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N-body simulations provide most of our insight into the structure and evolution of galaxies, but our analyses of these are often heuristic and from simple statistics. We propose a method that discovers the dynamics in space and time together by finding the most correlated temporal signals in multiple time series of basis function expansion coefficients and any other data fields of interest. The method extracts the dominant trends in the spatial variation of the gravitational field along with any additional data fields through time. The mathematics of this method is known as multichannel singular spectrum analysis (M-SSA). In essence, M-SSA is a principal component analysis of the covariance of time series replicates, each lagged successively by some interval. The dominant principal component represents the trend that contains the largest fraction of the correlated signal. The next principal component is orthogonal to the first and contains the next largest fraction, and so on. Using a suite of previously analysed simulations, we find that M-SSA describes bar formation and evolution, including mode coupling and pattern-speed decay. We also analyse a new simulation tailored to study vertical oscillations of the bar using kinematic data. Additionally, and to our surprise, M-SSA uncovered some new dynamics in previously analysed simulations, underscoring the power of this new approach.
Wu & Peek (2020) predict SDSS-quality spectra based on Pan-STARRS broad-band textit{grizy} images using machine learning (ML). In this letter, we test their prediction for a unique object, UGC 2885 (Rubins galaxy), the largest and most massive, isola
Merging is potentially the dominate process in galaxy formation, yet there is still debate about its history over cosmic time. To address this we classify major mergers and measure galaxy merger rates up to z $sim$ 3 in all five CANDELS fields (UDS,
Singular Spectrum Analysis (SSA) or Singular Value Decomposition (SVD) are often used to de-noise univariate time series or to study their spectral profile. Both techniques rely on the eigendecomposition of the cor- relation matrix estimated after em
Various observational techniques have been used to survey galaxies and AGN, from X-rays to radio frequencies, both photometric and spectroscopic. I will review these techniques aimed at the study of galaxy evolution and of the role of AGNs and star f
We examine morphology-separated color-mass diagrams to study the quenching of star formation in $sim 100,000$ ($zsim0$) Sloan Digital Sky Survey (SDSS) and $sim 20,000$ ($zsim1$) Cosmic Assembly Near-Infrared Deep Extragalactic Legacy Survey (CANDELS