Talks

 

Coffee will be served at 10h30

 

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Three Frontiers in the Search for Signs of Life Beyond Earth

For thousands of years, inspired by the starry night sky, humanity has wondered whether life exists beyond Earth. In the past three decades, this ancient question has moved from philosophy to data. Astronomers have discovered thousands of exoplanets orbiting stars other than the Sun, revealing that small rocky worlds are common in our galaxy. Astronomers now routine study exoplanet atmospheres, and have begun to search for gases that could be signs of life. Yet many observationally accessible rocky exoplanets may be hotter, more chemically aggressive, or otherwise unlike Earth. If so, the search for life must include environments beyond water. Venus offers a nearby test case, where its clouds have the right conditions for life: liquid, suitable temperatures, and an energy source (the Sun). The Venus clouds, however, are not made of liquid water but are composed of concentrated sulfuric acid—an aggressive chemical that is toxic for Earth life. We have found that some key biomolecules—including amino acids, short peptides, and even a genetic-like polymer—can remain stable in concentrated sulfuric acid under Venus-like conditions. This work has opened a third frontier: alternative solvents for life. Sulfuric acid may be only one example of a broader solvent frontier. Ionic liquids—polar, non-volatile solvents long known in chemistry—may form naturally from planetary materials and remain stable where water cannot exist. By considering environments very different from Earth, we open a frontier that draws together astronomy, planetary science, chemistry, biomolecular physics, and astrobiology—and forces us to ask what life truly requires

Galaxies are the primary tracers of the large-scale structure of the Universe and are traditionally used through summary statistics such as correlation functions and power spectra to constrain cosmological models. However, galaxy populations themselves contain rich information through their spatial distribution, environments, and internal properties, potentially extending beyond the information captured by standard summary statistics. In this talk, I will present a series of machine learning approaches designed to extract cosmological information directly from galaxy catalogs. I will discuss work using graph neural networks (GNNs) to infer cosmological parameters from simulated galaxy populations, including studies demonstrating robustness to observational effects and domain shifts across semi-analytic and hydrodynamical galaxy formation models. I will also present recent work exploring the cosmological information content of individual galaxies using symbolic regression, as well as ongoing efforts based on probabilistic generative models. Together, these results suggest that galaxy populations may encode cosmological information in ways that complement traditional large-scale structure analyses, opening new avenues for field-level and simulation-based inference in upcoming surveys

We perform for the first time full simulation-based inference on the Lyman-$\alpha$ forest 1D power spectrum. In particular, we consider the prediction of the Lyman-$\alpha$ forest $P_{\rm 1D}(k)$ at $2.0

The future of large-scale structure is at high redshift. In particular, the surveys of the next decade that will be at the vanguard of precision cosmology will target star-forming galaxies at z>2. Field-level inference efforts will, therefore, inevitably turn toward high-redshift samples in the near future, the clustering of which is not yet well-understood. The computational intractability of hydrodynamical simulations means that EFT-bias models at the field level will be especially compelling choices of field-level inference forward models for these samples. I will present my recent work measuring the EFT-bias parameters of simulated high-redshift galaxies at the field-level for the first time. I will also discuss how these results can be leveraged toward building simulation-based priors, which aid current field-level inference efforts.

Current and future high-redshift spectroscopic surveys such as DESI, DESI-II, and Spec-S5 raise the question of how to fully extract the information contained in these datasets. Field-level inference is opening a new frontier in cosmology by enabling analyses that capture the full information content of cosmological observations, rather than relying on two- or three-point summary statistics. High-redshift surveys probe unprecedented cosmological volumes, access quasi-linear modes sensitive to fundamental physics, benefit from precise calibration against small-scale hydrodynamic simulations, and exhibit diminishing shot noise toward higher redshift. However, accurately modeling these observations — particularly at the field level — remains a major challenge. I will present recent progress in modeling the Lyman-alpha forest at the field level and compare the information content of joint power spectrum and compressed bispectrum analyses with field-level inference approaches, combining methods from effective field theory (EFT) and machine learning (ML). This framework is then extended to additional high-redshift tracers, including Lyman-break galaxies and Lyman-alpha emitters, whose cross-correlations unlock new opportunities for extracting cosmological information and controlling systematic uncertainties. Finally, I will present ongoing work that combines perturbative mock generation with generative ML techniques to bridge large and small scales, enabling simulations that span DESI-like cosmological volumes while retaining the small-scale structure captured by hydrodynamic simulations. Together, these developments provide a scalable path toward fully exploiting next-generation surveys such as DESI-II and Spec-S5, establishing high-redshift structure as a key arena for precision, field-level cosmology.

Polarized Galactic emission is the foremost challenge for searches for a background of primordial gravitational waves imprinted in the polarization of the CMB. We argue that current methods struggle to address this challenge, either by being overly susceptible to model misspecification, or by failing to properly propagate the uncertainty due to residual Galactic emission after foreground cleaning. To address these issues, we explore a novel analysis framework for parameter inference with large-scale CMB polarization data. Our method combines simulation-based inference with the needlet internal linear combination (NILC) algorithm to compress the data into a summary statistic that is robust to model misspecification and small enough for neural posterior estimation with normalizing flows. We show that the semi-blind nature of the NILC-based compression significantly increases robustness to mismodeling of the anisotropic and non-Gaussian properties of the foreground fields. Using an idealized ground-based setup inspired by the Simons Observatory Small Aperture Telescopes, we demonstrate improved statistical constraining power for the tensor-to-scalar ratio r and improved robustness to complex foregrounds compared to other techniques in the literature. Trained on a semi-analytical foreground model, the method yields unbiased results across a range of PySM simulations, including the high-complexity d12 model, for which we obtain r=(1.09±0.27)⋅1e−2 for input r=0.01 and sky fraction fsky=0.21. Our results highlight the importance of designing data compression schemes for SBI that prioritize robustness to model misspecification over statistical optimality, and demonstrate the feasibility and advantages of a complete maps-to-parameters simulation-based analysis of large-scale CMB polarization for current ground-based observatories.

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