DATA MINING THE GALAXY ZOO MERGERS

STEVEN BAEHR*, ARUN VEDACHALAM*, KIRK BORNE*, AND DANIEL SPONSELLER*

Abstract. Collisions between pairs of galaxies usually end in the coalescence (merger) of the two galaxies. Collisions and mergers are rare phenomena, yet they may signal the ultimate fate of most galaxies, including our own Milky Way. With the onset of massive collection of astronomical data, a computerized and automated method will be necessary for identifying those colliding galaxies worthy of more detailed study. This project researches methods to accomplish that goal. Astronomical data from the Sloan Digital Sky Survey (SDSS) and human-provided classifications on merger status from the Galaxy Zoo project are combined and processed with machine learning algorithms. The goal is to determine indicators of merger status based solely on discovering those automated pipeline-generated attributes in the astronomical database that correlate most strongly with the patterns identified through visual inspection by the Galaxy Zoo volunteers. In the end, we aim to provide a new and improved automated procedure for classification of collisions and mergers in future petascale astronomical sky surveys. Both information gain analysis (via the C4.5 decision tree algorithm) and cluster analysis (via the Davies-Bouldin Index) are explored as techniques for finding the strongest correlations between human-identified patterns and existing database attributes. Galaxy attributes measured in the SDSS green waveband images are found to represent the most influential of the attributes for correct classification of collisions and mergers. Only a nominal information gain is noted in this research, however, there is a clear indication of which attributes contribute so that a direction for further study is apparent.

The Sloan Digital Sky Survey (SDSS) is a comprehensive survey of the northern sky. This dataset contains a subset of this survey, of 100077 objects classified as galaxies, it includes a CSV file with a collection of information and a set of files for each object, namely JPG image files, FITS and spectra data. This dataset is used to train and explore the astromlp-models collection of deep learning models for galaxies characterisation.

The dataset includes a CSV data file where each row is an object from the SDSS database, and with the following columns (note that some data may not be available for all objects):

objid: unique SDSS object identifier

mjd: MJD of observation

plate: plate identifier

tile: tile identifier

fiberid: fiber identifier

run: run number

rerun: rerun number

camcol: camera column

field: field number

ra: right ascension

dec: declination

class: spectroscopic class (only objetcs with GALAXY are included)

subclass: spectroscopic subclass

modelMag_u: better of DeV/Exp magnitude fit for band u

modelMag_g: better of DeV/Exp magnitude fit for band g

modelMag_r: better of DeV/Exp magnitude fit for band r

modelMag_i: better of DeV/Exp magnitude fit for band i

modelMag_z: better of DeV/Exp magnitude fit for band z

redshift: final redshift from SDSS data z

stellarmass: stellar mass extracted from the eBOSS Firefly catalog

w1mag: WISE W1 "standard" aperture magnitude

w2mag: WISE W2 "standard" aperture magnitude

w3mag: WISE W3 "standard" aperture magnitude

w4mag: WISE W4 "standard" aperture magnitude

gz2c_f: Galaxy Zoo 2 classification from Willett et al 2013

gz2c_s: simplified version of Galaxy Zoo 2 classification (labels set)

Besides the CSV file a set of directories are included in the dataset, in each directory you'll find a list of files named after the objid column from the CSV file, with the corresponding data, the following directories tree is available:

sdss-gs/ ├── data.csv ├── fits ├── img ├── spectra └── ssel

Where, each directory contains:

img: RGB images from the object in JPEG format, 150x150 pixels, generated using the SkyServer DR16 API

fits: FITS data subsets around the object across the u, g, r, i, z bands; cut is done using the ImageCutter library

spectra: full best fit spectra data from SDSS between 4000 and 9000 wavelengths

ssel: best fit spectra data from SDSS for specific selected intervals of wavelengths discussed by Sánchez Almeida 2010

Changelog

v0.0.4 - Increase number of objects to ~100k.

v0.0.3 - Increase number of objects to ~80k.

v0.0.2 - Increase number of objects to ~60k.

v0.0.1 - Initial import.

We present the Photometry and Rotation Curve Observations from Extragalactic Surveys (PROBES) compendium of extended rotation curves for 3163 late-type spirals, with matching homogeneous multi-band photometry for 1677 of them. The raw data are given in the profiles.zip file. These .prof files contain the surface brightness profiles and rotation curves for each galaxy. The columns each profile are described in README_profiles. Also included are .aux files which give details of the photometry extraction with AutoProf.

