopf}, Bernhard},
  year          = 2023,
  adsurl        = {https://adsabs.harvard.edu/abs/2023arXiv231208295G},
  eprint        = {2312.08295},
  journal       = {arXiv preprints},
  addendum      = {Accepted at the AI to Accelerate Science and Engineering (AI2ASE) workshop at AAAI 2024},
}

@article{Hayoz_2023,
  title         = {{CROCODILE Incorporating medium-resolution spectroscopy of close-in directly imaged exoplanets into atmospheric retrievals via cross-correlation}},
  author        = {{Hayoz}, Jean and {Cugno}, Gabriele and {Quanz}, Sascha P. and {Patapis}, Polychronis and {Alei}, Eleonora and {Bonse}, Markus J. and {Dannert}, Felix A. and {Garvin}, Emily O. and {Gebhard}, Timothy D. and {Konrad}, Björn S. and {Sartori}, Lia F.},
  year          = 2023,
  adsurl        = {https://adsabs.harvard.edu/abs/2023arXiv230910587H},
  eprint        = {2309.10587},
  journal       = {arXiv preprints},
}

@article{Vaughan_2023,
  adsurl    = {https://adsabs.harvard.edu/abs/2023MNRAS.524.5477V},
  author    = {{Vaughan}, Sophia R and {Gebhard}, Timothy D and {Bott}, Kimberly and {Casewell}, Sarah L and {Cowan}, Nicolas B and {Doelman}, David S and {Kenworthy}, Matthew and {Mazoyer}, Johan and {Millar-Blanchaer}, Maxwell A and {Trees}, Victor J H and {Stam}, Daphne M and {Absil}, Olivier and {Altinier}, Lisa and {Baudoz}, Pierre and {Belikov}, Ruslan and {Bidot}, Alexis and {Birkby}, Jayne L and {Bonse}, Markus J and {Brandl}, Bernhard and {Carlotti}, Alexis and {Choquet}, Elodie and {van Dam}, Dirk and {Desai}, Niyati and {Fogarty}, Kevin and {Fowler}, J and {van Gorkom}, Kyle and {Gutierrez}, Yann and {Guyon}, Olivier and {Haffert}, Sebastiaan Y and {Herscovici-Schiller}, Olivier and {Hours}, Adrien and {Juanola-Parramon}, Roser and {Kleisioti}, Evangelia and {König}, Lorenzo and {van Kooten}, Maaike and {Krasteva}, Mariya and {Laginja}, Iva and {Landman}, Rico and {Leboulleux}, Lucie and {Mouillet}, David and {N'Diaye}, Mamadou and {Por}, Emiel H and {Pueyo}, Laurent and {Snik}, Frans},
  doi       = {10.1093/mnras/stad2127},
  journal   = {Monthly Notices of the Royal Astronomical Society},
  month     = {7},
  number    = {4},
  pages     = {5477--5485},
  title     = {Chasing rainbows and ocean glints: Inner working angle constraints for the Habitable Worlds Observatory},
  volume    = {524},
  year      = {2023}
}

@article{Bonse_2023,
  title         = {{Comparing Apples with Apples: Robust Detection Limits for Exoplanet High-Contrast Imaging in the Presence of non-Gaussian Noise}},
  author        = {Markus J. Bonse and Emily O. Garvin and Timothy D. Gebhard and Felix A. Dannert and Faustine Cantalloube and others},
  year          = 2023,
  eprint        = {2303.12030},
  eprinttype    = {arXiv},
}

@article{Gebhard_2022,
  title         = {{Inferring molecular complexity from mass spectrometry data using machine learning}},
  author        = {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},
  year          = 2022,
  month         = 12,
  addendum      = {Accepted at the Machine Learning and the Physical Sciences workshop at NeurIPS 2022},
}

@article{Gebhard_2022,
  title         = {{Atmospheric retrievals of exoplanets using learned parameterizations of pressure-temperature profiles}},
  author        = {Timothy D. Gebhard and Daniel Angerhausen and Björn Konrad and Eleonora Alei and Sascha P. Quanz and Bernhard Schölkopf},
  year          = 2022,
  month         = 12,
  addendum      = {Accepted at the Machine Learning and the Physical Sciences workshop at NeurIPS 2022},
}

