The dynamical state of galaxy groups at intermediate redshifts can provide information about the growth of structure in the universe. We examine three goodness-of-fit tests, the Anderson--Darling (A-D), Kolmogorov and chi-squared tests, in order to determine which statistical tool is best able to distinguish between groups that are relaxed and those that are dynamically complex. We perform Monte Carlo simulations of these three tests and show that the chi-squared test is profoundly unreliable for groups with fewer than 30 members. Power studies of the Kolmogorov and A-D tests are conducted to test their robustness for various sample sizes. We then apply these tests to a sample of the second Canadian Network for Observational Cosmology Redshift Survey (CNOC2) galaxy groups and find that the A-D test is far more reliable and powerful at detecting real departures from an underlying Gaussian distribution than the more commonly used chi-squared and Kolmogorov tests. We use this statistic to classify a sample of the CNOC2 groups and find that 34 of 106 groups are inconsistent with an underlying Gaussian velocity distribution, and thus do not appear relaxed. In addition, we compute velocity dispersion profiles (VDPs) for all groups with more than 20 members and compare the overall features of the Gaussian and non-Gaussian groups, finding that the VDPs of the non-Gaussian groups are distinct from those classified as Gaussian.
The spectrum of a galaxy contains information about its physical properties. Classifying spectra using templates helps elucidate the nature of a galaxy's energy sources. In this paper, we investigate the use of self-organizing maps in classifying galaxy spectra against templates. We trained semi-supervised self-organizing map networks using a set of templates covering the wavelength range from far ultraviolet to near infrared. The trained networks were used to classify the spectra of a sample of 142 galaxies with 0.5 < z < 1 and the results compared to classifications performed using K-means c…
The regular or chaotic character of orbits of stars moving in the meridional plane (R,z) of an axially symmetric elliptical galaxy with a dense, massive spherical nucleus and a dark matter halo component is under investigation. In particular, we explore how the flattening of an elliptical galaxy influences the overall orbital structure of the system, by computing in each case the percentage of chaotic orbits, as well as the percentages of orbits composing the main regular families. In an attempt to discriminate safely and with certainty between regular and chaotic motion, we use the Smaller AL…
The taxonomy of galaxy morphology is critical in astrophysics as the morphological properties are powerful tracers of galaxy evolution. With the upcoming Large-scale Imaging Surveys, billions of galaxy images challenge astronomers to accomplish the classification task by applying traditional methods or human inspection. Consequently, machine learning, in particular supervised deep learning, has been widely employed to classify galaxy morphologies recently due to its exceptional automation, efficiency, and accuracy. However, supervised deep learning requires extensive training sets, which cause…
Convolutional Neural Networks for classifying galaxy mergers: Can faint tidal features aid in classifying mergers?
College of Natural Sciences, Kyungpook National University（庆北国立大学自然科学学院）；Department of Astronomy, Space Science and Geology, Chungnam National University（忠南国立大学天文、宇宙科学与地质学系）；University of Science and Technology (UST)（韩国科学技术院）；Korea Astronomy and Space Science Institute（韩国天文学与空间科学研究所）
Optical spectra of galaxies and quasars from large cosmological surveys are used to measure redshifts and infer distances. They are also rich with information on the intrinsic properties of these astronomical objects. However, their physical interpretation can be challenging due to the substantial number of degrees of freedom, various sources of noise, and degeneracies between physical parameters that cause similar spectral characteristics. To gain deeper insights into these degeneracies, we apply two unsupervised machine learning frameworks to a sample from the Sloan Digital Sky Survey data r…
Aims. This work investigates the potential of using the wavelength-dependence of galaxy structural parameters (Sérsic index, n, and effective radius, Re) to separate galaxies into distinct types. Methods. A sample of nearby galaxies with reliable visual morphologies is considered, for which we measure structural parameters by fitting multi-wavelength single-Sérsic models. Additionally, we use a set of artificially redshifted galaxies to test how these classifiers behave when the signal-to-noise decreases. Results. We show that the wavelength-dependence of n may be employed to separate visually…
