Basics of Machine Learning with HSC-SSP PDR

1. Downloading the whole HSC-SSP data (the most flexible if you don't mind the storage volume)
   You can search and download all HSC-SSP data through the Image Query Form.
   Or, you may want to access the data directories on the HSC-SSP page directly.

2. Image cutout tool (the most recommended if the sample size is less than ~1M)
   Image cutout tool is a convenient way to cutout the target images without downloading all data.
   More convenient command line tools (found at the bottom of the page) are also provided by the pipeline team.

3. Interactive cutout on the hscMap Sky Explore (the easiest but not recommended especially for low-z science)
   You can cutout the fits image interactively, as you wish, on the hscMap.
   Please select "Tool -> Rectangle Selection" on the menu bar, and select the region.
   Then you can cutout the available image by clicking the button on the corner.

4. Use of other tools
   There are some ways to download images directly (not fits files).
   You can find these tools at the bottom of the HSC-SSP page.

Note

• In the image cutout tool, you can define the output name by adding a column of "name" (see manual for details).

• If your targets are bright extended sources at low redshift, you should use the data before "the aggressive sky subtraction".
  You can select such data by changing the file type from "coadd" (default) to "coadd/bg". See the example below.

First of all, you may need to install the following libraries

import numpy as np
from PIL import Image
from astropy.io import fits
from astropy.nddata import Cutout2D
from scipy.ndimage import gaussian_filter

1. Import fits data
   Here I assume that you use the python library, Astropy. The code would be like this;
   hdu = fits.open(fitsname)[1] # note that HSC image is multi-extension fits
   img = hdu.data

2. PSF matching (if necessary)
   Scipy gaussian filter allows you to execute gaussian smoothing for the data
   img = gaussian_filter(img, sigma=sgm)

3. Cutout images (if necessary)
   If you need to further cutout the data, Cutout2D in Astropy would be useful
   def center(data):
   width, height = data.shape
   return (int(width / 2), int(height / 2))
   cut = Cutout2D(img.data, position=center(img.data), size=(size, size))

4. Flux normalization
   Refer to the Color Postage tool developed by the HSC-SSP pipeline team.
   This tool enables you to create SDSS or hscMap -type images in the RGB or grayscale.
   See Lupton et al. 2004 for more details on color normalization.

Note

• About image size: If your targets are identified by SDSS, a Petrosian R90 from SDSS would be useful.
  In this case, the image size of 4-8 times PetroR90 will be comfortable in most cases.

• About PSF matching: If you plan to conduct a PCA-type analysis, PSF matching should be necessary because the PSF distribution is also one of the principal components.