This tutorial intends to be an easy introduction to Multi-band (MB) Aperture photometry and Bayesian Photometric Redshifts derived from Modern Astronomical Redshift Surveys.

In particular, the tutorial aims at teaching how to perform different types of photometry (Isophotal, Circular, Flexible) on astronomical images, how to extract astrometric and morphological information from stars and galaxies, how to generate MB photometric catalogues,  how to create and trim (png) color images from (FITS) images, how to create mosaic (FITS) images, how to visualize images and catalogues with VO tools, how to derive Bayesian Photometric Redshifts and how to visualize and quantify its precision.

—

Here you can find a more detailed description about this tutorial:

 

  • Day-1: Multi-band Aperture-Matched Photometry. 

colors

During the 1st day, we will learn all the essential aspects from the SExtractor software  to perform Aperture Photometry on our Astronomical Images . In particular:

  • How to configure the SExtractor software:  *.sex + *.param
  • How to execute SExtractor to perform single and dual-mode photometry.
  • How to generate multi-band photometric catalogues.
  • How to derive multiple aperture photometry to extract growth-light curves.
  • How to create added-value images: segmentation, aperture and background-maps.
  • How to create (png or jpeg) color-images from FITS images with Trilogy.
  • How to trim them out to generate color stamps for our preferred targets.
  • How to create mosaic-like (FITS) images to visualize galaxies in several bands.
  • How to visualize our astronomical images using the Aladin software.
  • How to read and display our catalogues on our (FITS) images using Topcat.

 

Now you can download the photometric_codes

and the presentation.

 

 

  • Day-2: Bayesian Photometric Redshifts. 

combo2

During the 2nd day, after a previous revision on the different methodologies existing nowadays, we will learn how to derive Bayesian Photometric Redshifts. In particular:

  • How to configure the BPZ (v1.0) code: Libraries, Priors & Grids.
  • How to optimally execute BPZ depending on the scientific case.
  • How to adapt photometric catalogues to the BPZ standards.
  • How to quantify the precision reached by of our photo-z estimates.
  • How to visualize our SED fittings and the Probability Density Functions (PDF).
  • How to derive IFU-like analysis on spatially resolved galaxies.
  • How to identify systematics between data and models.

 

Now you can download the photoz_codes and the presentation.

 

Requested Software to get started (I)

This tutorial will make use of all the following softwares.

  • SExtractor: it detects and extracts sources from astronomical images and performs aperture photometry of several types. It creates science-value products such as catalogues and images.

SExtractor

  • BPZv1.0: based on an input multi-band photometric catalogue, it computes photometric redshift estimates for galaxies. Apart from the most like redshift and template solution, it also provides (if requested) entire Density Probability Distribution Functions (PDF) and expected Model Magnitudes for galaxies.

bpz

  • Aladin: it allows to display astronomical images in an interactive fashion, to do easy queries to the Virtual Observatory (VO) searching for images and catalogues.

Aladin

  • Topcat: it allows to upload data, to make sample selections, to cross-match catalogues and make interactive plots. As a VO tool, it manages to share data with Aladin displaying sample of sources on top of astronomical images.

topcat

  • Trilogy: this python-based software easily creates (png) color images from astronomical (FITS) images. Check Dan Coe’s website for more information!

trilogy

  • Scripts: several python-based codes will used to (automatically) execute the scripts. (they are coming soon!)

pycharm

 

Requested Software to get started (II)

python-logo

In order to execute our (python-based) scripts, it will be necessary to have previous installed the following python packages and Libraries:

  1. Python (in case you don’t have it installed already!)
  2. Numpy
  3. Pandas
  4. iPython
  5. MatplotLib
  6. Astropy
  7. Scipy
  8. PIL

 

Scientific Data for this Tutorial

alhambra_minlogo

In this tutorial we will make use of real scientific data (images and catalogues) from the Advance Large Homogeneous Area Medium Band Redshift Astronomical (ALHAMBRA) survey. With its 20 Optical +3 NIR bands, its photometric-depth and spatial-resolution, this survey represents an idea dataset to face all standard problems in Multi-band Photometry and Photometric Redshifts.

alhambra

 

clash

In addition, we will use data from the Cluster Lensing And Supernovae with Hubble (CLASH). This HST programme provides an unique multi-wavelength view of galaxies in clusters, combining data from both the ACS and WFC3 cameras, spanning from the Ultraviolet to the Near-Infrared passing through the Optical. This amazing wave-range is covered by 16 photometric broad passbands. Based on this dataset, we will be able to study how the stellar populations of a resolved face-on spiral galaxy changed spatially.

clash_spiral

***

The Data (images and catalogues) can be accessed from here:

  • Tutorial: this extended dataset will be used during the tutorial. Therefore, it has to be downloaded and stored. Be aware that it is more than 1GB! Download.

 

 

Here a few pictures from our school

 

FullSizeRender1

FullSizeRender2