Talks

It is well known that the study and observation of core collapse Supernovae (SN) provide powerful tools to probe possible scenarios of physics beyond the Standard Model (SM). After reviewing the basics beyond the mechanism of a SN explosion and how these observations can constraint models of new physics, I will focus on two novel ideas to exploit the already available data from past SN and the observation of a future, long due, galactic one. Firstly. I will discuss constraints, from the cooling on the SN, on the interactions of SM particles with an hypothetical dark sector, leading to bounds on the mediators of such interactions competitive with collider ones. Then, I will turn on the constrains on the emission of axion-like particles (ALPs) that can convert into photons in an external magnetic field, leading to a gamma-ray signal. I will discuss the possibility that ALPs can convert to gamma-rays in the stellar magnetic fields of the progenitor stars. Applying this concept to gamma-ray data from SN1987A leads to the strongest constraints on axion-like particles for masses within a few orders of magnitude of 10^-5 eV. The implications for a future galactic blue supergiant supernova will be discussed.

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In this talk, I will present a novel framework for modeling the weak lensing source galaxy redshift distribution and galaxy intrinsic alignment via a shared luminosity function. In the context of LSST Year 1 and Year 10 cosmic shear analysis, I demonstrate the significant impact of the luminosity function on source galaxy redshift distributions and intrinsic alignment contamination. I establish the influence of Schechter luminosity function parameters on the redshift distribution of a magnitude-limited sample and show the effects of marginalizing over these parameters in intrinsic alignment modeling. I forecast how this joint modeling approach affects cosmological parameter constraints, finding that it yields constraints comparable to standard analyses while mitigating potential biases from incorrectly fixed luminosity function parameters. I highlight the specific impact of the luminosity function shape on the cosmic shear data vector and discuss the method's potential for modeling generic selection functions and extending to a 3x2pt analysis with galaxy bias incorporation. While focused on LSST cosmic shear, this framework is broadly applicable to weak lensing surveys.

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Most adaptive traits are polygenic with many underlying loci. The genetic architecture of these traits specifies how the phenotypes can be changed by mutations, but many factors determine which of these loci will be used during adaptation. Additionally, adaptation of complex traits in replicate populations with phenotypic convergence can occur through selection of different sets of loci. The analysis of time-series phenotypic and genomic data in replicates of evolving populations is crucial for understanding the adaptive architecture of a trait. I will demonstrate how experimental evolution emerges as an exceptional approach for generating precisely this type of data. Drawing on empirical evidence, I will show that temporal phenotypic data enable the identification of adaptive patterns, even when the selected trait(s) are unidentified, as is often the case in natural and experimental populations. Finally, I will discuss the next generation of evolution experiments, designed to mimic th...