@article{Gebhard_2022,
  title         = {{Half-sibling regression meets exoplanet imaging: PSF modeling and subtraction using a flexible, domain knowledge-driven, causal framework}},
  author        = {Timothy D. Gebhard and Markus J. Bonse and Sascha P. Quanz and Bernhard Schölkopf},
  year          = 2022,
  month         = 10,
  doi           = {10.1051/0004-6361/202142529},
  publisher     = {{EDP} Sciences},
  volume        = 666,
  pages         = {A9},
  journal       = {Astronomy \& Astrophysics},
}

@article{Gebhard_2020,
  title         = {{Physically constrained causal noise models for high-contrast imaging of exoplanets}},
  author        = {Timothy D. Gebhard and Markus J. Bonse and Sascha P. Quanz and Bernhard Schölkopf},
  year          = 2020,
  month         = 10,
  eprint        = {2010.05591},
  eprinttype    = {arXiv},
  addendum      = {Accepted at the Machine Learning and the Physical Sciences workshop at NeurIPS 2020},
}

@article{Cuoco_2020,
  title         = {{Enhancing gravitational-wave science with machine learning}},
  author        = {Elena Cuoco and Jade Powell and Marco Cavaglià and Kendall Ackley and Michał Bejger and Chayan Chatterjee and Michael Coughlin and Scott Coughlin and Paul Easter and Reed Essick and Hunter Gabbard and Timothy Gebhard and Shaon Ghosh and Leïla Haegel and Alberto Iess and David Keitel and Zsuzsa Márka and Szabolcs Márka and Filip Morawski and Tri Nguyen and Rich Ormiston and Michael Pürrer and Massimiliano Razzano and Kai Staats and Gabriele Vajente and Daniel Williams},
  year          = 2020,
  month         = 12,
  journal       = {Machine Learning: Science and Technology},
  volume        = 2,
  number        = 1,
  pages         = {011002},
  doi           = {10.1088/2632-2153/abb93a},
}

@article{Gebhard_2019,
  title         = {{Convolutional neural networks: A magic bullet for gravitational-wave detection?}},
  author        = {Timothy D. Gebhard and Niki Kilbertus and Ian Harry and Bernhard Schölkopf},
  year          = 2019,
  month         = 9,
  journal       = {Physical Review D},
  volume        = 100,
  number        = 6,
  doi           = {10.1103/physrevd.100.063015},
  publisher     = {American Physical Society ({APS})},
  url           = {https://doi.org/10.1103/physrevd.100.063015},
}

@article{Gebhard_2017,
  title         = {{ConvWave: Searching for Gravitational Waves with Fully Convolutional Neural Nets}},
  author        = {Timothy Gebhard and Niki Kilbertus and Giambattista Parascandolo and Ian Harry and Bernhard Schölkopf},
  year          = 2017,
  month         = 12,
  booktitle     = {Workshop on Deep Learning for Physical Sciences (DLPS) at the 31st Conference on Neural Information Processing Systems (NeurIPS)},
  url           = {https://dl4physicalsciences.github.io/files/nips_dlps_2017_13.pdf},
}

@article{Ritter_2017,
  title         = {{Software Quality Control at Belle II}},
  author        = {Ritter, Martin and Kuhr, Thomas and Hauth, Thomas and Gebhard, Timothy and Kristof, Michal and Pulvermacher, Christian},
  year          = 2017,
  month         = 10,
  journal       = {Journal of Physics: Conference Series},
  volume        = 898,
  pages         = {072029},
  doi           = {10.1088/1742-6596/898/7/072029},
  publisher     = {{IOP} Publishing},
}

@inproceedings{Gebhard_2015,
  title         = {{Sample Size Estimation for Outlier Detection}},
  author        = {Gebhard, Timothy and Koerte, Inga and Bouix, Sylvain},
  year          = 2015,
  pages         = {743--750},
  doi           = {10.1007/978-3-319-24574-4_89},
  booktitle     = {Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015},
  publisher     = {Springer International Publishing},
  editor        = {Navab, Nassir and Hornegger, Joachim and Wells, William M. and Frangi, Alejandro F.},
  isbn          = {978-3-319-24574-4},
}