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Creators/Authors contains: "Battaglia, Nicholas"

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  1. Abstract

    As cosmic microwave background (CMB) photons traverse the universe, anisotropies can be induced via Thomson scattering (proportional to the electron density; optical depth) and inverse Compton scattering (proportional to the electron pressure; thermal Sunyaev–Zel’dovich effect). Measurements of anisotropy in optical depthτand Comptonyparameters are imprinted by the galaxies and galaxy clusters and are thus sensitive to the thermodynamic properties of the circumgalactic medium and intergalactic medium. We use an analytic halo model to predict the power spectrum of the optical depth (ττ), the cross-correlation between the optical depth and the Comptonyparameter (τy), and the cross-correlation between the optical depth and galaxy clustering (τg), and compare this model to cosmological simulations. We constrain the optical depths of halos atz≲ 3 using a technique originally devised to constrain patchy reionization at a higher redshift range. The forecasted signal-to-noise ratio is 2.6, 8.5, and 13, respectively, for a CMB-S4-like experiment and a Vera C. Rubin Observatory–like optical survey. We show that a joint analysis of these probes can constrain the amplitude of the density profiles of halos to 6.5% and the pressure profiles to 13%. These constraints translate to astrophysical parameters, such as the gas mass fraction,fg, which can be constrained to 5.3% uncertainty atz∼ 0. The cross-correlations presented here are complementary to other CMB and galaxy cross-correlations since they do not require spectroscopic galaxy redshifts and are another example of how such correlations are a powerful probe of the astrophysics of galaxy evolution.

     
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    Free, publicly-accessible full text available July 1, 2024
  2. Complex astrophysical systems often exhibit low-scatter relations between observable properties (e.g., luminosity, velocity dispersion, oscillation period). These scaling relations illuminate the underlying physics, and can provide observational tools for estimating masses and distances. Machine learning can provide a fast and systematic way to search for new scaling relations (or for simple extensions to existing relations) in abstract high-dimensional parameter spaces. We use a machine learning tool called symbolic regression (SR), which models patterns in a dataset in the form of analytic equations. We focus on the Sunyaev-Zeldovich flux−cluster mass relation ( Y SZ − M ), the scatter in which affects inference of cosmological parameters from cluster abundance data. Using SR on the data from the IllustrisTNG hydrodynamical simulation, we find a new proxy for cluster mass which combines Y SZ and concentration of ionized gas ( c gas ): M ∝ Y conc 3/5 ≡ Y SZ 3/5 (1 − A c gas ). Y conc reduces the scatter in the predicted M by ∼20 − 30% for large clusters ( M ≳ 10 14 h −1 M ⊙ ), as compared to using just Y SZ . We show that the dependence on c gas is linked to cores of clusters exhibiting larger scatter than their outskirts. Finally, we test Y conc on clusters from CAMELS simulations and show that Y conc is robust against variations in cosmology, subgrid physics, and cosmic variance. Our results and methodology can be useful for accurate multiwavelength cluster mass estimation from upcoming CMB and X-ray surveys like ACT, SO, eROSITA and CMB-S4. 
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  3. Abstract

    We present tomographic measurements of structure growth using cross-correlations of Atacama Cosmology Telescope (ACT) DR6 and Planck cosmic microwave background (CMB) lensing maps with the unWISE Blue and Green galaxy samples, which span the redshift ranges 0.2 ≲z≲ 1.1 and 0.3 ≲z≲ 1.8, respectively. We improve on prior unWISE cross-correlations not just by making use of the new, high-precision ACT DR6 lensing maps, but also by including additional spectroscopic data for redshift calibration and by analyzing our measurements with a more flexible theoretical model. We determine the amplitude of matter fluctuations at low redshifts (z≃ 0.2–1.6), findingS8σ8(Ωm/0.3)0.5=0.813±0.021using the ACT cross-correlation alone andS8= 0.810 ± 0.015 with a combination of Planck and ACT cross-correlations; these measurements are fully consistent with the predictions from primary CMB measurements assuming standard structure growth. The addition of baryon acoustic oscillation data breaks the degeneracy betweenσ8and Ωm, allowing us to measureσ8= 0.813 ± 0.020 from the cross-correlation of unWISE with ACT andσ8= 0.813 ± 0.015 from the combination of cross-correlations with ACT and Planck. These results also agree with the expectations from primary CMB extrapolations in ΛCDM cosmology; the consistency ofσ8derived from our two redshift samples atz∼ 0.6 and 1.1 provides a further check of our cosmological model. Our results suggest that structure formation on linear scales is well described by ΛCDM even down to low redshiftsz≲ 1.