Vishwakarma Institute of Information Technology（维施瓦卡尔马信息技术学院）
In recent decades, large-scale sky surveys such as Sloan Digital Sky Survey (SDSS) have resulted in generation of tremendous amount of data. The classification of this enormous amount of data by astronomers is time consuming. To simplify this process, in 2007 a volunteer-based citizen science project called Galaxy Zoo was introduced, which has reduced the time for classification by a good extent. However, in this modern era of deep learning, automating this classification task is highly beneficial as it reduces the time for classification. For the last few years, many algorithms have been prop…
The Legacy Survey of Space and Time (LSST) at the Vera C. Rubin Observatory will discover tens of thousands of extragalactic transients each night. The high volume of alerts demands immediate classification of transient types in order to prioritize observational follow-ups before events fade away. We use host galaxy features to classify transients, thereby providing classification upon discovery. In contrast to past work that focused on distinguishing Type Ia and core-collapse supernovae (SNe) using host galaxy features that are not always accessible (e.g., morphology), we determine the relati…
Classifying catalog objects as stars, galaxies, or AGN is a crucial part of any statistical study of galaxies. We describe our pipeline for binary (star/galaxy) and multiclass (star/galaxy/Type I AGN/Type II AGN) classification developed for the very deep CLAUDS+HSC-SSP $u^*grizy$ dataset. Our method uses the XGBoost implementation of Gradient Boosted Trees (GBT) to train ensembles of models which take photometry, colours, maximum surface brightnesses, and effective radii from all available bands as input, and output the probability that an object belongs to each of the classes under considera…
The ordered or chaotic character of orbits of stars moving in the meridional $(R,z)$ plane of an analytic axisymmetric time-independent disk galaxy model with an additional spherically symmetric central nucleus is investigated. Our aim is to determine how the total energy influences the orbital structure of the galaxy. For this purpose we monitor how the percentage of chaotic orbits as well as the rates of orbits composing the main regular families evolve as a function of the value of the energy. In order to distinguish with certainty between chaotic and ordered motion we use the SALI method i…
Traditional classification for subclass of the Seyfert galaxies is visual inspection or using a quantity defined as a flux ratio between the Balmer line and forbidden line. One algorithm of deep learning is Convolution Neural Network (CNN) and has shown successful classification results. We building a 1-dimension CNN model to distinguish Seyfert 1.9 spectra from Seyfert 2 galaxies. We find our model can recognize Seyfert 1.9 and Seyfert 2 spectra with an accuracy over 80% and pick out an additional Seyfert 1.9 sample which was missed by visual inspection. We use the new Seyfert 1.9 sample to i…
A non-zero mutual information between morphology of a galaxy and its large-scale environment is known to exist in SDSS upto a few tens of Mpc. It is important to test the statistical significance of these mutual information if any. We propose three different methods to test the statistical significance of these non-zero mutual information and apply them to SDSS and Millennium Run simulation. We randomize the morphological information of SDSS galaxies without affecting their spatial distribution and compare the mutual information in the original and randomized datasets. We also divide the galax…
The relative roles of mergers and star formation in regulating galaxy growth are still a matter of intense debate. We here present our DECODE, a new Discrete statistical sEmi-empiriCal mODEl specifically designed to predict rapidly and efficiently, in a full cosmological context, galaxy assembly and merger histories for any given input stellar mass-halo mass (SMHM) relation. DECODE generates object-by-object dark matter merger trees (hence discrete) from accurate subhalo mass and infall redshift probability functions (hence statistical) for all subhaloes, including those residing within other …
In recent years, large scale data intensive astronomical surveys have resulted in more detailed images being produced than scientists can manually classify. Even attempts to crowd-source this work will soon be outpaced by the large amount of data generated by modern surveys. This has brought into question the viability of human-based methods for classifying galaxy morphology. While supervised learning methods require datasets with existing labels, unsupervised learning techniques do not. Therefore, this paper implements unsupervised learning techniques to classify the Galaxy Zoo DECaLS dataset…
