Beecroft Institute for Particle Astrophysics and Cosmology

Abstract:

We apply machine learning (ML), a powerful method for uncovering complex correlations in high-dimensional data, to the galaxy-halo connection of cosmological hydrodynamical simulations. The mapping between galaxy and halo variables is stochastic in the absence of perfect information, but conventional ML models are deterministic and hence cannot capture its intrinsic scatter. To overcome this limitation, we design an ensemble of neural networks with a Gaussian loss function that predict probability distributions, allowing us to model statistical uncertainties in the galaxy-halo connection as well as its best-fitting trends. We extract a number of galaxy and halo variables from the Horizon-AGN and IllustrisTNG100-1 simulations and quantify the extent to which knowledge of some subset of one enables prediction of the other. This allows us to identify the key features of the galaxy-halo connection and investigate the origin of its scatter in various projections. We find that while halo properties beyond mass account for up to 50 per cent of the scatter in the halo-To-stellar mass relation, the prediction of stellar half-mass radius or total gas mass is not substantially improved by adding further halo properties. We also use these results to investigate semi-Analytic models for galaxy size in the two simulations, finding that assumptions relating galaxy size to halo size or spin are not successful.

Abstract:

Abstract The Reionization Era Bright Emission Line Survey (REBELS) is a cycle-7 ALMA Large Program (LP) that is identifying and performing a first characterization of many of the most luminous star-forming galaxies known in the z > 6.5 universe. REBELS is providing this probe by systematically scanning 40 of the brightest UV-selected galaxies identified over a 7 deg 2 area for bright [C ii ] 158 μ m and [O iii ] 88 μ m lines and dust-continuum emission. Selection of the 40 REBELS targets was done by combining our own and other photometric selections, each of which is subject to extensive vetting using three completely independent sets of photometry and template-fitting codes. Building on the observational strategy deployed in two pilot programs, we are increasing the number of massive interstellar medium (ISM) reservoirs known at z > 6.5 by ∼4–5× to >30. In this manuscript, we motivate the observational strategy deployed in the REBELS program and present initial results. Based on the first-year observations, 18 highly significant ≥ 7 σ [C ii ] 158 μ m lines have already been discovered, the bulk of which (13/18) also show ≥3.3 σ dust-continuum emission. These newly discovered lines more than triple the number of bright ISM-cooling lines known in the z > 6.5 universe, such that the number of ALMA-derived redshifts at z > 6.5 rival Ly α discoveries. An analysis of the completeness of our search results versus star formation rate (SFR) suggests an ∼79% efficiency in scanning for [C ii ] 158 μ m when the SFR UV+IR is >28 M ⊙ yr −1 . These new LP results further demonstrate ALMA’s efficiency as a “redshift machine,” particularly in the Epoch of Reionization.

Abstract:

In this work, we examine the impact of our motion with respect to the Cosmic Microwave Background (CMB) rest frame on statistics of CMB maps by examining the one-, two-, three-, and four- point statistics of simulated maps of the CMB and Sunyaev-Zeldovich (SZ) effects. We validate boosting codes by comparing their outcomes for temperature and polarization power spectra up to ℓ ∼ 6000. We derive and validate a new analytical formula for the computation of the boosted power spectrum of a signal with a generic frequency dependence. As an example we show how this increases the boosting correction to the power spectrum of CMB intensity measurements by ∼30 per cent at 150 GHz. We examine the effect of boosting on thermal and kinetic SZ power spectra from semianalytical and hydrodynamical simulations; the boosting correction is generally small for both simulations, except when considering frequencies near the tSZ null. For the non-Gaussian statistics, in general we find that boosting has no impact with two exceptions. We find that, whilst the statistics of the CMB convergence field are unaffected, quadratic estimators that are used to measure this field can become biased at the O(1)per cent level by boosting effects. We present a simple modification to the standard estimators that removes this bias. Second, bispectrum estimators can receive a systematic bias from the Doppler induced quadrupole when there is anisotropy in the sky - in practice this anisotropy comes from masking and inhomogeneous noise. This effect is unobservable and already removed by existing analysis methods.