Learning to Predict the Cosmological Structure Formation

Matter evolved under influence of gravity from minuscule density fluctuations. Non-perturbative structure formed hierarchically over all scales, and developed non-Gaussian features in the Universe, known as the Cosmic Web. To fully understand the structure formation of the Universe is one of the holy grails of modern astrophysics. Astrophysicists survey large volumes of the Universe and employ a large ensemble of computer simulations to compare with the observed data in order to extract the full information of our own Universe. However, to evolve trillions of galaxies over billions of years even with the simplest physics is a daunting task. We build a deep neural network, the Deep Density Displacement Model (hereafter D$^3$M), to predict the non-linear structure formation of the Universe from simple linear perturbation theory. Our extensive analysis, demonstrates that D$^3$M outperforms the second order perturbation theory (hereafter 2LPT), the commonly used fast approximate simulation method, in point-wise comparison, 2-point correlation, and 3-point correlation. We also show that D$^3$M is able to accurately extrapolate far beyond its training data, and predict structure formation for significantly different cosmological parameters. Our study proves, for the first time, that deep learning is a practical and accurate alternative to approximate simulations of the gravitational structure formation of the Universe.Comment: 8 pages, 5 figures, 1 tabl

Detecting Galaxy-Filament Alignments in the Sloan Digital Sky Survey III

Previous studies have shown the filamentary structures in the cosmic web influence the alignments of nearby galaxies. We study this effect in the LOWZ sample of the Sloan Digital Sky Survey using the "Cosmic Web Reconstruction" filament catalogue. We find that LOWZ galaxies exhibit a small but statistically significant alignment in the direction parallel to the orientation of nearby filaments. This effect is detectable even in the absence of nearby galaxy clusters, which suggests it is an effect from the matter distribution in the filament. A nonparametric regression model suggests that the alignment effect with filaments extends over separations of 30-40 Mpc. We find that galaxies that are bright and early-forming align more strongly with the directions of nearby filaments than those that are faint and late-forming; however, trends with stellar mass are less statistically significant, within the narrow range of stellar mass of this sample.Comment: 14 pages, 13 figures. Accepted to the MNRA

Learning neutrino effects in Cosmology with Convolutional Neural Networks

Measuring the sum of the three active neutrino masses, $M_\nu$, is one of the most important challenges in modern cosmology. Massive neutrinos imprint characteristic signatures on several cosmological observables in particular on the large-scale structure of the Universe. In order to maximize the information that can be retrieved from galaxy surveys, accurate theoretical predictions in the non-linear regime are needed. Currently, one way to achieve those predictions is by running cosmological numerical simulations. Unfortunately, producing those simulations requires high computational resources -- seven hundred CPU hours for each neutrino mass case. In this work, we propose a new method, based on a deep learning network (U-Net), to quickly generate simulations with massive neutrinos from standard $\Lambda$CDM simulations without neutrinos. We computed multiple relevant statistical measures of deep-learning generated simulations, and conclude that our method accurately reproduces the 3-dimensional spatial distribution of matter down to non-linear scales: $k < 0.7$ h/Mpc. Finally, our method allows us to generate massive neutrino simulations 10,000 times faster than the traditional methods.Comment: 8 pages, 7 figure

A comparative study of pre-service teachers' perceptions on STEAM education in UK and China

As more countries emphasize the development of science, technology, engineering, art, and mathematics (STEAM) education, the training of professional pre-service teachers has received considerable attention. To explore Chinese and UK preservice teachers' understanding of STEAM education, their willingness to engage in STEAM-related occupations, and their attitudes toward various STEAM disciplines, this study designed a questionnaire to investigate the perceptions of 109 and 379 preservice teachers from the United Kingdom and China, respectively. A quantitative analysis revealed the following: (1) Preservice teachers lacked the understanding of STEAM education in general. (2) Chinese and UK preservice teachers had different overall understandings of STEAM education. (3) Both Chinese and UK preservice teachers had different opinions about the role of art in STEAM. (4) The scores of Chinese preservice teachers in the semantic questionnaire in each discipline were significantly higher than those of the UK teachers, and significant differences in gender and profession were observed. (5) No significant differences were observed between the total scores of the UK and Chinese participants on the career interest questionnaire. Finally, we combined the experiences of the Chinese and UK preservice teachers to provide recommendations for teacher training

Inhibition of Interferon-Gamma-Stimulated Melanoma Progression by Targeting Neuronal Nitric Oxide Synthase (nNOS)

Interferon-gamma (IFN-γ) is shown to stimulate melanoma development and progression. However, the underlying mechanism has not been completely defined. Our study aimed to determine the role of neuronal nitric oxide synthase (nNOS)-mediated signaling in IFN-γ-stimulated melanoma progression and the anti-melanoma effects of novel nNOS inhibitors. Our study shows that IFN-γ markedly induced the expression levels of nNOS in melanoma cells associated with increased intracellular nitric oxide (NO) levels. Co-treatment with novel nNOS inhibitors effectively alleviated IFN-γ-activated STAT1/3. Further, reverse phase protein array (RPPA) analysis demonstrated that IFN-γ induced the expression of HIF1α, c-Myc, and programmed death-ligand 1 (PD-L1), in contrast to IFN-α. Blocking the nNOS-mediated signaling pathway using nNOS-selective inhibitors was shown to effectively diminish IFN-γ-induced PD-L1 expression in melanoma cells. Using a human melanoma xenograft mouse model, the in vivo studies revealed that IFN-γ increased tumor growth compared to control, which was inhibited by the co-administration of nNOS inhibitor MAC-3-190. Another nNOS inhibitor, HH044, was shown to effectively inhibit in vivo tumor growth and was associated with reduced PD-L1 expression levels in melanoma xenografts. Our study demonstrates the important role of nNOS-mediated NO signaling in IFN-γ-stimulated melanoma progression. Targeting nNOS using highly selective small molecular inhibitors is a unique and effective strategy to improve melanoma treatment