Context

The Galaxy Zoo team regularly receives requests for subject images for various versions of Galaxy Zoo, in order to facilitate other investigations, e.g. machine learning projects. This repository is an updated attempt to provide those in a way that is useful to the wider community.

Content

There are 243,434 images in total. This is off by about 0.08% from the total count in the tables - it's not clear what the cause of the discrepancy is

The images are available in the file images_gz2.

The most recent and reliable source for morphology measurements is "GZ2 - Table 1 - Normal-depth sample with new debiasing method – CSV" (from Hart et al. 2016), which is available at data.galaxyzoo.org To cross-reference the images with Table 1, this sample includes another CSV table (gz2_filename_mapping.csv) which contains three columns and 355,990 rows. The columns are:

• objid: the Data Release 7 (DR7) object ID for each galaxy. This should match the first column in Table 1.
• sample: string indicating the subsampling of the galaxy.
• asset_id: an integer that corresponds to the filename of the image in the zipped file linked above.

Acknowledgements

They are the "original" sample of subject images in Galaxy Zoo 2 (Willett et al. 2013, MNRAS, 435, 2835, DOI: 10.1093/mnras/stt1458) as identified in Table 1 of Willett et al. and also in Hart et al. (2016, MNRAS, 461, 3663, DOI: 10.1093/mnras/stw1588).

Inspiration

I want to know if it's possible to cluster the images in galaxy shape types of Hubble - de Vaucouleurs Galaxy Morphology Diagram:

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F6067505%2F8ac7df09aa0f85a1a07ac9dc0a81b57f%2FHubble_-_de_Vaucouleurs_Galaxy_Morphology_Diagram.png?generation=1611680439647479&alt=media" alt="">

• Ellipticals: with shapes from spherical to cilindrical almost homogeneous density.
• Spirals: with two or more arms (like the classical view of Milky Way Galaxy) and a dense core.
• Irregulars: with non defined shape, heterogeneous density.

If this three are not enough and you want to improve your notebook is possible to add:

• Lenticulars: disk shaped galaxies with a dense core.
• Barred Spirals: Type of spiral with straight arms near to the core and bended far of it.
• Usual Spirals: Type of spiral with bended arms from the core to the end.
• Intermediate Spirals: Type of spiral with non-defined arms.
• Dwarf Galaxy: Tiny irregular heterogeneous galaxy.

Didn't add this to the first clusters due to depending on the angle of the galaxy some lenticulars may seem Ellipticals or Spirals, is hard to see always the arms of spiral galaxies and is hard to determine if a galaxy is tiny or big with just a photography and nothing to compare.

