Seminars and Colloquia at ESO Garching and on the campus

We used ALMA for what it was built for: opening up new science by observing in the high frequency Band 10 (and 9). Specifically, we targeted the [OI] 63µm fine structure line in a sample of 12 gravitationally lensed dusty star-forming galaxies at 4.2<z<5.8. This line was expected to be as bright as the [CII] and [OIII]88µm lines, but we found it to be almost 100x fainter. Such extreme line ratios can only be explained by very strong self-absorption by foreground material within the galaxies, as also predicted in new hydrodynamical simulations. We only detect several narrow, spatially localized  [OI] 63µm emission “escape channels” preferentially detected in regions with weak or absent dust continuum emission. Intriguingly, in a few cases, the [OI] 63µm is detected in absorption against a bright continuum, reaching levels below the local CMB temperature. This suggests the presence of low-excitation, low-density gas along the line of sight. We argue that the very high [OI] 63µm optical depth is the dominant effect causing this strong absorption, limiting the diagnostic power of this line to trace regions of massive start formation in high-redshift DSFGs.

The interpretation of stellar surface images, fundamental parameters, stellar variability, and the detection and characterization of hosted planets requires realistic simulations of stellar convection. Regarding exoplanetary atmospheres, a time-dependent representation of the background stellar disk using 3D radiative-hydrodynamical (RHD) simulations is a natural and necessary step toward a better understanding of stellar properties and enabling a detailed and quantitative analysis of the atmospheric signatures of hosted planets. 3D RHD simulations have been, and will continue to be, crucial for studying the properties and dynamics of evolved stars as well. I will present how these simulations and their observables extend across the M-type domain of the Hertzsprung–Russell diagram.

Despite the century-long study of star-forming regions, we still do not have a clear observational picture of how star clusters actually assemble:
Is there a switch between hierarchical and monolithic formation? 
When do young stars decouple from their parent gas clouds? 
Current formation theories rely heavily on simulations, which often offer contrasting predictions. But with new data from multi-epoch surveys, we can finally push the boundaries of observational science and address the questions from a data-driven viewpoint.
I will briefly introduce these open questions and why they matter, and then discuss how multi-epoch NIR surveys like VISIONS, combined with new machine-learning tools, are letting us measure the motions of deeply embedded young stars and even the ISM itself. I will show what these measurements are telling us about cluster formation and how this will improve in the era of JWST, Roman, and Vera Rubin.

Artificial Intelligence (AI) has changed our way of assessing, processing, and distributing information, and Large Language Models (LLMs) like Chat GPT, Gemini, Claude and others could be seen as a replacement of intellectual performance previously only available through human intelligence. In the scientific community, many of us have been using AI and LLMs to accelerate their scientific productivity. However, it is unclear whether LLMs are actually needed to do high-quality science or simply yield higher productivity without substantially extending knowledge? This informal discussion was triggered by a discussion I had with Jason at the ESO guest house in Chile in January. I would like to discuss whether science actually profits from LLMs or if these tools rather undermine novel, quality research by design.

tbd