Ruprecht-Karls-Universität Heidelberg

Welcome to the Centre for Astronomy of Heidelberg University


This years' Astrophysical Software Award of the German Astronomical Society (AG) goes to Cornelis P. Dullemond (ZAH/ITA) for his radiative transfer software RADMC-3D. (2026/07/10)

Prof. Michela Mapelli (ZAH/ITA) has been appointed Chair of the Observation Science Board of one of the projects shaping the future of gravitational-wave astronomy. (2026/06/08)

Heidelberg Astrophysicist Michela Mapelli (ZAH/ITA) helps decode a new Black-Hole enigma in a new catalog of gravitational wave events. (2026/05/26)

A new study involving Michela Mapelli (ZAH/ITA) and collaborators suggests that the biggest black holes form through violent merging events in densely populated star clusters. (2026/05/08)

In a new study Zofia Kaczmarek (ZAH/ARI) and colleagues show how the Roman Space Telescope will enable the discovery of quiescent neutron stars. (2026/05/07)

Heidelberg astronomers help uncover how dense stellar building blocks form in the Large Magellanic Cloud. (2026/04/26)


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ASTROPHYSICS CURRICULUM

Summer Term 26 ►►►►



Next Astro colloquia
Oct 02
11:00
TBD
Duncan Christie (MPIA)
Königstuhl Kolloquium
Max-Planck-Institut für Astronomie, Level 3 Lecture Hall (301)
Oct 16
11:00
TBA
TBA (TBA)
Königstuhl Kolloquium
Max-Planck-Institut für Astronomie, Level 3 Lecture Hall (301)
Oct 20
16:30
Again! - but faster, better, and with more physics: ML-accelerated inference of galaxy properties in deep and wide surveys of the universe
Joel Leja (Penn State University)
Heidelberg Joint Astronomical Colloquium
Philosophenweg 12, Main Lecture hall (gHS),

More colloquia

Recent ZAH publications
9/2026
Guetzoyan, Paloma; Aird, James et al. (inc. Demke, Delvin; Wylezalek, Dominika)
SDSS-V: revealing a weak accretion state in X-ray selected red quasars
MNRAS 551, g1438(2026)

8/2026
Schneider, Fabian R. N.
Theory, Simulations, and Observations of Stellar Mergers
ARA&A 64, 629(2026)

10/2026
Schweder, F.; Trujillo-Gomez, S. et al.
Probabilistic estimation of galactic inclinations: Inferring calibrated uncertainties using a network trained on cosmological simulations
A&C 57, 01132(2026)


More publications

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