We use the Dark-ages, Reionization And Galaxy-formation Observables from Numerical Simulations (DRAGONS) framework to investigate the effect of galaxy-formation physics on the morphology and statistics of ionized hydrogen (HII) regions during the Epoch of Reioinization (EoR). DRAGONS self-consistently couples a semi-analytic galaxy-formation model with the inhomogeneous ionizing UV background, and can therefore be used to study the dependence of morphology and statistics of reionization on feedback phenomena of the ionizing source galaxy population. Changes in galaxy-formation physics modify t…
We use the mass discrepancy-acceleration relation (the correlation between the ratio of total-to-visible mass and acceleration in galaxies; MDAR) to test the galaxy-halo connection. We analyse the MDAR using a set of 16 statistics that quantify its four most important features: its shape, its scatter, the presence of a "characteristic acceleration scale," and the correlation of its residuals with other galaxy properties. We construct an empirical framework for the galaxy-halo connection in LCDM to generate predictions for these statistics, starting with conventional correlations (halo abundanc…
We describe a statistical approach for measuring the influence that a galaxy's closest companion has on the galaxy's properties out to arbitrarily wide separations. We begin by identifying the closest companion for every galaxy in a large spectroscopic sample of Sloan Digital Sky Survey galaxies. We then characterize the local environment of each galaxy by using the number of galaxies within 2 Mpc and by determining the isolation of the galaxy pair from other neighbouring galaxies. We introduce a sophisticated algorithm for creating a statistical control sample for each galaxy, matching on ste…
We investigate limits on the extinction values of Type Ia supernovae to statistically determine the most probable color excess, E(B-V), with galactocentric distance, and use these statistics to determine the absorption-to-reddening ratio, $R_V$, for dust in the host galaxies. We determined pixel-based dust mass surface density maps for 59 galaxies from the Key Insight on Nearby Galaxies: a Far-Infrared Survey with \textit{Herschel} (KINGFISH, Kennicutt et al. (2011)). We use Type Ia supernova spectral templates (Hsiao et al. 2007) to develop a Monte Carlo simulation of color excess E(B-V) with…
We use machine learning to classify galaxies according to their HI content, based on both their optical photometry and environmental properties. The data used for our analyses are the outputs in the range $z = 0-1$ from MUFASA cosmological hydrodynamic simulation. In our previous paper, where we predicted the galaxy HI content using the same input features, HI rich galaxies were only selected for the training. In order for the predictions on real observation data to be more accurate, the classifiers built in this study will first establish if a galaxy is HI rich ($\rm{log(M_{HI}/M_{*})} > -2 $…
Understanding the dynamics of galaxy triplet systems is one of the significant ways of obtaining insight into the dynamics of large galaxy clusters. Toward that aim, we present a detailed study of all isolated triplet systems (total of 315) taken from the `SDSS-based catalogue of Isolated Triplets' (SIT). In addition, we compared our results with those obtained for a sample of triplets from the Local Supercluster (LS), SDSS-triplets, Tully's catalogue, Wide (W) and Compact (K)-triplets. In addition, we performed the correlation between the dynamical parameters and the Large Scale Structure (LS…
The most probable state of an infinite self-gravitating gas in the dynamical equilibrium is defined by `gravitational haziness', a parameter representing many-body effects and formally like the temperature in the case of thermal equilibrium. A kinetic equation is constructed using a concept of statistical equipartition of the virial among subsystems of the self-gravitating gas. A closed equation for the gravitational potential is conjectured as a special property of the kinetic equation. An equilibrium particle distribution function in the phase space, an analog of the Maxwell-Boltzmann weight…
We apply four statistical learning methods to a sample of $7941$ galaxies ($z<0.06$) from the Galaxy and Mass Assembly (GAMA) survey to test the feasibility of using automated algorithms to classify galaxies. Using $10$ features measured for each galaxy (sizes, colours, shape parameters \& stellar mass) we apply the techniques of Support Vector Machines (SVM), Classification Trees (CT), Classification Trees with Random Forest (CTRF) and Neural Networks (NN), returning True Prediction Ratios (TPRs) of $75.8\%$, $69.0\%$, $76.2\%$ and $76.0\%$ respectively. Those occasions whereby all four algor…
