Lyα\alpha at Cosmic Dawn with a Simulated Roman Grism Deep Field

The slitless grism on the Nancy Grace Roman Space Telescope will enable deep near-infrared spectroscopy over a wide field of view. We demonstrate Roman's capability to detect Ly$\alpha$ galaxies at $z>7$ using a multi-position-angle (PA) observational strategy. We simulate Roman grism data using a realistic foreground scene from the COSMOS field. We also input fake Ly$\alpha$ galaxies spanning redshift z=7.5-10.5 and a line-flux range of interest. We show how a novel data cube search technique -- CUBGRISM -- originally developed for GALEX can be applied to Roman grism data to produce a Ly$\alpha$ flux-limited sample without the need for continuum detections. We investigate the impact of altering the number of independent PAs and exposure time. A deep Roman grism survey with 25 PAs and a total exposure time of $70$hrs can achieve Ly$\alpha$ line depths comparable to the deepest $z=7$ narrow-band surveys ($L_{{\rm{Ly}}\alpha}\gtrsim10^{43}$erg s$^{-1}$). Assuming a null result, where the opacity of the intergalactic medium (IGM) remains unchanged from $z\sim7$, this level of sensitivity will detect $\sim400$ deg$^{-2}$ Ly$\alpha$ emitters from $z=7.25-8.75$. A decline from this expected number density is the signature of an increasing neutral hydrogen fraction and the onset of reionization. Our simulations indicate that a deep Roman grism survey has the ability to measure the timing and magnitude of this decline, allowing us to infer the ionization state of the IGM and helping us to distinguish between models of reionization.Comment: 11 pages, 7 figures, Submitted to Ap

Mangrove\texttt{Mangrove}: Learning Galaxy Properties from Merger Trees

Efficiently mapping baryonic properties onto dark matter is a major challenge in astrophysics. Although semi-analytic models (SAMs) and hydrodynamical simulations have made impressive advances in reproducing galaxy observables across cosmologically significant volumes, these methods still require significant computation times, representing a barrier to many applications. Graph Neural Networks (GNNs) have recently proven to be the natural choice for learning physical relations. Among the most inherently graph-like structures found in astrophysics are the dark matter merger trees that encode the evolution of dark matter halos. In this paper we introduce a new, graph-based emulator framework, $\texttt{Mangrove}$, and show that it emulates the galactic stellar mass, cold gas mass and metallicity, instantaneous and time-averaged star formation rate, and black hole mass -- as predicted by a SAM -- with root mean squared error up to two times lower than other methods across a $(75 Mpc/h)^3$ simulation box in 40 seconds, 4 orders of magnitude faster than the SAM. We show that $\texttt{Mangrove}$ allows for quantification of the dependence of galaxy properties on merger history. We compare our results to the current state of the art in the field and show significant improvements for all target properties. $\texttt{Mangrove}$ is publicly available.Comment: 15 pages, 9 figures, 3 tables, 10 pages of Appendices. Accepted for publication in Ap

Galaxy assembly bias and large-scale distribution: a comparison between IllustrisTNG and a semi-analytic model

In this work, we compare large scale structure observables for stellar mass selected samples at z = 0, as predicted by two galaxy models, the hydrodynamical simulation IllustrisTNG and the Santa-Cruz semi-analytic model (SC-SAM). Although both models have been independently calibrated to match observations, rather than each other, we find good agreement between the two models for two-point clustering and galaxy assembly bias signatures. The models also show a qualitatively similar response of occupancy and clustering to secondary halo parameters other than mass, such as formation history and concentration, although with some quantitative differences. Thus, our results demonstrate that the galaxy–halo relationships in SC-SAM and TNG are quite similar to first order. However, we also find areas in which the models differ. For example, we note a strong correlation between halo gas content and environment in TNG, which is lacking in the SC-SAM, as well as differences in the occupancy predictions for low-mass haloes. Moreover, we show that higher order statistics, such as cumulants of the density field, help us to accurately describe the galaxy distribution and discriminate between models that show degenerate behaviour for two-point statistics. Our results suggest that SAMs are a promising cost-effective and intuitive method for generating mock catalogues for next generation cosmological surveys

Constraining cosmology with machine learning and galaxy clustering: the CAMELS-SAM suite

