Welcome to Weak Lensing Tutorial’s Documentation!

• Day 1 - Introduction
  • Working on SciServer
    • Your container is ready if it looks something like this -
    • Congratulations!
  • Reading and Plotting the Data
• Day 1 Solutions
• Day 2 - Finding neighbours
  • Reading the lens/source catalog
  • Visualising the coordinate space of input data
  • On-sky distance between two points on the surface of a unit sphere
  • Brute force search for neighbour objects within a specified distance
    • Testing the above brute force search functions
    • A visual sanity check for execution of find_neighbour_points function
  • Using k-d tree algorithm to query the neighbour points within a given distance
    • We’ll use the spatial.cKDTree package from scipy.
    • A sanity check
    • Compare the output of our brute force algorithm with cKDTree algorithm on few Demo objects
  • Compare the computer time required by our brute force algorithm vs the k-d tree algorithm
  • Congratulations! Now you have learnt to -
• Day 2 Solutions
  • Reading the lens/source catalog
  • Visualising the coordinate space of input data
  • On-sky distance between two points on the surface of a unit sphere
  • Brute force search for neighbour objects within a specified distance
    • Testing the above brute force search functions
    • A visual sanity check for execution of find_neighbour_points function
  • Using k-d tree algorithm to query the neighbour points within a given distance
    • A sanity check
    • Compare the output of our brute force algorithm with cKDTree algorithm on dew Demo objects
  • Compare the computer time required by our brute force algorithm vs the k-d tree algorithm
  • Congratulations! Now you have learnt to -
• Day 3 - Get the Weak Lensing Signals
  • Required Steps
  • Reading data from the catalog and appying the selection cuts.
  • Computing the tangential shear \(e_{\rm t}\) and inverse critical density \(\Sigma^{-1}_{\rm crit}\)
  • \(\Delta \Sigma\) measurements using cKDTree and writing the output to a file.
    • Decription of algorithm used in the code below (run_pipe) to get the signal \(\Delta \Sigma\).
  • Plotting the weak lensing signal
  • The bias corrected measurements
• Day 3 Solutions
• Day 4 - Model the weak lensing signal
• Day 4 Solutions

Indices and tables

• Index

• Module Index

• Search Page