READ ME File For 'PixelCNN output: log-likelihood ratios '

Dataset DOI: 10.5258/SOTON/D2049

ReadMe Author: Lorenzo Zanisi

This dataset supports the thesis entitled: Galaxy formation through the lens of galaxystructure with semi-empirical models anddeep learning
AWARDED BY: Univeristy of Southampton
DATE OF AWARD:2021

DESCRIPTION OF THE DATA
[This should include a detailed description of the data, how it was collected/created, any specialist software needed to view the data]

This is the dataset underlying the paper "A deep learning approach to test the small-scale galaxy morphology and its relationship with star formation activity in hydrodynamical simulations" by L. Zanisi et al., available at https://ui.adsabs.harvard.edu/abs/2021MNRAS.501.4359Z/abstract. The dataset was generated following the procedure outlined in Section 3 of that paper. The format of each of the filed in the dataset is .csv. This is suitable to be read in Excel as well as in Python Pandas.


This dataset contains: the log-likelihood ratio values for galaxies in: the Sloan Digital Sky Survey (SDSS), the Illustris simulation, the Illustris TNG 100 simulation, the Illustris TNG 50 simulations and the "Sersic blobs" simulations (these are discussed at length in the paper). The columns are the value of the likelihood ratio, the likelihood of the individual pixelCNN models (trained on SDSS and the Sersic blobs) and the ID identifying each objects. For SDSS, two files are provided: one includes the whole dataset ('SDSS_all.csv') and one only the test data ('SDSS_test.csv'). The SDSS catalogue used in this paper is coupled to the Meert et al. 2015 catalogs (the SDSSsee http://alan-meert-website-aws.s3-website-us-east-1.amazonaws.com/fit_catalog/index.html for the Meert et al. catalog) based on the SDSS Data Release 7. In the two SDSS files the SDSS Data Release 7 ID is reported ("objid") as well as the ID for the Meert et al. catalogue ("galcount"), for convenience .




Date of data collection: 2020

Information about geographic location of data collection: NA

Licence:
Creative Commons Attribution CC-BY
Related projects/Funders:
NA

Related publication:
"A deep learning approach to test the small-scale galaxy morphology and its relationship with star formation activity in hydrodynamical simulations" by L. Zanisi et al., available at https://ui.adsabs.harvard.edu/abs/2021MNRAS.501.4359Z/abstract


Date that the file was created: 12/2020