photometric-redshifts

There are 6 repositories under photometric-redshifts topic.

  • pfunzowalter/REGRESSION

    The photometric redshifts estimation is currently the most powerful and efficient way to estimate the distances to the extragalactic sources. The exponential data avalanche continues and this will require low cost, fast and efficient data-driven methods to analyse and make predictions from the data. In this study, we present the supervised machine learning algorithms that were used to attain the photometric redshifts of the galaxies and quasars found in Sloan Digital Sky Survey data release 16 (SDSS DR16). We adopt the K-Nearest Neighbour (KNN) and Random Forest (RF) regressors to estimate the photometric redshifts of 285685 galaxies and 124688 quasars by considering their photometric measurements.

    Language:Jupyter Notebook210

  • pedro-acunha/SHEEP

    Machine Learning pipeline to classify astronomical sources into galaxies, quasars and stars using photometric data.

    Language:Python1100

  • TasosTheodoropoulos/Photoz_SDSS

    Machine Learning models for the extraction of Photometric Redshifts from 64*64 ugriz images from the SDSS survey.

    Language:Jupyter Notebook110

  • Vikhyat2603/SpyderZ

    an efficient support vector machine library for photometric redshift estimation and redshift probability information

    Language:Jupyter Notebook1100

  • majianthu/quasar

    Code for the paper "Photometric Redshifts with Copula Entropy"

    Language:R0100

  • eduardrusu/zMstarPDF

    Gravitational lens environment modeling using Photo-z and Mstar PDFs with various systematics

    Language:Python