As the next generation of large galaxy surveys come online, it is becoming increasingly important to develop and understand the machine learning tools that analyze big astronomical data. Neural networks are powerful and capable of probing deep patterns in data, but must be trained carefully on large and representative data sets. We developed and generated a new `hump' of the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project: CAMELS-SAM, encompassing one thousand dark-matter only simulations of (100 $h^{-1}$ cMpc)$^3$ with different cosmological parameters ($\Omega_m$ and $\sigma_8$) and run through the Santa Cruz semi-analytic model for galaxy formation over a broad range of astrophysical parameters. As a proof-of-concept for the power of this vast suite of simulated galaxies in a large volume and broad parameter space, we probe the power of simple clustering summary statistics to marginalize over astrophysics and constrain cosmology using neural networks. We use the two-point correlation function, count-in-cells, and the Void Probability Function, and probe non-linear and linear scales across $0.68<$ R $<27\ h^{-1}$ cMpc. Our cosmological constraints cluster around 3-8$\%$ error on $\Omega_{\text{M}}$ and $\sigma_8$, and we explore the effect of various galaxy selections, galaxy sampling, and choice of clustering statistics on these constraints. We additionally explore how these clustering statistics constrain and inform key stellar and galactic feedback parameters in the Santa Cruz SAM. CAMELS-SAM has been publicly released alongside the rest of CAMELS, and offers great potential to many applications of machine learning in astrophysics: https://camels-sam.readthedocs.io.Comment: 40 pages, 22 figures (11 made of subfigures

Lyα at Cosmic Dawn with a Simulated Roman Grism Deep Field

The slitless grism on the Nancy Grace Roman Space Telescope will enable deep near-infrared spectroscopy over a wide field of view. We demonstrate Roman's capability to detect Ly α galaxies at z > 7 using a multiple position angle (PA) observational strategy. We simulate Roman grism data using a realistic foreground scene from the COSMOS field. We also input fake Ly α galaxies spanning redshift z = 7.5–10.5 and a line-flux range of interest. We show how a novel data-cube search technique—CUBGRISM—originally developed for the Galaxy Evolution Explorer can be applied to Roman grism data to produce a Ly α flux-limited sample without the need for continuum detections. We investigate the impact of altering the number of independent PAs and exposure time. A deep Roman grism survey with 25 PAs and a total exposure time of 70 hr can achieve Ly α line depths comparable to the deepest z = 7 narrowband surveys ( L _Ly _α ≳ 10 ^43 erg s ^−1 ). Assuming a null result, where the opacity of the intergalactic medium (IGM) remains unchanged from z ∼ 7, this level of sensitivity will detect ∼400 deg ^−2 Ly α emitters from z = 7.25 to 8.75. A decline from this expected number density is the signature of an increasing neutral hydrogen fraction and the onset of reionization. Our simulations indicate that a deep Roman grism survey has the ability to measure the timing and magnitude of this decline, allowing us to infer the ionization state of the IGM and helping us to distinguish between models of reionization

Finding Peas in the Early Universe with JWST

The Early Release Observations (EROs) of JWST beautifully demonstrate the promise of JWST in characterizing the universe at Cosmic Dawn. We analyze the Near Infrared Spectrograph ERO spectra of three z ∼ 8 galaxies to determine their metallicities, gas temperatures, and ionization. These galaxies offer the first opportunity to understand the physical properties of Epoch-of-Reionization galaxies through detailed rest-optical emission-line spectroscopy. We show that these objects have metal abundances $12+\mathrm{log}[{\rm{O}}/{\rm{H}}]\,\approx$ 6.9–8.3, based on both the T _e method and on a recent calibration of the R _23 metallicity indicator. Since the spectra are some of the earliest science data from JWST, we compare several line ratios with values expected from robust physics, to validate our measurement procedures. We compare the abundances and emission-line ratios to a nearby sample of Green Pea galaxies—a population of nearby emission-line galaxies whose UV properties resemble Epoch-of-Reionization galaxies, and which often have large Lyman continuum escape fractions. The JWST data show striking further similarities between these high-redshift galaxies and nearby Green Peas. The z ∼ 8 galaxies span the metallicity range covered by Green Peas. They also show the compact morphology that is typical of emission-line-dominated galaxies at all redshifts. Based on these similarities with Green Peas, it is likely that these are the first rest-optical spectra of galaxies that are actively driving cosmological reionization