Jens Jasche
About me
Dr. Jens Jasche is a Senior Lecturer recognised for his expertise in astrophysics and cosmology, in particular in the fields of cosmic structure formation, dark matter, and dark energy research. He started his academic journey with a physics diploma from Leibniz University of Hannover, focusing on the impact of cosmic rays on the formation of the first stars. He then pursued doctoral studies at the Max Planck Institute for Astrophysics, where he laid the groundwork for the new research area of cosmic field-level inference. Thanks to his pioneering work, it became possible to reconstruct the spatial distribution of cosmic structures and their gravitational formation history using data from large scale surveys of galaxies.
After the completion of his Ph.D., Dr. Jasche continued his postdoctoral research at the Argelander Institute for Astronomy, the Institut d’Astrophysique de Paris, and the Excellence Cluster Universe in Munich. Supported by a Feodor Lynen Fellowship from the Alexander von Humboldt Foundation, he extended his research to advanced inference and machine learning techniques to understand the origin and evolution of cosmic structures under the impact of dark matter and dark energy.
Dr. Jasche's scientific contributions made him an IAA Elected Fellow in 2020 by the International Astrostatistics Association. Additionally, he founded the Aquila consortium, an international collaboration focused on advancing cosmological research through innovative statistical and machine learning techniques (https://aquila-consortium.org/).
Dr. Jasche is dedicated to mentoring and collaboration, nurturing the next generation of cosmologists and astrophysicists. He emphasises interdisciplinary dialogue within the scientific community and aims to transmit scientific knowledge beyond the academic boundaries to the public at large.
Teaching
I am teaching the course Statistics for physicists.
I am also teaching the course Machine Learning for Physicists and Astronomers.
Research
My research is focused on unravelling the origin and evolution of cosmic structure, providing comprehensive insights into how gravity shapes the Universe at cosmic scales.
This allows us to understand the dynamics of dark matter, dark energy, and the early universe, as well as the implications of inflation for cosmic structure formation.
To achieve these objectives, my research methodology relies on three key pillars: theoretical and phenomenological modelling encompassing both analytical and numerical approaches, the utilisation of the latest datasets obtained from cosmological surveys, and the application of novel analyses and machine learning techniques to efficiently link theory and observations..
The physics research conducted within my group focuses on several fundamental topics in cosmology:
Exploring the Origin of Cosmic Structure and Testing Inflation Theory: This entails investigating the genesis of cosmic structures and conducting empirical assessments to validate the theory of inflation, thereby shedding light on the early stages of the evolution of the Universe.
Investigating Dynamic Equations of State for Dark Energy: Our efforts involve exploring alternative dynamic equations of state for dark energy, serving as plausible alternatives to the cosmological constant, with the aim of gaining deeper insights into the accelerating expansion of the Universe.
Testing Alternative Theories of Gravity: Through rigorous tests of alternative theories of gravity and scrutinising their predicted phenomenology concerning cosmic structure formation, we aim to elucidate the gravitational interactions at play on cosmic scales.
Unravelling the Particle Nature of Dark Matter: Our research delves into studying the cosmological decay and annihilation signatures of dark matter, elucidating their implications for cosmic structure formation and providing valuable insights into the properties of dark matter.
Probing the Existence and Effects of Massive Neutrinos: We investigate the presence and impact of massive neutrinos on cosmic structures, contributing to our understanding of neutrino physics and its broader implications for cosmology.
To accomplish these objectives, we leverage observational data obtained from galaxy surveys and spearhead research initiatives within prominent international cosmology collaborations, notably the Dark Energy Science Collaboration (https://lsstdesc.org/) and the Euclid consortium (https://www.euclid-ec.org/).
A significant portion of our research is devoted to advancing the technical capabilities of inference and machine learning techniques for the analysis of cosmological datasets. Our efforts in this domain encompass:
Accelerating Numerical Cosmic Structure Simulations: With the increasing precision of cosmological data, there is a growing demand for accurate physical modelling. To address this demand, we employ deep learning methodologies to develop surrogate models for intricate cosmic structure simulations. These surrogate models facilitate faster model testing and enable efficient exploration of parameter space.
Large-Scale Cosmological Inference using Generative Models: Our objective is to leverage generative modelling techniques to construct sophisticated digital representations of the Universe, commonly referred to as digital twins. These digital twins faithfully emulate the matter distribution and dynamical evolution of the actual cosmos, providing a novel approach for investigating intricate physical processes and aligning them with observational data.
Development of Machine Learning Techniques for Discovering Natural Laws: We focus on developing physics-informed machine learning models that leverage inherent symmetries within the data to autonomously discern fundamental principles of nature.
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