The reduced data are contained in various .csv files. Each galaxy has a unique name which is given in the first column of each table. The file main_table.csv contains high level information about every galaxy including redshift, morphology, and which photometric bands are available. The model_fits.csv file contains fitting parameters for a number of parametric models which describe either the rotation curves or surface brightness profiles. The structural_parameters.csv file contains structural parameters such as the effective radius or absolute magnitude for every galaxy which has the relevant data. The columns in each table are described in the README_tables file.

The data provided here are part of a Galaxy Training Network tutorial that demonstrates mapping-by-sequencing analysis and represent a subsample of the data used in Sun & Schneeberger, 2015 (DOI:10.1007/978-1-4939-2444-8_19).

142667 Global import shipment records of Galaxy with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

The First Public Data Release (DR1) of Transient Host Exchange (THEx) Dataset

Paper describing the dataset: “Linking Extragalactic Transients and their Host Galaxy Properties: Transient Sample, Multi-Wavelength Host Identification, and Database Construction” (Qin et al. 2021)

The data release contains four compressed archives.

“BSON export” is a binary export of the “host_summary” collection, which is the “full version” of the dataset. The schema was presented in the Appendix section of the paper.

You need to set up a MongoDB server to use this version of the dataset. After setting up the server, you may import this BSON file into your local database as a collection using “mongorestore” command.

You may find some useful tutorials for setting up the server and importing BSON files into your local database at:

https://docs.mongodb.com/manual/installation/

https://www.mongodb.com/basics/bson

You may run common operations like query and aggregation once you import this BSON snapshot into your local database. An official tutorial can be found at:

https://docs.mongodb.com/manual/tutorial/query-documents/

There are other packages (e.g., pymongo for Python) and software to perform these database operations.

“JSON export” is a compressed archive of JSON files. Each file, named by the unique id and the preferred name of the event, contains complete host data of a single event. The data schema and contents are identical to the “BSON” version.

“NumPy export” contains a series of NumPy tables in “npy” format. There is a row-to-row correspondence across these files. Except for the “master table” (THEx-v8.0-release-assembled.npy), which contains all the columns, each file contains the host properties cross-matched in a single external catalog. The meta info and ancillary data are summarized in THEx-v8.0-release-assembled-index.npy.

There is also a THEx-v8.0-release-typerowmask.npy file, which has rows co-indexed with other files and columns named after each transient type. The “rowmask” file allows you to select a subset of events under a specific transient type.

Note that in this version, we only include cataloged properties of the confirmed hosts or primary candidates. If the confirmed host (or primary candidate) cross-matched multiple sources in a specific catalog, we only use the representative source for host properties. Properties of other cross-matched groups are not included. Finally, table THEx-v8.0-release-MWExt.npy contains the calculated foreground extinction (in magnitudes) at host positions. These extinction values have not been applied to magnitude columns in our dataset. You need to perform this correction by yourself if desired.

“FITS export” includes the same individual tables as in “NumPy export”. However, the FITS standard limits the number of columns in a table. Therefore, we do not include the “master table” in “FITS export.”

Finally, in BSON and JSON versions, cross-matched groups (under the “groups” key) are ordered by the default ranking function. Even if the first group in this list (namely, the confirmed host or primary host candidate) is a mismatched or misidentified one, we keep it in its original position. The result of visual inspection, including our manual reassignments, has been summarized under the “vis_insp” key.

For NumPy and FITS versions, if we have manually reassigned the host of an event, the data presented in these tables are also updated accordingly. You may use the “case_code” column in the “index” file to find the result of visual inspection and manual reassignment, where the flags for this “case_code” column are summarized in case-code.txt. Generally, codes “A1” and “F1” are known and new hosts that passed our visual inspection, while codes “B1” and “G1” are mismatched known hosts and possibly misidentified new hosts that have been manually reassigned.

