Revisiting the Lyman Continuum Escape Crisis: Predictions for z > 6 from Local Galaxies

The intrinsic escape fraction of ionizing Lyman continuum photons ($f_{esc}$) is crucial to understand whether galaxies are capable of reionizing the neutral hydrogen in the early universe at z>6. Unfortunately, it is not possible to access $f_{esc}$ at z>4 with direct observations and the handful of measurements from low redshift galaxies consistently find $f_{esc}$ < 10%, while at least $f_{esc}$ ~ 10% is necessary for galaxies dominate reionization. Here, we present the first empirical prediction of $f_{esc}$ at z>6 by combining the (sparsely populated) relation between [OIII]/[OII] and $f_{esc}$ with the redshift evolution of [OIII]/[OII] as predicted from local high-z analogs selected by their H$\alpha$ equivalent-width. We find $f_{esc}$ = $5.7_{-3.3}^{+8.3}$% at z=6 and $f_{esc}$ = $10.4_{-6.3}^{+15.5}$% at z=9 for galaxies with log(M/M$_{sun}$) ~ 9.0 (errors given as 1$\sigma$). However, there is a negative correlation with stellar mass and we find up to 50% larger $f_{esc}$ per 0.5 dex decrease in stellar mass. The population averaged escape fraction increases according to $f_{esc}$ = $f_{esc,0} ((1+z)/3)^a$, with $f_{esc,0} = 2.3 \pm 0.05$% and $a=1.17 \pm 0.02$ at z > 2 for log(M/M$_{sun}$) ~ 9.0. With our empirical prediction of $f_{esc}$ (thus fixing an important previously unknown variable) and further reasonable assumption on clumping factor and the production efficiency of Lyman continuum photons, we conclude that the average population of galaxies is just capable to reionize the universe by z ~ 6.Comment: 10 pages, 4 figure, 1 table. Accepted by Ap

SILVERRUSH. VIII. Spectroscopic Identifications of Early Large-scale Structures with Protoclusters over 200 Mpc at z ~ 6–7: Strong Associations of Dusty Star-forming Galaxies

We have obtained three-dimensional maps of the universe in ~200 × 200 × 80 comoving Mpc^3 (cMpc^3) volumes each at z = 5.7 and 6.6 based on a spectroscopic sample of 179 galaxies that achieves ≳80% completeness down to the Lyα luminosity of log(L_(Lyα)/[erg s^(−1)]) = 43.0, based on our Keck and Gemini observations and the literature. The maps reveal filamentary large-scale structures and two remarkable overdensities made out of at least 44 and 12 galaxies at z = 5.692 (z57OD) and z = 6.585 (z66OD), respectively, making z66OD the most distant overdensity spectroscopically confirmed to date, with >10 spectroscopically confirmed galaxies. We compare spatial distributions of submillimeter galaxies at z ≃ 4–6 with our z = 5.7 galaxies forming the large-scale structures, and detect a 99.97% signal of cross-correlation, indicative of a clear coincidence of dusty star-forming galaxy and dust-unobscured galaxy formation at this early epoch. The galaxies in z57OD and z66OD are actively forming stars with star-formation rates (SFRs) ≳5 times higher than the main sequence, and particularly the SFR density in z57OD is 10 times higher than the cosmic average at the redshift (a.k.a. the Madau-Lilly plot). Comparisons with numerical simulations suggest that z57OD and z66OD are protoclusters that are progenitors of the present-day clusters with halo masses of ~10^(14) M_⊙

An alternate approach to measure specific star formation rates at 2<z<7

We trace the specific star formation rate (sSFR) of massive star-forming galaxies ($\gtrsim\!10^{10}\,\mathcal{M}_\odot$) from $z\sim2$ to 7. Our method is substantially different from previous analyses, as it does not rely on direct estimates of star formation rate, but on the differential evolution of the galaxy stellar mass function (SMF). We show the reliability of this approach by means of semi-analytical and hydrodynamical cosmological simulations. We then apply it to real data, using the SMFs derived in the COSMOS and CANDELS fields. We find that the sSFR is proportional to $(1+z)^{1.1\pm0.2}$ at $z>2$, in agreement with other observations but in tension with the steeper evolution predicted by simulations from $z\sim4$ to 2. We investigate the impact of several sources of observational bias, which however cannot account for this discrepancy. Although the SMF of high-redshift galaxies is still affected by significant errors, we show that future large-area surveys will substantially reduce them, making our method an effective tool to probe the massive end of the main sequence of star-forming galaxies.Comment: ApJ accepte

