Talks

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...

The interplay between natural selection and migration is predicted to shape the architecture of adaptation in different ways, depending on whether the direction of selection is spatially homogenous or heterogeneous. When different populations experience selection towards a similar phenotypic optimum, there is no tension with migration and "global adaptation" proceeds in manner similar to that predicted for a single population. By contrast, when populations are selected towards different optima, "local adaptation" occurs, which tends to favour architectures driven by fewer, larger, and more tightly clustered alleles than global adaptation. Despite this clear theoretical prediction, there have been few, if any, comprehensive tests in natural populations. Here, we bring together genome sequence data from thousands of individuals from 25 species of plants to compare signatures of repeated selective sweeps (global adaptation) with those of genotype-environment association (local adaptation)...