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Empirical Inference Members Publications

2019 Progress Report

2019 Progress Report

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Members

Empirical Inference
Bernhard Schölkopf
  • Director
Empirical Inference
Timothy Gebhard
  • Doctoral Researcher
Empirical Inference
Niki Kilbertus
  • Doctoral Researcher
Empirical Inference
Carl Johann Simon-Gabriel
  • Postdoctoral Researcher
Empirical Inference
Dominik Janzing
  • Research Scientist
Empirical Inference
Jonas Peters
Science and Probabilistic Intelligence
Maximilian Dax
  • Research Group Leader
Empirical Inference
Jakob Macke
Senior Research Scientist

Publications

Empirical Inference Article Real-time gravitational wave science with neural posterior estimation Dax, M., Green, S. R., Gair, J., Macke, J. H., Buonanno, A., Schölkopf, B. Physical Review Letters, 127(24), December 2021 (Published) arXiv DOI URL BibTeX
Empirical Inference Article Enhancing gravitational-wave science with machine learning Cuoco, E., Powell, J., Cavaglià, M., Ackley, K., Bejger, M., Chatterjee, C., Coughlin, M., Coughlin, S., Easter, P., Essick, R., Gabbard, H., Gebhard, T., Ghosh, S., Haegel, L., Iess, A., Keitel, D., Márka, Z., Márka, S., Morawski, F., Nguyen, T., et al. Machine Learning: Science and Technology, 2(1), 2020 (Published) DOI BibTeX
Empirical Inference Conference Paper Physically constrained causal noise models for high-contrast imaging of exoplanets Gebhard, T. D., Bonse, M. J., Quanz, S. P., Schölkopf, B. Machine Learning and the Physical Sciences - Workshop at the 34th Conference on Neural Information Processing Systems (NeurIPS), 2020 (Published) arXiv BibTeX
Empirical Inference Article Convolutional neural networks: A magic bullet for gravitational-wave detection? Gebhard, T., Kilbertus, N., Harry, I., Schölkopf, B. Physical Review D, 100(6):article no. 063015, American Physical Society, September 2019 (Published) DOI URL BibTeX
Empirical Inference Conference Paper ConvWave: Searching for Gravitational Waves with Fully Convolutional Neural Nets Gebhard, T., Kilbertus, N., Parascandolo, G., Harry, I., Schölkopf, B. Workshop on Deep Learning for Physical Sciences (DLPS) at the 31st Conference on Neural Information Processing Systems, December 2017 (Published) URL BibTeX
Empirical Inference Article A Causal, Data-driven Approach to Modeling the Kepler Data Wang, D., Hogg, D. W., Foreman-Mackey, D., Schölkopf, B. Publications of the Astronomical Society of the Pacific, 128(967):094503, 2016, Astrophysics Source Code Library ascl: 2107.024 (Published) DOI URL BibTeX
Empirical Inference Probabilistic Numerics Article Gaussian Process-Based Predictive Control for Periodic Error Correction Klenske, E. D., Zeilinger, M., Schölkopf, B., Hennig, P. IEEE Transactions on Control Systems Technology , 24(1):110-121, 2016 (Published) PDF DOI BibTeX
Empirical Inference Article Modeling Confounding by Half-Sibling Regression Schölkopf, B., Hogg, D., Wang, D., Foreman-Mackey, D., Janzing, D., Simon-Gabriel, C. J., Peters, J. Proceedings of the National Academy of Science, 113(27):7391-7398, 2016 (Published) Code DOI URL BibTeX
Empirical Inference Article The population of long-period transiting exoplanets Foreman-Mackey, D., Morton, T. D., Hogg, D. W., Agol, E., Schölkopf, B. The Astronomical Journal, 152(6):article no. 206, 2016 (Published) URL BibTeX
Empirical Inference Article A systematic search for transiting planets in the K2 data Foreman-Mackey, D., Montet, B., Hogg, D., Morton, T., Wang, D., Schölkopf, B. The Astrophysical Journal, 806(2), 2015 (Published)
Photometry of stars from the K2 extension of NASA’s Kepler mission is afflicted by systematic effects caused by small (few-pixel) drifts in the telescope pointing and other spacecraft issues. We present a method for searching K2 light curves for evidence of exoplanets by simultaneously fitting for these systematics and the transit signals of interest. This method is more computationally expensive than standard search algorithms but we demonstrate that it can be efficiently implemented and used to discover transit signals. We apply this method to the full Campaign 1 data set and report a list of 36 planet candidates transiting 31 stars, along with an analysis of the pipeline performance and detection efficiency based on artificial signal injections and recoveries. For all planet candidates, we present posterior distributions on the properties of each system based strictly on the transit observables.
DOI URL BibTeX