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In the age of large-scale galaxy and lensing surveys, such as DESI, Euclid, Roman, and Rubin, we stand poised to usher in a transformative new phase of data-driven cosmology. To fully harness the capabilities of these surveys, it is critical to constrain the poorly understood influence of baryon feedback physics on the matter power spectrum. We investigate the use of a powerful and novel cosmological probe, fast radio bursts (FRBs), to capture baryonic effects on the matter power spectrum, leveraging simulations from the Cosmology and Astrophysics with MachinE Learning Simulations (or CAMELS) project, including IllustrisTNG, SIMBA, and Astrid. We find that FRB statistics exhibit a strong correlation, independent of the subgrid model and cosmology, with quantities known to encapsulate baryonic impacts on the matter power spectrum, such as baryon spread and the halo baryon fraction. We propose an innovative method utilizing FRB observations to quantify the effects of feedback physics and enhance weak-lensing measurements of S8. We outline the necessary steps to prepare for the imminent detection of large FRB populations in the coming years, focusing on understanding the redshift evolution of FRB observables and mitigating the effects of cosmic variance.

We present CAMELS-ASTRID, the third suite of hydrodynamical simulations in the Cosmology and Astrophysics with MachinE Learning (CAMELS) project, along with new simulation sets that extend the model parameter space based on the previous frameworks of CAMELS-TNG and CAMELS-SIMBA, to provide broader training sets and testing grounds for machine-learning algorithms designed for cosmological studies. CAMELS-ASTRID employs the galaxy formation model following the ASTRID simulation and contains 2124 hydrodynamic simulation runs that vary three cosmological parameters (Ω m , σ 8, Ω b ) and four parameters controlling stellar and active galactic nucleus (AGN) feedback. Compared to the existing TNG and SIMBA simulation suites in CAMELS, the fiducial model of ASTRID features the mildest AGN feedback and predicts the least baryonic effect on the matter power spectrum. The training set of ASTRID covers a broader variation in the galaxy populations and the baryonic impact on the matter power spectrum compared to its TNG and SIMBA counterparts, which can make machine-learning models trained on the ASTRID suite exhibit better extrapolation performance when tested on other hydrodynamic simulation sets. We also introduce extension simulation sets in CAMELS that widely explore 28 parameters in the TNG and SIMBA models, demonstrating the enormity of the overall galaxy formation model parameter space and the complex nonlinear interplay between cosmology and astrophysical processes. With the new simulation suites, we show that building robust machine-learning models favors training and testing on the largest possible diversity of galaxy formation models. We also demonstrate that it is possible to train accurate neural networks to infer cosmological parameters using the high-dimensional TNG-SB28 simulation set.

During the past two decades, concern about spatial concentrations of poverty and disadvantage has become an ascendant scholarly and policy issue, and research on the effect of neighborhoods on individual and family life chances has grown substantially. The Choice Neighborhoods Initiative (hereafter, Choice), introduced in 2009, is a new federal program designed to address concentrated poverty. Choice, which is functionally the successor to the Housing Opportunities for People Everywhere, or HOPE VI, Program, provides competitive grants to fund redevelopment and revitalization in neighborhoods that have concentrations of poverty and publicly subsidized housing, with the goal of transforming them into neighborhoods of choice, thereby improving neighborhood outcomes. For the types of neighborhoods being targeted, little information beyond their having high rates of poverty is so far available. Drawing from the results of U.S. Department of Housing and Urban Development-funded research on the characteristics of Choice Planning Grant applicants, this article presents findings related to race and ethnicity in these targeted neighborhoods. The findings show that Choice Planning Grant applicant neighborhoods are highly segregated by race and ethnicity and that this segregation is linked to differences in educational attainment, labor force participation, unemployment rates, and income levels. These demographics suggest that Choice, like its predecessor, is likely to have a disproportionate effect on minority racial and ethnic groups.

