Getting Started
Using iDiffuse to calculate expected photometric precisions
Import necessary packages
from __future__ import print_function
import numpy as np
import pandas as pd
import idiffuse
import pysynphot as S
# if pysynphot says:
# - UserWarning: PYSYN_CDBS is undefined; functionality will be SEVERELY crippled.
# then idiffuse should still work.
# You can set the PYSYN_CDBS by following the pysynphot installation instructions here:
# https://pysynphot.readthedocs.io/en/latest/#installation-and-setup
Initialize Telescope Class that implements the ARC 3.5m at APO
Also correctly sets up the diffuser in ARCTIC (0.34 deg opening angle, at a distance of 200mm)
arc = idiffuse.telescope.TelescopeARC()
# Print descriptive text about the telescope
print(arc)
Telescope: ARC 3.5m
Throughput (flat) (%): 39.838
Diameter (cm): 350.000
Fnum: 8.000
Focal length (m): 28.000
Gain: 2.000
Pixel size (um): 15.000
Num pixels: 4096.000
Plate scale (arcsec/pix): 0.110
FOV (arcmin): 7.509
Dark Noise (e/s/pix): 0.000
Read Noise (e/pix): 3.700
Altitude (m): 2788.000
Central Obstruction (%): 9.000
Diffuser dist to detector (mm): 200.000
11 available filters in folder: /Users/gks/Dropbox/mypylib/notebooks/GIT/idiffuse/idiffuse/filters
The arc object has an attribute to list the available filters. These are also accessible through the arc.FILT_DICT dictionary.
arc.get_filter_filenames()
['/Users/gks/Dropbox/mypylib/notebooks/GIT/idiffuse/idiffuse/filters/arctic_qe.txt',
'/Users/gks/Dropbox/mypylib/notebooks/GIT/idiffuse/idiffuse/filters/bess-b.txt',
'/Users/gks/Dropbox/mypylib/notebooks/GIT/idiffuse/idiffuse/filters/bess-i.txt',
'/Users/gks/Dropbox/mypylib/notebooks/GIT/idiffuse/idiffuse/filters/bess-r.txt',
'/Users/gks/Dropbox/mypylib/notebooks/GIT/idiffuse/idiffuse/filters/bess-u.txt',
'/Users/gks/Dropbox/mypylib/notebooks/GIT/idiffuse/idiffuse/filters/bess-v.txt',
'/Users/gks/Dropbox/mypylib/notebooks/GIT/idiffuse/idiffuse/filters/semrock_857_30.txt',
'/Users/gks/Dropbox/mypylib/notebooks/GIT/idiffuse/idiffuse/filters/sloan_g_filter.txt',
'/Users/gks/Dropbox/mypylib/notebooks/GIT/idiffuse/idiffuse/filters/sloan_i_filter.txt',
'/Users/gks/Dropbox/mypylib/notebooks/GIT/idiffuse/idiffuse/filters/sloan_r_filter.txt',
'/Users/gks/Dropbox/mypylib/notebooks/GIT/idiffuse/idiffuse/filters/sloan_z_filter.txt']
Plot Telescope QE / Throughput plot for given filters
We can plot the throughput of the different filters used, and compare them to the Quantum Efficiency of the ARCTIC detector.
# use FILT_DICT dictionary
ifilt = S.FileBandpass(arc.FILTER_DICT['sloan_i_filter.txt'])
rfilt = S.FileBandpass(arc.FILTER_DICT['sloan_r_filter.txt'])
arc.plot_throughput(bandpass=rfilt,bandpass_name="SDSS r'")
arc.plot_throughput(bandpass=ifilt,bandpass_name="SDSS i'")
Example on-sky diffuser assisted precision
Below are examples of how to calculate the expected photometric error and the resulting cadence for on-sky observations of a given target. In Stefansson et al. 2018, we compare these expected precision values to the on-sky achieved precision.