     
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  4. Abstract

    Rayleigh scattering of the cosmic microwave background (CMB) by neutral hydrogen shortly after recombination leaves frequency-dependent imprints on intensity and polarization fluctuations. High signal-to-noise observations of CMB Rayleigh scattering would provide additional insight into the physics of recombination, including greater constraining power for parameters like the primordial helium fraction, the light relic density, and the sum of neutrino masses. However, such a measurement of CMB Rayleigh scattering is challenging due to the presence of astrophysical foregrounds, which are more intense at the high frequencies, where the effects of Rayleigh scattering are most prominent. Here we forecast the detectability of CMB Rayleigh scattering including foreground removal using blind internal linear combination methods for a set of near-future surveys. We show that atmospheric effects for ground-based observatories and astrophysical foregrounds pose a significant hindrance to detecting CMB Rayleigh scattering with experiments planned for this decade, though a high-significance measurement should be possible with a future CMB satellite.

     
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  5. Abstract We forecast the number of galaxy clusters that can be detected via the thermal Sunyaev–Zel’dovich (tSZ) signals by future cosmic microwave background (CMB) experiments, primarily the wide area survey of the CMB-S4 experiment but also CMB-S4's smaller de-lensing survey and the proposed CMB-HD experiment. We predict that CMB-S4 will detect 75,000 clusters with its wide survey of f sky = 50% and 14,000 clusters with its deep survey of f sky = 3%. Of these, approximately 1350 clusters will be at z ≥ 2, a regime that is difficult to probe by optical or X-ray surveys. We assume CMB-HD will survey the same sky as the S4-Wide, and find that CMB-HD will detect three times more overall and an order of magnitude more z ≥ 2 clusters than CMB-S4. These results include galactic and extragalactic foregrounds along with atmospheric and instrumental noise. Using CMB-cluster lensing to calibrate the cluster tSZ–mass scaling relation, we combine cluster counts with primary CMB to obtain cosmological constraints for a two-parameter extension of the standard model (ΛCDM + ∑ m ν + w 0 ). In addition to constraining σ ( w 0 ) to ≲1%, we find that both surveys can enable a ∼2.5–4.5 σ detection of ∑ m ν , substantially strengthening CMB-only constraints. We also study the evolution of the intracluster medium by modeling the cluster virialization v( z ) and find tight constraints from CMB-S4, with further factors of three to four improvement for CMB-HD. 
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  6. Abstract

    It is important to understand the cycle of baryons through the circumgalactic medium (CGM) in the context of galaxy formation and evolution. In this study, we forecast constraints on the feedback processes heating the CGM with current and future Sunyaev–Zeldovich (SZ) observations. To constrain these processes, we use a suite of cosmological simulations, the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS). CAMELS varies four different feedback parameters of two previously existing hydrodynamical simulations, IllustrisTNG and SIMBA. We capture the dependences of SZ radial profiles on these feedback parameters with an emulator, calculate their derivatives, and forecast future constraints on these feedback parameters from upcoming experiments. We find that for a galaxy sample similar to what would be obtained with the Dark Energy Spectroscopic Instrument at the Simons Observatory, all four feedback parameters can be constrained (some within the 10% level), indicating that future observations will be able to further restrict the parameter space for these subgrid models. Given the modeled galaxy sample and forecasted errors in this work, we find that the inner SZ profiles contribute more to the constraining power than the outer profiles. Finally, we find that, despite the wide range of parameter variation in active galactic feedback in the CAMELS simulation suite, we cannot reproduce the thermal SZ signal of galaxies selected by the Baryon Oscillation Spectroscopic Survey as measured by the Atacama Cosmology Telescope.

     
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  7. Abstract 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 4233 cosmological simulations, 2049 N -body simulations, and 2184 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 catalogs, power spectra, bispectra, Ly α spectra, probability distribution functions, halo radial profiles, and X-rays photon lists. We also release over 1000 catalogs that contain billions of galaxies from CAMELS-SAM: a large collection of N -body simulations that have been combined with the Santa Cruz semianalytic 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 . 
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