Detecting the line-intensity mapping (LIM) signal from the galaxies of the Epoch of Reionization is an emerging tool to constrain their role in reionization. Ongoing and upcoming experiments target the signal fluctuations across the sky to reveal statistical and astrophysical properties of these galaxies via signal statistics, e.g., the power spectrum. Here, we revisit the [C ii]$_{158 \mu \text{m}}$ LIM power spectrum under non-uniform line-luminosity scatter, which has a halo-mass variation of statistical properties. Line-luminosity scatter from a cosmological hydrodynamic and radiative transfer simulation of galaxies at z = 6 is considered in this study. We test the robustness of different model frameworks that interpret the impact of the line-luminosity scatter on the signal statistics. We use a simple power-law model to fit the scatter and demonstrate that the mean luminosity-halo mass correlation fit cannot preserve the mean intensity of the LIM signal (hence the clustering power spectrum) under non-uniform scatter. In our case, the mean intensity changes by ∼48 per cent compared to the mean correlation fit in contrast to the general case with semi-analytic scatter. However, we find that the prediction for the mean intensity from the most-probable fit can be modelled robustly, considering the generalized and more realistic non-uniform scatter. We also explore the possibility of diminishing luminosity bias under non-uniform scatter, affecting the clustering power spectrum, although this phenomenon might not be statistically significant. Therefore, we should adopt appropriate approaches that can consistently interpret the LIM power spectrum from observations.
Redshifted [C II] 158µm line-intensity mapping (LIM) of the Epoch of Reionization (EoR) with ongoing and upcoming experiments like the CONCERTO, TIME and FYST, is a new tool to constrain the role of the early galaxies in reionization. We expect statistics, e.g., the power spectrum of the LIM signal to be detectable by these experiments which will help us understand the clustering and astrophysical properties of the [C II] line emitters. Although the fluctuations of the LIM signal are generally modeled by assuming a one-to-one L [CII] -M halo relationship, the scatter in the [C II] luminosity, arising due to various ongoing astrophysical processes under varied environment inside individual galaxies, will also impact the power spectrum. In this work, using the results from a hydrodynamic and radiative transfer simulation of early galaxies, we find that this scatter can enhance the LIM power spectrum up to a factor of ∼ 2.7 − 2.9 (at z = 6). It is, therefore, crucial to take the effect of [C II] luminosity scatter into account while interpreting the [C II] LIM power spectrum from future observations.
Inventing new ways of exploring the materials phase space accelerates functional materials discovery. For new breakthroughs materials, faster exploration of bigger phase spaces is a key goal. High throughput computational screening (HTCS) is widely used to quickly search for materials with the right functional property. In this article we redefine HTCS methods to combine deep learning and physics based model to explore much large chemical spaces than possible by pure physics driven HTCS. Deep generative models are used to autonomously create materials libraries with high likelihood of desired properties, inverting the design paradigm. Additionally machine learnt surrogates enable next layer of screening to prune the set further such as high quality quantum mechanical simulations can be performed. With organic photovoltaic (OPV) molecules as test bench, we show the power of this redesigned HTCS approach in inverse design OPV molecules with very limited computational expense.
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