This table contains an all-sky catalog of 869 nearby galaxies having individual distance estimates within 11 Mpc or corrected radial velocities relative to the Local Group centroid V_LG < 600 km s^-1. The catalog is a renewed and expanded version of the previous Catalog of Neighboring Galaxies by Karachentsev et al. (2004, AJ, 127, 2031). It collects data on the following galaxy observables: angular diameters, apparent magnitudes in the far-UV, B, and K_s bands, H-alpha and H I fluxes, morphological types, H I-line widths, radial velocities, and distance estimates. In this Local Volume (LV) sample, 108 dwarf galaxies still remain without measured radial velocities. The catalog also lists calculated global galaxy parameters: the linear Holmberg diameters, absolute B magnitudes, surface brightnesses, H I masses, stellar masses estimated via K-band luminosity, H I rotational velocities corrected for galaxy inclination, indicative masses within the Holmberg radius, and three kinds of "tidal index" which quantify the local density environment. In the reference paper, the authors briefly discuss the Hubble flow within the LV and different scaling relations that characterize galaxy structure and global star formation in them. They also trace the behavior of the mean stellar mass density, H I-mass density, and star formation rate density within the volume considered. This table was created by the HEASARC in June 2013 based on electronic versions of Tables 1 and 2 from the reference paper which were obtained form the AJ web site. This is a service provided by NASA HEASARC .

The MCG database contains the "Morphological Catalogue of Galaxies," a compilation of information for approximately 34,000 galaxies found and examined on the Palomar Observatory Sky Survey (POSS). Individual identifiers are assigned for about 29,000 galaxies and information on the remaining 5,000 is present in the extensive notes of the published catalogs (Vorontsov-Velyaminov et al. 1962-1968). The catalog is structured according to the POSS zones and is numbered from +15 (corresponding to +90 deg) to +01 (+06 deg zone) and +00 (equatorial zone) to -05 (-30 deg zone); the fields are numbered with increasing right ascension. The original goal of the compilation was to be complete for galaxies brighter than magnitude 15.1, but the final catalog lists many objects considerably fainter. Information given in the original printed volumes includes: cross- identifications to the NGC (Dreyer 1888) and IC (Dreyer 1895, 1908) catalogs, equatorial coordinates for 1950.0, magnitude, estimated sizes and intensities of the bright inner region and the entire object, estimated inclination, and coded description (by symbols) of the appearance of the galaxy. Each field is then followed by notes on individual objects. All of the above data except the coded description are included in the machine version, except that special coding (e.g. for uncertainty or source designation) is not present (other than for the NGC/IC cross identifications [added at the Astronomical Data Center for this machine version]). Although the notes are not computerized, the presence of a note in the original is flagged in the machine version Detailed descriptions of modifications, corrections and the record format are provided for the machine-readable version of the "Morphological Catalogue of Galaxies" (Vorontsov-Velyaminov et al. 1962-68); see the Additional Information section below. In addition to hundreds of individual corrections, a detailed comparison of the machine-readable with the published catalog resulted in the addition of 116 missing objects, the deletion of 10 duplicate records, and a format modification to increase storage efficiency. This is a service provided by NASA HEASARC .

This dataset comprises lens model-fits to 59 strong lenses from the SLACS and BELLS-GALLERY samples, contained in the paper "Automated galaxy-galaxy strong lens modelling: no lens left behind" by Etherington et al.. For every model-fit of every strong lens this dataset includes: - A model.info file with the priors and values of every parameter. - A model.results file with the parameter estimates and errors. - An image folder containing .png files showing different aspects of the overall fit. Due to file size limitations, we do not include the full dynesty nested sampling chains and do not include fits to the simulated mock data discussed in the paper. Reproducing figures in the paper also requires interfacing with the PyAutoLens database feature. Due to changes in version number and backwards compatibility issues with results, we have opted not to include these scripts in this release. We'll aim to do better in the future! Please feel free to contact me (james.w.nightingale@durham.ac.uk) for any missing data or help with reproducing visuals.

