Tiling scheme

ANDOR Camera

The primary camera for GIT is the 4k ANDOR camera, with a 0.7 degree field of view (FoV). For follow-up observations with this camera, we use a pre-defined grid of tiles on the sky.

Since the FoV is circular, there is a trade-off between how much area of the sky we cover twice (or more) in the grid of tiles by overlapping, and how much area we miss out on due to the circular shape. These two quantities are controlled by the spacing between the centres of adjacent tiles in RA and Dec.

We created tile grids using several combinations of the spacings in RA and Dec to minimize the area covered twice and the area not covered at all. Using the results, we finally settled on an RA spacing of 0.59 degrees, and a Dec spacing of 0.63 degrees between adjacent tiles. An example of what the tiles look like on the sky is shown below.

Tiling scheme at two sky locations - at (RA, Dec) = (0, 0) and (180, 0). Each orange circle represents a 0.7 degree diameter FoV of the GROWTH-India Telescope. Darker regions indicate overlap, while white regions indicate the area missed by the tiling scheme. The dots are the centres of each tile. The rows tiles start aligned at RA=0°, but naturally get misaligned at 180°. This also leads to changes in the overlap in successive rows at various values of RA.

The full list of tile centres can be generated with this python code. The tile ID is the index 'l' calculated below.

import numpy as np

import pandas as pd

dec_r = 0.63

ra_r = 0.59

dec_range = np.arange(-33, 90, dec_r)

tile_centers = []

dec_range = np.append(dec_range, 89.99)

l = 0

for i, dec in (enumerate(dec_range)):

for j, ra in enumerate(np.linspace(0, 360, math.ceil(360*np.cos(dec*(np.pi/180))/ra_r))):

tile_centers.append(tuple((l, ra, dec)))

l+=1

df_tiles = pd.DataFrame(tile_centers, columns =['Tile No', 'RA_Center', 'DEC_Center'], index=None)

df_tiles.to_csv('tiles_GIT.csv')

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