Seminars and Colloquia at ESO Garching and on the campus

SAMOS is a programmable visible multi-object spectrograph designed to exploit the wide-field image quality delivered by the SOAR GLAO module. Its digital micromirror device provides a reconfigurable focal-plane mask in which arbitrary slit patterns can be generated in seconds, while a parallel imaging channel supports target acquisition, monitoring, and photometric calibration.

Following commissioning and completion of the imaging and spectroscopic reduction pipelines, SAMOS now provides an end-to-end demonstration of both the advantages and practical implementation of programmable multi-object spectroscopy. I will summarize its on-sky performance, observing workflow, multiplexing, calibration strategy, and final pipeline products.

I will then use SAMOS as a starting point for looking beyond seeing-limited spectroscopy. In studies of micromirror-based spectrographs for a possible UV/visible instrument for the Habitable Worlds Observatory (HWO, the next “super Hubble"), diffraction by individual mirrors, inter-mirror gaps, and tilted-mirror geometry becomes part of the optical response and introduces a new set of design constraints. Similar considerations arise for visible spectroscopy behind extreme-AO systems on large ground-based telescopes. MAVIS at the VLT could enable multiplexed spectroscopy of crowded fields and resolved stellar populations, providing a ground-based precursor, over a smaller field of view, to some of the visible-wavelength science capabilities envisioned for HWO.

SAMOS is therefore not only an operational instrument, but also a unique on-sky demonstrator for the next generation of reconfigurable astronomical spectroscopy.

As a taxpayer-funded organisation that designs, builds, and operates ground-based astronomical observatories, ESO has a duty to share the science enabled by its facilities. At the same time, communicating about discoveries made by our telescopes helps raise ESO’s profile and build public support for the organisation. Press releases, specifically those based on scientific papers using ESO data (the focus of this talk), are one of the best means we have to do that. In this informal discussion, I will summarise how we decide which papers to press release, clarify the do’s and don’ts of pitching a paper to the Department of Communication, and detail how we produce and distribute ESO press releases. 

The distribution of galaxies throughout the Universe is mapped to ever better precision by an ambitious survey program, and it holds important clues about the nature of Dark Energy, Gravity, and Dark Matter. Yet, unlocking this information is challenging. State-of-the-art analyses compare theory and data in terms of summary statistics, foremost the galaxy power spectrum. In this compression, information is lost. Field-level analysis, in contrast, considers the three-dimensional galaxy density and can optimally constrain cosmological parameters. These gains can be significant, and on mock data, field-level analysis doubles the precision over the state-of-the-art for some parameters. As an additional benefit, it constrains the primordial density fluctuations that give rise to the observed galaxy distribution. A central ingredient to facilitate field-level analysis is the forward model that connects an assumption on cosmology and initial conditions to the observations. By describing galaxy bias in the forward model with the Effective Field Theory of Large Scale Structure (EFT of LSS), uncertainties in galaxy formation physics can be marginalized over. Nevertheless, the high precision in field-level analysis puts stringent requirements on the accurate representation of cosmological, observational, and systematic effects, and the high number of variables demands sophisticated algorithms for numerical inference. In this talk, I explain how field-level analysis can be implemented, why it should, and the challenges ahead in realizing the promise of robust and precise cosmological insights on the initial conditions and their cosmological evolution from observations.