The thermal and kinematic Sunyaev-Zel'dovich effects (tSZ, kSZ) probe the thermodynamic properties of the circumgalactic and intracluster medium (CGM and ICM) of galaxies, groups, and clusters, since they are proportional, respectively, to the integrated electron pressure and momentum along the line of sight. We present constraints on the gas thermodynamics of CMASS (constant stellar mass) galaxies in the Baryon Oscillation Spectroscopic Survey using new measurements of the kSZ and tSZ signals obtained in a companion paper [Schaan et al.]. Combining kSZ and tSZ measurements, we measure within our model the amplitude of energy injection ϵM ⋆ c 2 , where M ⋆ is the stellar mass, to be ϵ ¼ ð40 AE 9Þ × 10 −6 , and the amplitude of the nonthermal pressure profile to be α Nth < 0.2ð2σÞ, indicating that less than 20% of the total pressure within the virial radius is due to a nonthermal component. We estimate the effects of including baryons in the modeling of weak-lensing galaxy cross-correlation measurements using the best-fit density profile from the kSZ measurement. Our estimate reduces the difference between the original theoretical model and the weak-lensing galaxy cross-correlation measurements in [A. Leauthaud et al., Mon. Not. R. Astron. Soc. 467, 3024 (2017)] by half (50% at most), but does not fully reconcile it. Comparing the kSZ and tSZ measurements to cosmological simulations, we find that they underpredict the CGM pressure and to a lesser extent the CGM density at larger radii with probabilities to exceed ranging from 0.00 to 0.03 and 0.12 to 0.14, for tSZ and kSZ, respectively. This suggests that the energy injected via feedback models in the simulations that we compared against does not sufficiently heat the gas at these radii. We do not find significant disagreement at smaller radii. These measurements provide novel tests of current and future simulations. This work demonstrates the power of joint, high signal-to-noise kSZ and tSZ observations, upon which future cross-correlation studies will improve.
We present the Cosmology and Astrophysics with Machine Learning Simulations (CAMELS) Multifield Data set (CMD), a collection of hundreds of thousands of 2D maps and 3D grids containing many different properties of cosmic gas, dark matter, and stars from more than 2000 distinct simulated universes at several cosmic times. The 2D maps and 3D grids represent cosmic regions that span ∼100 million light-years and have been generated from thousands of state-of-the-art hydrodynamic and gravity-only N-body simulations from the CAMELS project. Designed to train machine-learning models, CMD is the largest data set of its kind containing more than 70 TB of data. In this paper we describe CMD in detail and outline a few of its applications. We focus our attention on one such task, parameter inference, formulating the problems we face as a challenge to the community. We release all data and provide further technical details at https://camels-multifield-dataset.readthedocs.io.
We conduct a census of the high-mass protostellar population of the ∼ 70, 000 M Infrared Dark Cloud (IRDC) G028.37+00.07, identifying 35 sources based on their 70 µm emission, as reported in the Herschel Hi-Gal catalog of Molinari et al. (2016). We perform aperture photometry to construct spectral energy distributions (SEDs), which are then fit with the massive protostar models of Zhang & Tan (2018). We find that the sources span a range of isotropic luminosities from ∼ 20 to 4,500 L . The most luminous sources are predicted to have current protostellar masses of m * ∼ 10 M forming from cores of mass M c ∼ 40 to 400 M . The least luminous sources in our sample are predicted to be protostars with masses as low as ∼ 0.5 M forming from cores with M c ∼ 10 M , which are the minimum values explored in the protostellar model grid. The detected protostellar population has a total estimated protostellar mass of M * ∼ 100 M . Allowing for completeness corrections, which are constrained by comparison with an ALMA study in part of the cloud, we estimate a star formation efficiency per free-fall time of ∼ 3% in the IRDC. Finally, analyzing the spatial distribution of the sources, we find relatively low degrees of central concentration of the protostars. Thus, the most massive protostars do not appear to be especially centrally concentrated in the protocluster.
We present multi-wavelength images observed with SOFIA-FORCAST from ∼10 to 40 µm of 14 protostars, selected as intermediate-mass protostar candidates, as part of the SOFIA Massive (SOMA) Star Formation Survey. We build protostellar spectral energy distributions (SEDs) with the SOFIA observations, together with archival data from Spitzer, Herschel and IRAS. We then fit the SEDs with radiative transfer (RT) models of Zhang & Tan (2018), based on Turbulent Core Accretion theory, to estimate key properties of the protostars. The SEDs generally indicate the validity of these RT models down to intermediate-mass and/or early-stage protostars. With the addition of these intermediatemass sources, the protostars analyzed so far in the SOMA survey span a range of luminosities from ∼ 10 2 to ∼ 10 6 L , a range of current protostellar masses from ∼ 0.5 to ∼ 30 M and a range of ambient clump mass surface densities, Σ cl of 0.1 − 3 g cm −2 . A wide range of evolutionary states of the individual protostars and of the protocluster environments are also probed. The 19 to 37 µm spectral index of the sources correlates with outflow cavity opening angle, ratio of this angle to viewing angle, and evolutionary stage. We have also added a sample of about 50 protostellar sources identified from within Infrared Dark Clouds and expected to be at the earliest stages of their evolution. With this global sample, most of the evolutionary stages of high-and intermediate-mass protostars are probed. From the best fitting models of the protostars, there is no evidence of a threshold value of protocluster clump mass surface density being needed to form protostars up to about 25 M . However, to form more massive protostars, there is tentative evidence that Σ cl needs to be at least 1 g cm −2 . We discuss how this is consistent with expectations from core accretion models that include internal feedback from the forming massive star.
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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