Using the framework of the Tragedy of the Anticommons and informality, this chapter explores how some actors have attempted to negotiate intersecting and contradictory property rights and regulatory regimes in the United States, and the ambiguous position of temporary uses within these spaces. The Tragedy of the Anticommons may arise where ownership, development, and use rights become too fragmented, stymieing efforts at redevelopment and encouraging informal, temporary solutions. Using this framework, two examples – food carts in Portland, Oregon, and pop‐up shops in Detroit, Michigan – are used to highlight specific challenges faced in trying to negotiate informality and the complicated task of navigating property rights, local land use regulations, retail markets, and political environments to successfully create viable activities that contribute to revitalization.

In the age of large-scale galaxy and lensing surveys, such as DESI, Euclid, Roman and Rubin, we stand poised to usher in a transformative new phase of data-driven cosmology. To fully harness the capabilities of these surveys, it is critical to constrain the poorly understood influence of baryon feedback physics on the matter power spectrum. We investigate the use of a powerful and novel cosmological probe - fast radio bursts (FRBs) - to capture baryonic effects on the matter power spectrum, leveraging simulations from the CAMELS projects, including IllustrisTNG, SIMBA, and Astrid. We find that FRB statistics exhibit a strong correlation, independent of the subgrid model and cosmology, with quantities known to encapsulate baryonic impacts on the matter power spectrum, such as baryon spread and the halo baryon fraction. We propose an innovative method utilizing FRB observations to quantify the effects of feedback physics and enhance weak lensing measurements of \\(S_8\\). We outline the necessary steps to prepare for the imminent detection of large FRB populations in the coming years, focusing on understanding the redshift evolution of FRB observables and mitigating the effects of cosmic variance.

This dissertation explores the translation and adaptation of national policy by local governance to fit specific local contexts by examining the process through which HOPE VI backed public housing redevelopment plans have been created and implemented in Chicago, IL. Previous research on the HOPE VI program has largely focused on two aspects of the program: HOPE VI as an evolution of U.S. public housing policy and specific outcomes of the program in a particular location. While both bodies of literature acknowledge the important role of local governance and context in shaping redevelopment plans, neither adequately explores this process. This dissertation examines (1) the process through which decisions regarding the planning and implementation of public housing redevelopment at the local level have been reached, and (2) the effect of the local political, institutional, economic and spatial context on these decisions. It finds that Chicago's public housing redevelopment planning has been a path dependent process that has required the collaboration of competing interests. However, divisions, particularly class-based divisions, have not been set aside in favor of collaborative action to address local problems. Rather, ostensibly collaborative institutional arrangements have become venues for conflict and have served to reinforce or exacerbate existing power disparities. This dissertation provides a deeper understanding of the specific implementation of the HOPE VI program in Chicago as well as the functioning of the program more generally. It bridges the policy gap that exists between federal level programs and local implementation and furthers understanding of how local power structures and contexts influence the ability to produce equitable outcomes from urban redevelopment projects.

Baryonic processes such as radiative cooling and feedback from massive stars and active galactic nuclei (AGN) directly redistribute baryons in the Universe but also indirectly redistribute dark matter due to changes in the gravitational potential. In this work, we investigate this \"back-reaction\" of baryons on dark matter using thousands of cosmological hydrodynamic simulations from the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project, including parameter variations in the SIMBA, IllustrisTNG, ASTRID, and Swift-EAGLE galaxy formation models. Matching haloes to corresponding N-body (dark matter-only) simulations, we find that virial masses decrease owing to the ejection of baryons by feedback. Relative to N-body simulations, halo profiles show an increased dark matter density in the center (due to radiative cooling) and a decrease in density farther out (due to feedback), with both effects being strongest in SIMBA (> 450% increase at r < 0.01 Rvir). The clustering of dark matter strongly responds to changes in baryonic physics, with dark matter power spectra in some simulations from each model showing as much as 20% suppression or increase in power at k ~ 10 h/Mpc relative to N-body simulations. We find that the dark matter back-reaction depends intrinsically on cosmology (Omega_m and sigma_8) at fixed baryonic physics, and varies strongly with the details of the feedback implementation. These results emphasize the need for marginalizing over uncertainties in baryonic physics to extract cosmological information from weak lensing surveys as well as their potential to constrain feedback models in galaxy evolution.