We study the impact of cluster environment on the morphology of a sample of 4304 extended radio galaxies from Radio Galaxy Zoo. A total of 87% of the sample lies within a projected 15 Mpc of an optically identified cluster. Brightest cluster galaxies (BCGs) are more likely than other cluster members to be radio sources, and are also moderately bent. The surface density as a function of separation from cluster center of non-BCG radio galaxies follows a power law with index $-1.10\pm 0.03$ out to $10~r_{500}$ ($\sim 7~$Mpc), which is steeper than the corresponding distribution for optically sele…
We study the statistical properties of 320 bulges of disk galaxies in the Carnegie-Irvine Galaxy Survey, using robust structural parameters of galaxies derived from image fitting. We apply the Kormendy relation to classify classical and pseudo bulges and characterize bulge dichotomy with respect to bulge structural properties and physical properties of host galaxies. We confirm previous findings that pseudo bulges on average have smaller Sérsic indices, smaller bulge-to-total ratios, and fainter surface brightnesses when compared with classical bulges. Our sizable sample statistically shows th…
In order to study the internal dynamics of actual galaxy pairs, we need to derive the probability distribution function (PDF) of true 3D (orbital) intervelocities and interdistances between pair members from their observed (projected) values, and of the pair masses from Kepler's third law. Our Isolated Galaxy Pair Catalog (IGPC) of 13114 pairs \cite{Nottale2018a} is used here for this research. The algorithms of statistical deprojection elaborated in \cite{Nottale2018b} are applied to these observational data. We derive the orbital velocity PDFs for the whole catalog and for several selected s…
In the field of Astrobiology, the precise location, prevalence and age of potential extraterrestrial intelligence (ETI) have not been explicitly explored. Here, we address these inquiries using an empirical galactic simulation model to analyze the spatial-temporal variations and the prevalence of potential ETI within the Galaxy. This model estimates the occurrence of ETI, providing guidance on where to look for intelligent life in the Search for ETI (SETI) with a set of criteria, including well-established astrophysical properties of the Milky Way. Further, typically overlooked factors such as…
We present criteria for separately classifying stars and unresolved background galaxies in photometric catalogs generated with the point spread function (PSF) fitting photometry software DOLPHOT from images taken of Draco II, WLM, and M92 with the Near Infrared Camera (NIRCam) on JWST. Photometric quality metrics from DOLPHOT in one or two filters can recover a pure sample of stars. Conversely, colors formed between short-wavelength (SW) and long-wavelength (LW) filters can be used to effectively identify pure samples of galaxies. Our results highlight that the existing DOLPHOT output paramete…
Large galaxy samples from multi-object IFS surveys now allow for a statistical analysis of the z~0 galaxy population using resolved kinematics. However, the improvement in number statistics comes at a cost, with multi-object IFS survey more severely impacted by the effect of seeing and lower S/N. We present an analysis of ~1800 galaxies from the SAMI Galaxy Survey and investigate the spread and overlap in the kinematic distributions of the spin parameter proxy $λ_{Re}$ as a function of stellar mass and ellipticity. For SAMI data, the distributions of galaxies identified as regular and non-regu…
According to their specific star formation rate (sSFR), galaxies are often divided into `star-forming' and `passive' populations. It is argued that the former define a narrow `Main Sequence of Star-Forming Galaxies' (MSSF) of the form $\sSFR(\Mstar)$, whereas `passive' galaxies feature negligible levels of star formation activity. Here we use data from the Sloan Digital Sky Survey and the Galaxy and Mass Assembly survey at $z<0.1$ to constrain the conditional probability of the specific star formation rate at a given stellar mass. We show that the whole population of galaxies in the local Univ…
Statistical inference of fast radio burst environments using galaxy number density
National Chung Hsing University（中兴大学）；National Tsing Hua University（清华大学）；National Centre for Radio Astrophysics (NCRA–TIFR)（国家射电天文中心（NCRA-TIFR））；Tohoku University（东北大学）
本研究利用WISE x PS1星表，通过KS检验统计分析CHIME星表1中19个重复FRB与253个非重复FRB的环境，发现二者环境无显著差异，且FRB或偏好低密度星系环境，凸显大样本FRB研究的必要性。