Galaxy groups are the most common bound galaxy systems, and unlike clusters, themetal enrichment of their environments can be heavily dominated by galacticwinds. The potential well of groups is too shallow to hold protogalactic winds,so that intragroup medium (IGM) should be mostly enriched by secondary SNIawinds. This enrichment history should be evident in the relative metal abundanceratios. We propose to measure the enrichment properties in 2 typical galaxygroups determine the IGM contamination from SNIa & II and study in detail SNIawind energetics, testing whether SN II winds escaped completely from the HCGsor if they were trapped,by massive DM halos. truncated!, Please see actual data for full text [truncated!, Please see actual data for full text]

This table contains the BAX X-Ray Galaxy Clusters and Groups Catalog. BAX (`Base de Donnees Amas de Galaxies X': see http://bax.ast.obs-mip.fr/ for more details) is a multi-wavelength database dedicated to X-ray clusters and groups of galaxies which allows detailed information retrieval. BAX is designed to support astronomical research by providing access to published measurements of the main physical quantities and to the related bibliographic references: basic data stored in the database are cluster/group identifiers, equatorial coordinates, redshift, flux, X-ray luminosity (in the ROSAT band) and temperature, and (in the online version at http://bax.ast.obs-mip.fr/) links to additional linked parameters (in X-rays, such as spatial profile parameters, as well as SZ parameters of the hot gas, lensing measurements, and data at other wavelengths, such as the optical and radio bands). The clusters and groups in the online BAX database can be queried by the basic parameters as well as the linked parameters or combinations of these. The authors expect BAX to become an important tool for the astronomical community. BAX will optimize various aspects of the scientific analysis of X-ray clusters and groups of galaxies, from proposal planning to data collection, interpretation and publication, from both ground based facilities like MEGACAM (CFHT), VIRMOS (VLT) and from space missions like XMM-Newton, Chandra and Planck. This table was created by the HEASARC in October 2004 based on CDS table B/bax/bax.dat. This is a service provided by NASA HEASARC .

We propose a first detection experiment with XMMNewton on the socalled GreenPea galaxies (GPs) in Xrays. These galaxies were noted first by volunteers inthe Galaxy Zoo project because of their peculiar green color and small size,unresolved in SDSS imaging. GPs present one of the largest and most homogeneoussamples of lowmass starbursts at redshift z smaller 1. The GPs propertiessuggest that they are snapshots of an extreme and short phase of galaxyevolution. GPs hence provide a local laboratory with which to study the extremestar formation processes that are known to occur in highz galaxies. ProposedXray detections and if possible spectral analysis will allow us to putfurther constraints on this new and still enigmatic class of extremely starforming galaxies. truncated!, Please see actual data for full text [truncated!, Please see actual data for full text]

AGN feedback is a key ingredient in modern galaxy formation models, invoked tosuppress star formation (SF) activity in earlytype galaxies at recent epochs.However, the physics of this process is not well understood and poorlyconstrained by observations. Compelling observational evidence for its mereexistence has been missing so far. We identified a sample of earlytype galaxiesin SDSS that define an AGN feedback sequence transitioning from SF via AGN toquiescence. We propose observations on a subsample of 18 targets characterizingthe AGN feedback process. These data will provide critical information about thepresence and significance of black hole activity in this sample, providingdeeper insight into the physics of AGN feedback in earlytype galaxies. truncated!, Please see actual data for full text [truncated!, Please see actual data for full text]

We present the first data release of Radio Galaxy Zoo, an online citizen science project that enlists the help of citizen scientists to cross-match extended radio sources from the Faint Images of the Radio Sky at Twenty Centimeters (FIRST) and the Australia Telescope Large Area Survey (ATLAS) surveys, often with complex structure, to host galaxies in $3.6\,\mu$m infrared images from the {\em{Wide-field Infrared Survey Explorer}} (WISE) and the {\em{Spitzer Space Telescope}}. This first data release consists of 100,185 classifications for 98,559 radio sources from the FIRST survey and 582 radio sources from the ATLAS survey. As such, there are radio sources for which more than one classification is listed. We include two tables for each of the FIRST and ATLAS surveys: 1) the identification of all components making up each radio source; and 2) the cross-matched host galaxies. These classifications have an average reliability of 0.83 based on the weighted consensus levels of our citizen scientists. Please refer to the RGZ Data Release 1 paper by Wong et al 2024 for more details.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

This repository contains the data released in the paper "Galaxy Zoo DECaLS: Detailed Visual Morphology Measurements from Volunteers and Deep Learning for 314,000 Galaxies" (DOI to follow on publication).

We release detailed morphology catalogues, both volunteer and automated, for Galaxy Zoo DECaLS.

• gz_decals_volunteers_1_and_2 contains volunteer classifications for galaxies classified during the GZD-1 and GZD-2 campaigns.

