First Evidence of Intrinsic Alignments of Red Galaxies at z > 1: Cross Correlation between CFHTLenS and FastSound Samples

We report the first evidence for intrinsic alignment (IA) of red galaxies at z > 1. We measure the gravitational shear-intrinsic ellipticity cross correlation function at z ∼ 1.3 using galaxy positions from the FastSound spectroscopic survey and galaxy shapes from the Canada France Hawaii telescope lensing survey data. Adopting the nonlinear alignment model, we obtain a 2.4σ level detection of the IA amplitude A LA = 27.48 − 11.54 + 11.53 (and 2.6σ with A LA = 29.43 − 11.49 + 11.48 when weak lensing contaminations are taken into account), which is larger than the value extrapolated from the constraints obtained at lower redshifts. Our measured IA is translated into a ∼20% contamination of the weak-lensing power spectrum for the red galaxies. This marginal detection of IA for red galaxies at z > 1 motivates the continuing investigation of the nature of IA for weak lensing studies. Furthermore, our result provides the first step to utilize IA measurements in future high-z surveys as a cosmological probe, complementary to galaxy clustering and lensing.

Detection and Analysis of Galaxy Clusters Via a Hierarchical Algorithmic Approach

This paper is a discussion of our analysis of galaxy clustering using an algorithmic approach. Our algorithmic galaxy clustering analysis and galaxy morphology analysis produced promising results in identifying galaxy clusters at different scales, and we used these clusters to draw correlations between cluster membership and galaxy properties such as size and color. We also compare our work in algorithmic galaxy clustering to existing work using machine learning, showing where our results are consistent with previous work, and where they differ from previous work. Overall, we found our research to be insightful into how algorithms perform when finding clusters of galaxies, and we find many possible follow up questions to explore in the future.

Predicting halo occupation and galaxy assembly bias with machine learning

Understanding the impact of halo properties beyond halo mass on the clustering of galaxies (namely galaxy assembly bias) remains a challenge for contemporary models of galaxy clustering. We explore the use of machine learning to predict the halo occupations and recover galaxy clustering and assembly bias in a semi-analytic galaxy formation model. For stellar mass selected samples, we train a random forest algorithm on the number of central and satellite galaxies in each dark matter halo. With the predicted occupations, we create mock galaxy catalogues and measure the clustering and assembly bias. Using a range of halo and environment properties, we find that the machine learning predictions of the occupancy variations with secondary properties, galaxy clustering, and assembly bias are all in excellent agreement with those of our target galaxy formation model. Internal halo properties are most important for the central galaxies prediction, while environment plays a critical role for the satellites. Our machine learning models are all provided in a usable format. We demonstrate that machine learning is a powerful tool for modelling the galaxy–halo connection, and can be used to create realistic mock galaxy catalogues which accurately recover the expected occupancy variations, galaxy clustering, and galaxy assembly bias, imperative for cosmological analyses of upcoming surveys.