We quantify the cosmological spread of baryons relative to their initial neighboring dark matter distribution using thousands of state-of-the-art simulations from the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project. We show that dark matter particles spread relative to their initial neighboring distribution owing to chaotic gravitational dynamics on spatial scales comparable to their host dark matter halo. In contrast, gas in hydrodynamic simulations spreads much further from the initial neighboring dark matter owing to feedback from supernovae (SNe) and Active Galactic Nuclei (AGN). We show that large-scale baryon spread is very sensitive to model implementation details, with the fiducial SIMBA model spreading \\(\\)40\\% of baryons \\(>\\)1\\,Mpc away compared to \\(\\)10\\% for the IllustrisTNG and ASTRID models. Increasing the efficiency of AGN-driven outflows greatly increases baryon spread while increasing the strength of SNe-driven winds can decrease spreading due to non-linear coupling of stellar and AGN feedback. We compare total matter power spectra between hydrodynamic and paired \\(N\\)-body simulations and demonstrate that the baryonic spread metric broadly captures the global impact of feedback on matter clustering over variations of cosmological and astrophysical parameters, initial conditions, and galaxy formation models. Using symbolic regression, we find a function that reproduces the suppression of power by feedback as a function of wave number (\\(k\\)) and baryonic spread up to \\(k 10\\,h\\)\\,Mpc\\(^-1\\) while highlighting the challenge of developing models robust to variations in galaxy formation physics implementation.

The Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project was developed to combine cosmology with astrophysics through thousands of cosmological hydrodynamic simulations and machine learning. CAMELS contains 4,233 cosmological simulations, 2,049 N-body and 2,184 state-of-the-art hydrodynamic simulations that sample a vast volume in parameter space. In this paper we present the CAMELS public data release, describing the characteristics of the CAMELS simulations and a variety of data products generated from them, including halo, subhalo, galaxy, and void catalogues, power spectra, bispectra, Lyman-\\(\\) spectra, probability distribution functions, halo radial profiles, and X-rays photon lists. We also release over one thousand catalogues that contain billions of galaxies from CAMELS-SAM: a large collection of N-body simulations that have been combined with the Santa Cruz Semi-Analytic Model. We release all the data, comprising more than 350 terabytes and containing 143,922 snapshots, millions of halos, galaxies and summary statistics. We provide further technical details on how to access, download, read, and process the data at https://camels.readthedocs.io.

We present CAMELS-ASTRID, the third suite of hydrodynamical simulations in the Cosmology and Astrophysics with MachinE Learning (CAMELS) project, along with new simulation sets that extend the model parameter space based on the previous frameworks of CAMELS-TNG and CAMELS-SIMBA, to provide broader training sets and testing grounds for machine-learning algorithms designed for cosmological studies. CAMELS-ASTRID employs the galaxy formation model following the ASTRID simulation and contains 2,124 hydrodynamic simulation runs that vary 3 cosmological parameters (\\(_m\\), \\(_8\\), \\(_b\\)) and 4 parameters controlling stellar and AGN feedback. Compared to the existing TNG and SIMBA simulation suites in CAMELS, the fiducial model of ASTRID features the mildest AGN feedback and predicts the least baryonic effect on the matter power spectrum. The training set of ASTRID covers a broader variation in the galaxy populations and the baryonic impact on the matter power spectrum compared to its TNG and SIMBA counterparts, which can make machine-learning models trained on the ASTRID suite exhibit better extrapolation performance when tested on other hydrodynamic simulation sets. We also introduce extension simulation sets in CAMELS that widely explore 28 parameters in the TNG and SIMBA models, demonstrating the enormity of the overall galaxy formation model parameter space and the complex non-linear interplay between cosmology and astrophysical processes. With the new simulation suites, we show that building robust machine-learning models favors training and testing on the largest possible diversity of galaxy formation models. We also demonstrate that it is possible to train accurate neural networks to infer cosmological parameters using the high-dimensional TNG-SB28 simulation set.