Sonification for Exploratory Data Analysis #### Chapter 8: Sonification Models In Chapter 8 of the thesis, 6 sonification models are presented to give some examples for the framework of Model-Based Sonification, developed in Chapter 7. Sonification models determine the rendering of the sonification and possible interactions. The "model in mind" helps the user to interprete the sound with respect to the data. ##### 8.1 Data Sonograms Data Sonograms use spherical expanding shock waves to excite linear oscillators which are represented by point masses in model space. * Table 8.2, page 87: Sound examples for Data Sonograms File: Iris dataset: started in plot (a) at S0 (b) at S1 (c) at S2
10d noisy circle dataset: started in plot (c) at S0 (mean) (d) at S1 (edge)
10d Gaussian: plot (d) started at S0
3 clusters: Example 1
3 clusters: invisible columns used as output variables: Example 2 Description: Data Sonogram Sound examples for synthetic datasets and the Iris dataset Duration: about 5 s ##### 8.2 Particle Trajectory Sonification Model This sonification model explores features of a data distribution by computing the trajectories of test particles which are injected into model space and move according to Newton's laws of motion in a potential given by the dataset. * Sound example: page 93, PTSM-Ex-1 Audification of 1 particle in the potential of phi(x). * Sound example: page 93, PTSM-Ex-2 Audification of a sequence of 15 particles in the potential of a dataset with 2 clusters. * Sound example: page 94, PTSM-Ex-3 Audification of 25 particles simultaneous in a potential of a dataset with 2 clusters. * Sound example: page 94, PTSM-Ex-4 Audification of 25 particles simultaneous in a potential of a dataset with 1 cluster. * Sound example: page 95, PTSM-Ex-5 sigma-step sequence for a mixture of three Gaussian clusters * Sound example: page 95, PTSM-Ex-6 sigma-step sequence for a Gaussian cluster * Sound example: page 96, PTSM-Iris-1 Sonification for the Iris Dataset with 20 particles per step. * Sound example: page 96, PTSM-Iris-2 Sonification for the Iris Dataset with 3 particles per step. * Sound example: page 96, PTSM-Tetra-1 Sonification for a 4d tetrahedron clusters dataset. ##### 8.3 Markov chain Monte Carlo Sonification The McMC Sonification Model defines a exploratory process in the domain of a given density p such that the acoustic representation summarizes features of p, particularly concerning the modes of p by sound. * Sound Example: page 105, MCMC-Ex-1 McMC Sonification, stabilization of amplitudes. * Sound Example: page 106, MCMC-Ex-2 Trajectory Audification for 100 McMC steps in 3 cluster dataset * McMC Sonification for Cluster Analysis, dataset with three clusters, page 107 * Stream 1 MCMC-Ex-3.1 * Stream 2 MCMC-Ex-3.2 * Stream 3 MCMC-Ex-3.3 * Mix MCMC-Ex-3.4 * McMC Sonification for Cluster Analysis, dataset with three clusters, T =0.002s, page 107 * Stream 1 MCMC-Ex-4.1 (stream 1) * Stream 2 MCMC-Ex-4.2 (stream 2) * Stream 3 MCMC-Ex-4.3 (stream 3) * Mix MCMC-Ex-4.4 * McMC Sonification for Cluster Analysis, density with 6 modes, T=0.008s, page 107 * Stream 1 MCMC-Ex-5.1 (stream 1) * Stream 2 MCMC-Ex-5.2 (stream 2) * Stream 3 MCMC-Ex-5.3 (stream 3) * Mix MCMC-Ex-5.4 * McMC Sonification for the Iris dataset, page 108 * MCMC-Ex-6.1 * MCMC-Ex-6.2 * MCMC-Ex-6.3 * MCMC-Ex-6.4 * MCMC-Ex-6.5 * MCMC-Ex-6.6 * MCMC-Ex-6.7 * MCMC-Ex-6.8 ##### 8.4 Principal Curve Sonification Principal Curve Sonification represents data by synthesizing the soundscape while a virtual listener moves along the principal curve of the dataset through the model space. * Noisy Spiral dataset, PCS-Ex-1.1 , page 113 * Noisy Spiral dataset with variance modulation PCS-Ex-1.2 , page 114 * 9d tetrahedron cluster dataset (10 clusters) PCS-Ex-2 , page 114 * Iris dataset, class label used as pitch of auditory grains PCS-Ex-3 , page 114 ##### 8.5 Data Crystallization Sonification Model * Table 8.6, page 122: Sound examples for Crystallization Sonification for 5d Gaussian distribution File: DCS started at center, in tail, from far outside Description: DCS for dataset sampled from N{0, I_5} excited at different locations Duration: 1.4 s * Mixture of 2 Gaussians, page 122 * DCS started at point A DCS-Ex1A * DCS started at point B DCS-Ex1B * Table 8.7, page 124: Sound examples for DCS on variation of the harmonics factor File: h_omega = 1, 2, 3, 4, 5, 6 Description: DCS for a mixture of two Gaussians with varying harmonics factor Duration: 1.4 s * Table 8.8, page 124: Sound examples for DCS on variation of the energy decay time File: tau_(1/2) = 0.001, 0.005, 0.01, 0.05, 0.1, 0.2 Description: DCS for a mixture of two Gaussians varying the energy decay time tau_(1/2) Duration: 1.4 s * Table 8.9, page 125: Sound examples for DCS on variation of the sonification time File: T = 0.2, 0.5, 1, 2, 4, 8 Description: DCS for a mixture of two Gaussians on varying the duration T Duration: 0.2s -- 8s * Table 8.10, page 125: Sound examples for DCS on variation of model space dimension File: selected columns of the dataset: (x0) (x0,x1) (x0,...,x2) (x0,...,x3) (x0,...,x4) (x0,...,x5) Description: DCS for a mixture of two Gaussians varying the dimension Duration: 1.4 s * Table 8.11, page 126: Sound examples for DCS for different excitation locations File: starting point: C0, C1, C2 Description: DCS for a mixture of three Gaussians in 10d space with different rank(S) = {2,4,8} Duration: 1.9 s * Table 8.12, page 126: Sound examples for DCS for the mixture of a 2d distribution and a 5d cluster File: condensation nucleus in (x0,x1)-plane at: (-6,0)=C1, (-3,0)=C2, ( 0,0)=C0 Description: DCS for a mixture of a uniform 2d and a 5d Gaussian Duration: 2.16 s * Table 8.13, page 127: Sound examples for DCS for the cancer dataset File: condensation nucleus in (x0,x1)-plane at: benign 1, benign 2
malignant 1, malignant 2 Description: DCS for a mixture of a uniform 2d and a 5d Gaussian Duration: 2.16 s ##### 8.6 Growing Neural Gas Sonification * Table 8.14, page 133: Sound examples for GNGS Probing File: Cluster C0 (2d): a, b, c
Cluster C1 (4d): a, b, c
Cluster C2 (8d): a, b, c Description: GNGS for a mixture of 3 Gaussians in 10d space Duration: 1 s * Table 8.15, page 134: Sound examples for GNGS for the noisy spiral dataset File: (a) GNG with 3 neurons 1, 2
(b) GNG with 20 neurons end, middle, inner end
(c) GNG with 45 neurons outer end, middle, close to inner end, at inner end
(d) GNG with 150 neurons outer end, in the middle, inner end
(e) GNG with 20 neurons outer end, in the middle, inner end
(f) GNG with 45 neurons outer end, in the middle, inner end Description: GNG probing sonification for 2d noisy spiral dataset Duration: 1 s * Table 8.16, page 136: Sound examples for GNG Process Monitoring Sonification for different data distributions File: Noisy spiral with 1 rotation: sound
Noisy spiral with 2 rotations: sound
Gaussian in 5d: sound
Mixture of 5d and 2d distributions: sound Description: GNG process sonification examples Duration: 5 s #### Chapter 9: Extensions #### In this chapter, two extensions for Parameter Mapping

