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Sydney Otten

Inferring astrophysical X-ray polarization with deep learning

May 16, 2020
Nikita Moriakov, Ashwin Samudre, Michela Negro, Fabian Gieseke, Sydney Otten, Luc Hendriks

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We investigate the use of deep learning in the context of X-ray polarization detection from astrophysical sources as will be observed by the Imaging X-ray Polarimetry Explorer (IXPE), a future NASA selected space-based mission expected to be operative in 2021. In particular, we propose two models that can be used to estimate the impact point as well as the polarization direction of the incoming radiation. The results obtained show that data-driven approaches depict a promising alternative to the existing analytical approaches. We also discuss problems and challenges to be addressed in the near future.

* Accepted to International Conference on Learning Representations (ICLR) 2020 Workshop: Fundamental Science in the Era of AI

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Constraining the Parameters of High-Dimensional Models with Active Learning

May 19, 2019
Sascha Caron, Tom Heskes, Sydney Otten, Bob Stienen

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Constraining the parameters of physical models with $>5-10$ parameters is a widespread problem in fields like particle physics and astronomy. In this paper we show that this problem can be alleviated by the use of active learning. We illustrate this with examples from high energy physics, a field where computationally expensive simulations and large parameter spaces are common. We show that the active learning techniques query-by-committee and query-by-dropout-committee allow for the identification of model points in interesting regions of high-dimensional parameter spaces (e.g. around decision boundaries). This makes it possible to constrain model parameters more efficiently than is currently done with the most common sampling algorithms. Code implementing active learning can be found on GitHub.

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