Project description
A new way to shed light on dark matter
Astrophysics faces two major questions: the nature of dark matter and its interaction with baryonic processes. With the support of the Marie Skłodowska-Curie Actions programme, the TraDE-DML project aims to answer these by analysing galaxy observations and simulations across a redshift range of z = 0.3-2.5 covering 3-10 billion years of cosmic history. TraDE-DML introduces a novel machine learning approach to precisely determine the dynamical masses of galaxies, improving dark matter estimates. This project will leverage existing and upcoming survey data, preparing for large-scale telescopic initiatives like ELT and SKA. Combining expertise in observational astronomy, theoretical physics and machine learning, TraDE-DML will shed light on dark matter’s elusive nature.
Objective
"We plan to answer two pivotal questions of modern astrophysics: the nature of dark matter and its interaction with baryonic processes. Utilizing galaxy observations and cosmological hydrodynamical galaxy simulations across a redshift range of z = 0.3-2.5 we will examine 3-10 Gyr of cosmic history.
We propose to ""Trace the Dynamical Evolution of Dark Matter via Machine Learning""- TraDE-DML, that pioneers an advanced methodology for assessing the dynamical masses of galaxies, aiming for unprecedented precision in the quantification of both baryonic and dark matter components. Unlike conventional velocity profile studies, TraDE-DML eliminates assumptions of symmetry and dynamical equilibrium, substantially reducing uncertainties in dark matter estimates. Our project aims to exploit existing and future survey data, preparing for expansive telescopic projects like ELT and SKA. Simple in concept but revolutionary in application, the machine learning techniques used in TraDE-DML are poised for transformative advances in dark matter studies, particularly in determining its central density slope. By synergistically integrating knowledge from observational astronomy, theoretical physics, machine learning, and statistics, TraDE-DML aims to make significant strides in unraveling the elusive nature of dark matter.
As an expert in observational data analysis with privileged access to leading galaxy surveys like MAGPI and MIGHTEE, I possess the skills to efficiently extract and analyse pertinent data. The host, Dr. Benoit Famaey, excels in galaxy dynamics and alternative dark matter theories. Supported by a team versed in cosmological simulations and machine learning experts at the Inter-disciplinary Institute IRMIA++, we form a unique research synergy. Utilizing advanced machine learning frameworks and leveraging expansive survey data, TraDE-DML is well-positioned for immediate execution.
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Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
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Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA)
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Topic(s)
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Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
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Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships
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Call for proposal
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(opens in new window) HORIZON-MSCA-2023-PF-01
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75794 Paris
France
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