We explore the connection between the stellar component of galaxies and their host halos during the epoch of reionization (5 ≤ z ≤ 10) using the CROC (Cosmic Reionization on Computers) simulations. We compare simulated galaxies with observations and find that CROC underpredicts the abundance of luminous galaxies when compared to observed UV luminosity functions, and analogously the most massive galaxies when compared to observed stellar mass functions. We can trace the deficit of star formation to high redshifts, where the slope of the star formation rate to stellar mass relation is consistent with observations, but the normalization is systematically low. This results in a star formation rate density and stellar mass density that are systematically offset from observations. However, the less luminous or lower stellar mass objects have luminosities and stellar masses that agree fairly well with observational data. We explore the stellar-to-halo mass ratio (SHMR), a key quantity that is difficult to measure at high redshifts and that models do not consistently predict. In CROC, the SHMR decreases with redshift, a trend opposite to some abundance-matching studies. These discrepancies uncover where future effort should be focused in order to improve the fidelity of modeling cosmic reionization. We also compare the CROC galaxy bias with observational measurements using Lyman-break galaxy samples, finding reasonable consistency.

Gravitational lensing directly probes the underlying mass distribution of lensing systems, the high redshift universe, and cosmological models. The advent of large scale surveys such as the Large Synoptic Sky Telescope (LSST) and Euclid has prompted a need for automatic and efficient identification of strong lensing systems. We present (1) a strong lensing identification pipeline, and (2) a mock LSST dataset with strong galaxy-galaxy lenses. In this first application, we employ a fast feature extraction method, Histogram of Oriented Gradients (HOG), to capture edge patterns that are characteristic of strong gravitational arcs in galaxy-galaxy strong lensing. We use logistic regression to train a supervised classifier model on the HOG of HST-and LSST-like images. We use the area under the curve (AUC) of a Receiver Operating Characteristic (ROC) curve to assess model performance; AUC = 1.0 is an ideal classifier, and AUC = 0.5 is no better than randomly guessing. Our best performing models on a training set of 10,000 lens containing images and 10,000 non-lens containing images exhibit an AUC of 0.975 for an HST-like sample. However, for one exposure of LSST, our model only reaches an AUC of 0.625. For 10-year mock LSST observations, the AUC improves to 0.809. Model performance appears to continually improve with the size of the training set. Models trained on fewer images perform better in absence of the light from the lens galaxy. However, with larger training data sets, information from the lens galaxy actually improves model performance. Our results demonstrate an efficient and effective method for automatically identifying strong lenses that captures much of the complexity of the arc finding problem. The linear classifier both runs on a personal laptop and can easily scale to large data sets on a computing cluster, all while using existing open source tools.

We consider cosmological models where dark energy is described by a dynamical field equipped with the Chameleon screening mechanism, which serves to hide its effects in local dense regions and to conform to Solar System observations. In these models, there is no universal gravitational coupling and here we study the effective couplings that determine the force between massive objects, GN, and the propagation of gravitational waves, Ggw. In particular, we revisit the Chameleon screening mechanism without neglecting the time dependence of the galactic environment where local regions are embedded in, and analyze the induced time evolution on GN and Ggw, which can be tested with Lunar Laser Ranging and direct gravitational waves observations. We explicitly show how and why these two couplings generically differ. We also find that due to the particular way the Chameleon screening mechanism works, their time evolutions are highly suppressed in the weak-field non-relativistic approximation.

We examine the reionization history of present-day galaxies by explicitly tracing the building blocks of halos from the Cosmic Reionization On Computers project. We track dark matter particles that belong to z = 0 halos and extract the neutral fractions at corresponding positions during rapid global reionization. The resulting particle reionization histories allow us to explore different definitions of a halo's reionization redshift and to account for the neutral content of the interstellar medium. Consistent with previous work, we find a systematic trend of reionization redshift with mass -present day halos with higher masses have earlier reionization times. Finally, we quantify the spread of reionization times within each halo, which also has a mass dependence.

The low-redshift mass-metallicity relation (MZR) is well studied, but the high-redshift MZR remains difficult to observe. To study the early MZR further, we analyze the Cosmic Reionization on Computers (CROC) simulations with a focus on the MZR from redshifts 5 to 10. We find that, across all redshifts, CROC galaxies exhibit similar stellar-phase and gas-phase MZRs that flatten with higher stellar mass. We attribute this flattening to the inaccurate star formation and feedback modeling in CROC (star formation is overly suppressed in massive CROC galaxies). In addition, we show that the ratio between stellar metallicity and gas metallicity (Z * /Z gas ) decreases as stellar age increases, meaning that in CROC galaxies, gas accretion rate is lower than metal production rate. With JWST we will be able to compare our predictions to observations of the Epoch of Reionization and understand better early galaxy formation.