Torsten Enßlin

Cosmologist, Astrophysicist, Scientist, and Curious Person

I am a scientist at the Max-Planck-Institut für Astrophysik (MPA), Garching (near Munich), and lecturer at the Ludwig Maximilians University, Munich in Germany. I am interested in Information Theory, especially Information Field Theory (IFT), Artificial and Other Intelligence, Cosmology, and High Energy Astrophysics.

Recent Research Highlights

Algorithmic improvements for radio interferometry

Radio telescopes observe the sky in a very indirect fashion. Sky images in the radio frequency range therefore have to be computed using sophisticated algorithms. Scientists at the MPI for Astrophysics have developed a series of improvements for these algorithms, which help to improve the telescopes' resolution considerably.

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  • Artificial intelligence combined

    Artificial intelligence expands into all areas of the daily life, including research. Neural networks learn to solve complex tasks by training them on the basis of enormous amounts of examples. Researchers at the Max Planck Institute for Astrophysics in Garching have now succeeded in combining several networks, each one specializing in a different task, to jointly solve tasks using Bayesian logic in areas none was originally trained on. This enables the recycling of expensively trained networks and is an important step towards universally deductive artificial intelligence.

Information Field Theory

Information field theory (IFT) is information theory, the logic of reasoning under uncertainty, applied to fields. A field can be any quantity defined over some space, e.g. the air temperature over Europe, the magnetic field strength in the Milky Way, or the matter density in the Universe. IFT describes how data and knowledge can be used to infer field properties. Mathematically it is a statistical field theory and exploits many of the tools developed for such. Practically, it is a framework for signal processing and image reconstruction.

Learning Machines, Extended Logik, & Intelligence

Loosly connected research lines on machine learning, information theory, as well as artificial and other intelligence. Learning Machines better reason according to logic. If uncertainties are involved, this should be extended, probabilistic, or Bayesian logic. The same is true for any form of intelligence, whether of human, artificial, or other nature.

Cosmology

The temperature fluctuations in the cosmic microwave background (CMB) and the cosmic matter distribution in the large-scale structure (LSS) are both tracers of the primordial quantum fluctuations. Those are believed to have happened during the very first moments of the Universe in the inflationary epoch. CMB and LSS are therefore our primary information sources on cosmology. Their detailed studies provide us insight into the history, geometry and composition of the Universe. IFT permits us to construct optimal methods to analyse and interpret CMB and LSS data, and to image with high fidelity the cosmic structures imprinted in those data sets.

High Energy Astrophysics

The Universe is permeated by high-energy particles and magnetic fields. Charged particles with nearly the speed of light spiraling around in the magnetic fields, which themselves are bound to the cosmic plasma. The particles and fields are important ingredients of the interstellar and intergalactic media. They transport energy, they push and heat the thermal gas, and they trace violent processes in cosmic plasmas. A number of observational windows in basically all electromagnetic wavebands, ranging from the radio to the gamma ray regime, provide us with direct and indirect vision into the high energy Universe. The IFT group develops special purpose methods to better imagine relativistic particles, magnetic fields, and even to tomographically reconstruct their distributions within the Milkey Way.

Lecture on Information Theory & Information Field Theory

Imaging in astronomy, geology and medicine require intelligent methods to obtain high fidelity images from noisy, incomplete data. The theoretical and mathematical framework in which imaging and data analysis methods are derived should be information theory to which these lectures will introduce first (first 1/3 semester, suited for Bachelor and Master students, 3 ETCS). Based on this, information theory for fields will be developed, which can be used to reconstruct signals from data (remaining 2/3 semester, more targeted at Master students, 6 ETCS).

Seminar Information Theory & Information Field Theory

The seminar is intended for participants of the lecture on Information Theory (1/3 semester) & Information Field Theory (2/3 semester), the content of which will be assumed to be known by all participants. The main seminar goal is to extend the participants' knowledge beyond the material covered in the lecture, especially with respect to concrete measurement situations, imaging, and existing algorithms. A second goal is to practice presentations and open discussions.

Past & current Software Projects

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Contact

  • Address

    MPA
    Karl-Schwarzschild-Str. 1
    85748 Garching
    Germany
    Office: 010
  • Email

    ensslin@mpa-garching.mpg.de
  • Phone

    +49 (0) 89 30000 2243