Properties, Images and Spectrals of All Observable Spiral Galaxies

Filenames consist of galaxy names. Each file contains the data of that galaxy. All data were obtained from the closest point to the center point.

Context

Spiral galaxies form a class of galaxy originally described by Edwin Hubble in his 1936 work The Realm of the Nebulae and, as such, form part of the Hubble sequence. Most spiral galaxies consist of a flat, rotating disk containing stars, gas and dust, and a central concentration of stars known as the bulge. These are often surrounded by a much fainter halo of stars, many of which reside in globular clusters.

Spiral galaxies are named by their spiral structures that extend from the center into the galactic disc. The spiral arms are sites of ongoing star formation and are brighter than the surrounding disc because of the young, hot OB stars that inhabit them.

Roughly two-thirds of all spirals are observed to have an additional component in the form of a bar-like structure, extending from the central bulge, at the ends of which the spiral arms begin. The proportion of barred spirals relative to barless spirals has likely changed over the history of the universe, with only about 10% containing bars about 8 billion years ago, to roughly a quarter 2.5 billion years ago, until present, where over two-thirds of the galaxies in the visible universe (Hubble volume) have bars.

The Milky Way is a barred spiral, although the bar itself is difficult to observe from Earth's current position within the galactic disc. The most convincing evidence for the stars forming a bar in the galactic center comes from several recent surveys, including the Spitzer Space Telescope.

Together with irregular galaxies, spiral galaxies make up approximately 60% of galaxies in today's universe. They are mostly found in low-density regions and are rare in the centers of galaxy clusters.

https://upload.wikimedia.org/wikipedia/commons/thumb/c/c5/M101_hires_STScI-PRC2006-10a.jpg/285px-M101_hires_STScI-PRC2006-10a.jpg" alt="">

VizieR Online Data Catalog: Nuclear star clusters in late-type spiral galaxies(Boker T.+, 2004)