• gz_decals_volunteers_5 similarly contains classifications from the GZD-5 campaign. Note that GZD-5 used a modified schema designed to better detect mergers and weak bars, and includes many galaxies with only approx. five volunteer responses.

• gz_decals_auto_posteriors contains the predicted posteriors for volunteer responses to all galaxies used in any campaign. The full posteriors are recorded as Dirichlet distribution concentrations. gz_decals_auto_posteriors also summarises these posteriors as the automated equivalent of previous Galaxy Zoo data releases; the expected vote fractions (mean posteriors). Note that not all posteriors/vote fractions are relevant for every galaxy; we suggest assessing relevance using the estimated fraction of volunteers that would have been asked each question.

We include a schema document, schema.md, to define the column names in each catalogue.

We also release the galaxy images shown to volunteers on www.galaxyzoo.org during GZD-5. The images on which the automated classifier was trained may be derived from these volunteer-facing images. These images are split into four zip files, each of which contains images named by iauname inside a subfolder named by the first four characters in their iauname. Not all images were labelled during GZD-5 - refer to the catalog for training labels. We are working with the Zenodo team to add these large files to this repository - meanwhile, you can download them from The University of Manchester here.

The .csv and .parquet files contain identical data. Parquet is a fast column-oriented binary format which can be read with pd.read_parquet(loc, columns=[some columns]).

You may also be interested in the github repository which contains code to reproduce the model and to fine-tune it for new tasks (including pretrained weights).

We will release updates if needed via Zenodo versioning. We recommend using the latest version of this repository. You can check the version you are currently viewing on the right-hand sidebar.

Please cite the paper (DOI to follow on publication) when using the data in this repository.

History

v0.0.1 (submission) provides the catalog files.

v0.0.2 (first revision) renames the catalog files, adds flags for poorly sized galaxies, and includes the galaxy images via the University of Manchester

This data is used for the Seurat version of the batch correction and integration tutorial on the Galaxy Training Network.

The input data was provided by Seurat in the 'Integrative Analysis in Seurat v5' tutorial. The input dataset provided here has been filtered to include only cells for which nFeature_RNA > 1000. The other datasets were produced on Galaxy.

The original dataset was published as: Ding, J., Adiconis, X., Simmons, S.K. et al. Systematic comparison of single-cell and single-nucleus RNA-sequencing methods. Nat Biotechnol 38, 737–746 (2020). https://doi.org/10.1038/s41587-020-0465-8.

The data here is a subset of the data published in 10.5281/zenodo.3732359 to be used in GTN 'Upload data to ENA' tutorial.

Human traces have been removed following https://training.galaxyproject.org/training-material/topics/sequence-analysis/tutorials/human-reads-removal/tutorial.html

Deep Learning models used in the paper “Deep Learning Assessment of galaxy morphology in S-PLUS DataRelease 1 “.

We share the .h5 Deep Learning models trained using S-PLUS.:

EfficientNetB2 (3 Bands – pretrained), EfficientNetB2 (5 Bands), EfficientNetB2 (8 Bands), EfficientNetB2 (12 Bands)

We release the catalog of our three samples: The Train and Validation, Ambiguous and Blind samples.

The Principal Galaxy Catalog, 2003 Version (PGC2003) is a new catalog of principal galaxies. It constitutes the framework of the HYPERLEDA database that supersedes the LEDA one, with more data and more capabilities. The catalog is still restricted to confirmed galaxies, i.e. about one million galaxies, brighter than a B-magnitude of ~18. In order to provide the best possible identification for each galaxy, the authors give accurate coordinates (typical accuracy of better than 2 arcseconds), diameters, axis ratios and position angles. Diameters and axis ratios have been homogenized to the RC2 system at the limiting surface brightness of 25 B-mag/arcsec², using a new method (EPIDEMIC). In order to provide the best designation for each galaxy, the authors have collected names from 50 catalogs. The compatibility of the spelling has been tested against NED and SIMBAD, and, as far as possible a spelling is used that is compatible with both. For some cases, where no consensus exists between NED, SIMBAD and LEDA, the authors have proposed some changes that could make the spelling of names fully compatible. The full catalog is distributed through the CDS and can be extracted from HYPERLEDA, http://leda.univ-lyon1.fr/. This table was created by the HEASARC in July 2004 based on the CDS catalog VII/237 file pgc.dat.gz. This is a service provided by NASA HEASARC .