K2-28b
err, cad = arc.get_err_cad_for_adu(vegamag=13.8,
BandPass=ifilt,
binning=4,
max_adu_per_pixel=2800.,
read_time=2.7,
airmass=1.3,
num_ref_stars=3.,
sky_mag_per_arcsec=19.7)
##### Exptime #####
Exptime [s]: 29.954
Total cadence [s]: 32.654
Obs. Efficiency [%]: 91.73
npix [pix]: 307.30
n_b [pix]: 537.77
##### Counts #####
Star counts - Total [adu]: 860439.86
Star counts - /pix [adu/pix]: 2800.00
Sky counts - Total [adu]: 223454.12
Sky counts - /pix [adu/pix]: 727.15
##### Noise in exptime=29.95s ######
Photometric noise [ppm] 1045.96
Photon noise [ppm]: 880.23
Scintillation noise [ppm]: 452.96
Total noise [ppm]: 1139.83
##### Noise ######
Noise in 1min [ppm]: 840.88
Noise in 30min [ppm]: 153.52
#####
TRES-3b
err, cad = arc.get_err_cad_for_adu(vegamag=11.2,
BandPass=ifilt,
binning=4,
max_adu_per_pixel=27000.,
read_time=2.7,
airmass=1.2,
num_ref_stars=13.,
sky_mag_per_arcsec=19.7)
##### Exptime #####
Exptime [s]: 26.343
Total cadence [s]: 29.043
Obs. Efficiency [%]: 90.70
npix [pix]: 307.30
n_b [pix]: 537.77
##### Counts #####
Star counts - Total [adu]: 8297098.66
Star counts - /pix [adu/pix]: 27000.00
Sky counts - Total [adu]: 196514.27
Sky counts - /pix [adu/pix]: 639.49
##### Noise in exptime=26.34s ######
Photometric noise [ppm] 259.50
Photon noise [ppm]: 254.75
Scintillation noise [ppm]: 377.35
Total noise [ppm]: 457.97
##### Noise ######
Noise in 1min [ppm]: 318.63
Noise in 30min [ppm]: 58.17
#####
WASP-85 A b
err, cad = arc.get_err_cad_for_adu(vegamag=10.0,
BandPass=rfilt,
binning=4,
max_adu_per_pixel=20000.,
read_time=2.7,
airmass=1.3,
num_ref_stars=3.,
sky_mag_per_arcsec=19.7)
##### Exptime #####
Exptime [s]: 3.896
Total cadence [s]: 6.596
Obs. Efficiency [%]: 59.07
npix [pix]: 307.30
n_b [pix]: 537.77
##### Counts #####
Star counts - Total [adu]: 6145999.01
Star counts - /pix [adu/pix]: 20000.00
Sky counts - Total [adu]: 48201.48
Sky counts - /pix [adu/pix]: 156.85
##### Noise in exptime=3.90s ######
Photometric noise [ppm] 331.46
Photon noise [ppm]: 329.35
Scintillation noise [ppm]: 1255.93
Total noise [ppm]: 1298.94
##### Noise ######
Noise in 1min [ppm]: 430.69
Noise in 30min [ppm]: 78.63
#####
K2-100b
err, cad = arc.get_err_cad_for_adu(vegamag=10.0,
BandPass=ifilt,
binning=4,
max_adu_per_pixel=23000.,
read_time=2.7,
airmass=1.05,
num_ref_stars=10.,
sky_mag_per_arcsec=19.7)
##### Exptime #####
Exptime [s]: 7.431
Total cadence [s]: 10.131
Obs. Efficiency [%]: 73.35
npix [pix]: 307.30
n_b [pix]: 537.77
##### Counts #####
Star counts - Total [adu]: 7067898.86
Star counts - /pix [adu/pix]: 23000.00
Sky counts - Total [adu]: 55431.70
Sky counts - /pix [adu/pix]: 180.38
##### Noise in exptime=7.43s ######
Photometric noise [ppm] 280.74
Photon noise [ppm]: 278.96
Scintillation noise [ppm]: 568.44
Total noise [ppm]: 633.98
##### Noise ######
Noise in 1min [ppm]: 260.51
Noise in 30min [ppm]: 47.56
#####