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.

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,

Preprint BibTeX

Cite this paper

@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},
}
NASA/ADS Oral presentation

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

Accepted for publication at Astronomy & Astrophysics,

Preprint BibTeX

Cite this paper

@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},
}
NASA/ADS

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

Accepted for publication in Astronomy & Astrophysics,

Preprint BibTeX

Cite this paper

@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}
}
NASA/ADS Code Dataset

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),

Preprint PDF BibTeX

Cite this paper

@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}
}
NASA/ADS Code DOI

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),

Preprint PDF BibTeX

Cite this paper

@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},
}
NASA/ADS Code DOI

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,

PDF BibTeX

Cite this paper

@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},
}
Poster

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,

PDF BibTeX

Cite this paper

@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},
}
Poster

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),

Preprint PDF BibTeX

Cite this paper

@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},
}
NASA/ADS Code Dataset DOI

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,

Preprint BibTeX

Cite this paper

@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},
}
NASA/ADS

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),

Preprint PDF BibTeX

Cite this paper

@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},
}
NASA/ADS DOI

Convolutional neural networks: A magic bullet for gravitational-wave detection?

Timothy D. Gebhard*, Niki Kilbertus*, Ian Harry, Bernhard Schölkopf

Physical Review D, 100 (6),

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@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},
}
NASA/ADS Code Dataset DOI

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,

PDF BibTeX

Cite this paper

@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},
}
Code Poster

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,

PDF BibTeX

Cite this paper

@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},
}
NASA/ADS DOI

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),

PDF BibTeX

Cite this paper

@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},
}
Poster DOI Oral presentation