Assembly of a family of monomeric, dimeric, and polymeric W/Cu/S clusters from a precursor cluster [Et4N]Tp*W(μ3-S)3(CuBr)3 (1) and various N-donor ligands was reported. The treatment of 1 with pyridine (py) or aniline (ani) in the presence of NH4PF6 afforded a cationic cluster Tp*W(μ3-S)3Cu3(py)3(μ3-Br) (2) and a neutral cluster [{Tp*W(μ3-S)3(CuBr)3}(μ6-Br){Tp*W(μ3-S)3Cu3(ani)3}]·4ani·0.5Et2O (3·4ani·0.5Et2O). On the other hand, the treatment of 1 with excess 4,4′-bipyridine (4,4′-bipy) or 1,2-bis(4-pyridyl)ethylene (bpee) followed by the addition of NH4PF6 led to the formation of a polymeric cluster {Tp*W(μ3-S)3Cu3(4,4′-bipy)3(μ3-Br)·H2O}n (4) and a neutral cluster [{Tp*W(μ3-S)3Cu3Br2}2(bpee)]·0.5CH2Cl2 (5·0.5CH2Cl2). Meanwhile, analogous reactions of 1 with excess 1,2-bis(4-pyridyl)ethane (bpe) or 1,3-bis(4-pyridyl)propane (bpp) in DMF under the presence of NH4PF6 resulted in the formation of two polymeric clusters {{Tp*W(μ3-S)3Cu3(μ3-Br)}2(bpe)32·MeCN}n (6) and {[Tp*W(μ3-S)3Cu3Br(μ3-Br)(bpp)]·DMF}n (7). Compounds 1−7 were characterized by elemental analysis, IR spectra, UV−vis spectra, 1H NMR, electrospray ionization mass spectra, and X-ray crystallography. The anion of 1 has an incomplete cubanelike [Tp*W(μ3-S)3(CuBr)3] structure, while the cation of 2 has a cubanelike [Tp*W(μ3-S)3Cu3(μ3-Br)] structure. Compound 3 may be viewed as having a corner-shared double cubanelike structure that consists of one [Tp*W(μ3-S)3Cu3(ani)3]2+ dication and one [Tp*W(μ3-S)3(CuBr)3]− anion linked by a μ6-Br bridge. For 4, each [Tp*W(μ3-S)3Cu3(μ3-Br)] unit works as a pyramidal three-connecting node to connect its equivalent ones via three 4,4′-bipy bridges to yield a 2D (6,3) cationic network. Compound 5 has a dimeric structure in which two incomplete cubanelike [Tp*W(μ3-S)3Cu3Br2] cores are bridged with one bpee ligand. For 6, each dimeric [{Tp*W(μ3-S)3Cu3(μ3-Br)}2(bpe)2] unit is interconnected via a pair of bpe bridges to form a 1D zigzag cationic chain. Compound 7 has a 1D spiral chain in which each [Tp*W(μ3-S)3Cu3Br(μ3-Br)] core is interlinked by a couple of bpp bridges. The formation of 2−7 from the precursor cluster 1 through various N-donor ligands offers a new way to the design and assembly of the W/Cu/S clusters with interesting molecular and supramolecular arrays.

Young Massive Clusters keyOpen YMCkeyClose appear to be abundantly forming in merging galaxies comma but are not found in the Milky Way. They provide the opportunity to study the conditions necessary for the formation of massive comma compact stellar systems comma giving insight into conditions of the earliest epochs of galaxy formation comma when ancient Globular Clusters keyOpen GCkeyClose formed comma thus helping to constrain scenarios of galaxy formation and evolution. We propose STIS UV spectroscopy of three extremely young comma UVluminous clusters in the Local Group spiral galaxy M33 comma selected from our extensive survey with WFPC2 imaging. From multiband integrated photometry we inferred age upper limits of 10 MyrsdoublePoint UV spectra will provide precise ages comma thus masses comma for these objects from the earliest spectral types present comma revealed by the strong spectral signatures comma and from synthetic spectral modeling. We will be able to assess whether their mass overlap with GC masses comma and whether such systems can survive internal dynamical evolution. These young comma massive and compact objects provide a key link to the young cluster systems in mergers comma and may be the only such counterparts accessible to detailed studies.

VizieR Online Data Catalog: Gas kinematics of spiral galaxies(Kutdemir E.+, 2010)

pone.0294540.t012 - The great divide between employees: Clustering employee “well-being” during a pandemic