Predicting H{\alpha} emission line galaxy counts for future galaxy redshift surveys

Knowledge of the number density of H$\alpha$ emitting galaxies is vital for assessing the scientific impact of the Euclid and WFIRST missions. In this work we present predictions from a galaxy formation model, Galacticus, for the cumulative number counts of H$\alpha$-emitting galaxies. We couple Galacticus to three different dust attenuation methods and examine the counts using each method. A $\chi^2$ minimisation approach is used to compare the model predictions to observed galaxy counts and calibrate the dust parameters. We find that weak dust attenuation is required for the Galacticus counts to be broadly consistent with the observations, though the optimum dust parameters return large values for $\chi^2$, suggesting that further calibration of Galacticus is necessary. The model predictions are also consistent with observed estimates for the optical depth and the H$\alpha$ luminosity function. Finally we present forecasts for the redshift distributions and number counts for two Euclid-like and one WFIRST-like survey. For a Euclid-like survey with redshift range $0.9\leqslant z\leqslant 1.8$ and H$\alpha+{\rm [NII]}$ blended flux limit of $2\times 10^{-16}{\rm erg}\,{\rm s}^{-1}\,{\rm cm}^{-2}$ we predict a number density between 3900--4800 galaxies per square degree. For a WFIRST-like survey with redshift range $1\leqslant z\leqslant 2$ and blended flux limit of $1\times 10^{-16}{\rm erg}\,{\rm s}^{-1}\,{\rm cm}^{-2}$ we predict a number density between 10400--15200 galaxies per square degree.Comment: 21 pages (including appendix), 12 figures, 6 tables. Accepted b

Revisiting the Lyman Continuum Escape Crisis: Predictions for z > 6 from Local Galaxies

The intrinsic escape fraction of ionizing Lyman continuum photons (f_(esc)) is crucial to understanding whether galaxies are capable of reionizing the neutral hydrogen in the early universe at z > 6. Unfortunately, it is not possible to access f_(esc) at z > 4 with direct observations, and the handful of measurements from low-redshift galaxies consistently find f_(esc) 6 by combining the (sparsely populated) relation between [O III]/[O II] and f_(esc) with the redshift evolution of [O III]/[O II] as predicted from local high-z analogs selected by their Hα equivalent width. We find f_(esc) = 5.7(+8.3)(-3.3)% at z = 6 and f_(esc) = 10.4(+15.5)(-6.3)% at z = 9 for galaxies with log(M/M_⊙) ~ 9.0 (errors given as 1σ). However, there is a negative correlation with stellar mass and we find up to 50% larger f_(esc) per 0.5 dex decrease in stellar mass. The population-averaged escape fraction increases according to f_(esc) = f_(esc,0) ((1 + z) 3)^α, with f_(esc,0) = (2.3 ± 0.05)% and α = 1.17 ± 0.02 at z > 2 for log(M/M_⊙) ~ 9.0. With our empirical prediction of f_(esc) (thus fixing an important, previously unknown variable) and further reasonable assumptions on clumping factor and the production efficiency of Lyman continuum photons, we conclude that the average population of galaxies is just capable of reionizing the universe by z ∼ 6

How to Find Variable Active Galactic Nuclei with Machine Learning

Machine-learning (ML) algorithms will play a crucial role in studying the large data sets delivered by new facilities over the next decade and beyond. Here, we investigate the capabilities and limits of such methods in finding galaxies with brightness-variable active galactic nuclei (AGNs). Specifically, we focus on an unsupervised method based on self-organizing maps (SOM) that we apply to a set of nonparametric variability estimators. This technique allows us to maintain domain knowledge and systematics control while using all the advantages of ML. Using simulated light curves that match the noise properties of observations, we verify the potential of this algorithm in identifying variable light curves. We then apply our method to a sample of ~8300 WISE color-selected AGN candidates in Stripe 82, in which we have identified variable light curves by visual inspection. We find that with ML we can identify these variable classified AGN with a purity of 86% and a completeness of 66%, a performance that is comparable to that of more commonly used supervised deep-learning neural networks. The advantage of the SOM framework is that it enables not only a robust identification of variable light curves in a given data set, but it is also a tool to investigate correlations between physical parameters in multidimensional space—such as the link between AGN variability and the properties of their host galaxies. Finally, we note that our method can be applied to any time-sampled light curve (e.g., supernovae, exoplanets, pulsars, and other transient events)