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Inductive Biases In Field-Level Cosmological Inference From Galaxy Catalogs, James O'Connor Baldwin
Inductive Biases In Field-Level Cosmological Inference From Galaxy Catalogs, James O'Connor Baldwin
Dissertations, Theses, and Capstone Projects
We perform field-level likelihood-free inference of the matter density parameter Ωm from simulated galaxy catalogs using machine learning models with differing inductive biases. Using features extracted from hydrodynamic simulations in the CAMELS suite, we investigate how both observable choice and model architecture govern the extraction of cosmological information. We consider galaxy positions and line-of-sight peculiar velocities, both separately and in combination, and compare permutation-invariant Deep Sets, implemented with either standard multilayer perceptrons (MLPs) or Kolmogorov–Arnold Networks (KANs), to graph neural networks (GNNs) implemented with MLPs, which explicitly encode spatial relations. We evaluate inference performance under both in-distribution and out-of-distribution (OOD) …
A Machine Learning Approach To Discovering Physical Models Of Galaxy Formation, Festa Bucinca
A Machine Learning Approach To Discovering Physical Models Of Galaxy Formation, Festa Bucinca
Dissertations, Theses, and Capstone Projects
Galaxies are the breathtakingly beautiful starry islands of the Universe. The process of galaxy formation involves the transformation from simple initial conditions in the early Universe to the complex galaxy structures we observe today. Spanning an immense spatial range and tremendous time scales - from the vastness of the Universe to the scale of individual stars - the physics of galaxy formation is both complex and crucial for understanding the Universe we live in. However, despite significant advancements, our theoretical understanding of galaxy formation remains incomplete.
In the era of big data available from hydrodynamical simulations and observations, Machine Learning …
Searching Harder, Localizing Better, Classifying Faster: Optimizing Fast Radio Burst Detection And Analysis, Kshitij Aggarwal
Searching Harder, Localizing Better, Classifying Faster: Optimizing Fast Radio Burst Detection And Analysis, Kshitij Aggarwal
Graduate Theses, Dissertations, and Problem Reports (ETD)
Fast Radio Bursts (or FRBs) are millisecond-duration transients of extragalactic origin. They exhibit dispersion caused by propagation through an ionized medium, and quantified by Dispersion Measure (DM). Around 800 FRBs (24 repeaters) have been discovered; so far, 24 FRBs have been confidently associated with a host galaxy. In this thesis, we discuss multiple new FRB search and analysis techniques and the corresponding tools that enable us to search for FRBs harder, localize them better, and classify candidates faster.
We discuss five open-source software suites that can be used in FRB analysis. These suites are used to distinguish between FRBs and …