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Abstract We presentslick(the Scalable Line Intensity Computation Kit), a software package that calculates realistic CO, [Ci], and [Cii] luminosities for clouds and galaxies formed in hydrodynamic simulations. Built on the radiative transfer codedespotic,slickcomputes the thermal, radiative, and statistical equilibrium in concentric zones of model clouds, based on their physical properties and individual environments. We validate our results by applyingslickto the high-resolution run of theSimbasimulations, testing the derived luminosities against empirical and theoretical/analytic relations. To simulate the line emission from a universe of emitting clouds, we have incorporated random forest machine learning (ML) methods into our approach, allowing us to predict cosmologically evolving properties of CO, [Ci], and [Cii] emission from galaxies such as luminosity functions. We tested this model in 100,000 gas particles, and 2500 galaxies, reaching an average accuracy of ∼99.8% for all lines. Finally, we present the first model light cones created with realistic and ML-predicted CO, [Ci], and [Cii] luminosities in cosmological hydrodynamical simulations, fromz= 0 toz= 10. 

Abstract We present the second data release of the Massive and Distant Clusters of WISE Survey 2 (MaDCoWS2). We expand from the equatorial first data release to most of the Dark Energy Camera Legacy Survey area, covering a total area of 6498 deg². The catalog consists of 133,036 signal-to-noise ratio (S/N) ≥ 5 galaxy cluster candidates at 0.1 ≤z≤ 2, including 6790 candidates atz> 1.5. We train a convolutional neural network (CNN) to identify spurious detections and include CNN-based cluster probabilities in the final catalog. We also compare the MaDCoWS2 sample with literature catalogs in the same area. The larger sample provides robust results that are consistent with our first data release. At S/N ≥ 5, we rediscover 59%–91% of clusters in existing catalogs that lie in the unmasked area of MC2. The median positional offsets are under 250 kpc, and the standard deviation of the redshifts is 0.031(1 +z). We fit a redshift-dependent power law to the relation between MaDCoWS2 S/N and observables from existing catalogs. Over the redshift ranges where the surveys overlap with MaDCoWS2, the lowest scatter is found between S/N and observables from optical/infrared surveys. We also assess the performance of our method using a mock light cone measuring purity and completeness as a function of cluster mass. The purity is above 90%, and we estimate the 50% completeness threshold at a virial mass of log(M/M_⊙) ≈ 14.3. The completeness estimate is uncertain due to the small number of massive halos in the light cone, but consistent with the recovery fraction found by comparing to other cluster catalogs.