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Abstract:

We study the M_HI−M_⋆ relation over the last billion years using the MIGHTEE-H i sample. We first model the upper envelope of the M_HI−M_⋆ relation with a Bayesian technique applied to a total number of 249 H i-selected galaxies, without binning the datasets, while taking account of the intrinsic scatter. We fit the envelope with both linear and non-linear models, and find that the non-linear model is preferred over the linear one with a measured transition stellar mass of log10 (M_⋆M_⊙) = 9.15±0.87, beyond which the slope flattens. This finding supports the view that the lack of H i gas is ultimately responsible for the decreasing star formation rate observed in the massive main-sequence galaxies. For spirals alone, which are biased towards the massive galaxies in our sample, the slope beyond the transition mass is shallower than for the full sample, indicative of distinct gas processes ongoing for the spirals/high-mass galaxies from other types with lower stellar masses. We then create mock catalogues for the MIGHTEE-H i detections and non-detections with two main galaxy populations of late- and early-type galaxies to measure the underlying M_HI−M_⋆ relation. We find that the turnover in this relation persists whether considering the two galaxy populations as a whole or separately. We note that an underlying linear relation could mimic this turnover in the observed scaling relation, but a model with a turnover is strongly preferred. Measurements on the logarithmic average of H i masses against the stellar mass are provided as a benchmark for future studies.

Abstract:

We present FORECAST, a new flexible and adaptable software package that performs forward modeling of the output of any cosmological hydrodynamical simulations to create a wide range of realistic synthetic astronomical images. With customizable options for filters, field of view size and survey parameters, it allows users to tailor the synthetic images to their specific requirements. FORECAST constructs light-cone exploiting the output snapshots of a simulation and computes the observed flux of each simulated stellar element, modeled as a Single Stellar Population, in any chosen set of pass-band filters, including k-correction, IGM absorption and dust attenuation. As a first application, we emulated the GOODS-South field as observed for the CANDELS survey exploiting the IllustrisTNG simulation. We produce images of 200 sq. arcmin., in 13 bands (eight Hubble Space Telescope optical and near-infrared bands from ACS B435 to WFC3 H160, the VLT HAWK-I Ks band, and the four IRAC filters from Spitzer), with depths consistent with the real data. We analysed the images with the same processing pipeline adopted for real data in CANDELS and ASTRODEEP publications, and we compared the results against both the input data used to create the images, and real data, generally finding good agreement with both, with some interesting exceptions which we discuss. As part of this work, we release the FORECAST code and two datasets: the CANDELS dataset analyzed in this study, and 10 JWST CEERS survey-like images (8 NIRCam and 2 MIRI) in a field of view of 200 sq. arcmin. between z=0-20. FORECAST is a flexible tool: it creates images that can then be processed and analysed using standard photometric algorithms, allowing for a consistent comparison among observations and models, and for a direct estimation of the biases introduced by such techniques.Comment: 21 pages, 15 figures, 6 tables, submitted to A&