Unit conversion with broadband detectors looking at the CMB

healpy
cosmology
Published

September 4, 2020

import healpy as hp
import numpy as np
import matplotlib.pyplot as plt
from pysm3 import units as u
import pysm3 as pysm
%matplotlib inline
dip = hp.synfast([0,1], lmax=1, nside=128) * u.V
/home/zonca/zonca/p/software/healpy/healpy/sphtfunc.py:438: FutureChangeWarning: The order of the input cl's will change in a future release.
Use new=True keyword to start using the new order.
See documentation of healpy.synalm.
  category=FutureChangeWarning,
/home/zonca/zonca/p/software/healpy/healpy/sphtfunc.py:824: UserWarning: Sigma is 0.000000 arcmin (0.000000 rad) 
  sigma * 60 * 180 / np.pi, sigma
/home/zonca/zonca/p/software/healpy/healpy/sphtfunc.py:829: UserWarning: -> fwhm is 0.000000 arcmin
  sigma * 60 * 180 / np.pi * (2.0 * np.sqrt(2.0 * np.log(2.0)))
hp.mollview(dip, unit=dip.unit)

We measure the sky with out broadband instrument, we assume we only measure the CMB solar dipole, initially the units are arbitrary, for example Volts of our instrument.

Next we calibrate on the solar dipole, which is known to be 3.3 mK.

calibration_factor = 2 * 3.3 * u.mK_CMB / (dip.max() - dip.min())
calibration_factor

3.9033114mKCMBV

calibrated_dip = calibration_factor * dip
calibrated_dip

[0.44224864, 0.41525913, 0.40329058, , 0.43028009, 0.44224864, 0.41525913]mKCMB

hp.mollview(calibrated_dip, unit=calibrated_dip.unit)

First we simplify and consider a delta-frequency instrument at 300 GHz

center_frequency = 300 * u.GHz
dip_peak = calibrated_dip.max()
calibrated_dip.max()

3.3mKCMB

calibrated_dip.max().to(u.mK_RJ, equivalencies=u.cmb_equivalencies(center_frequency))

0.47252855mKRJ

calibrated_dip.max().to(u.MJy/u.sr, equivalencies=u.cmb_equivalencies(center_frequency))

1.3065993MJysr

Next we assume instead that we have a broadband instrument, of 20% bandwidth, with uniform response in that range. For simplicity, we only take 4 points.

freq = [270, 290, 310, 330] * u.GHz
weights = [1, 1, 1, 1]
weights /= np.trapz(weights, freq)
weights

[0.016666667, 0.016666667, 0.016666667, 0.016666667]1GHz

The instrument bandpass is defined in power so we can transform our signal in MJy/sr at the 4 reference frequencies, then integrate.

dip_peak_MJysr = dip_peak.to(u.MJy/u.sr, equivalencies=u.cmb_equivalencies(freq))
dip_peak_MJysr

[1.4642815, 1.363255, 1.2471396, 1.1232462]MJysr

integrated_SR = np.trapz(dip_peak_MJysr * weights, freq)
integrated_SR

1.3013861MJysr

This is different than assuming uniform bandpass in KCMB, where instead we would recover the same result of the delta-bandpass:

np.trapz(dip_peak * weights, freq)

3.3mKCMB

SR = u.MJy/u.sr

We use the PySM 3 function to compute unit conversion given a bandpass

$ [unit_{out}] = [unit_{in}] { C_{unit_{out}}{Jy~sr{-1}}() g() d} $

which comes from equating in power:

$ [unit_{out}]{ C_{unit_{out}}{Jy~sr{-1}}() g() d} = [unit_{in}]C_{unit_{in}}{Jy~sr{-1}}() g() d$

SR

MJysr

pysm.utils.bandpass_unit_conversion(freq, weights=weights, output_unit=u.mK_CMB, input_unit=SR)

2.5357578mKCMBsrMJy

integrated_SR * _

3.3mKCMB

1 * u.mK_CMB / (1 * SR)

1mKCMBsrMJy

We can doublecheck the implementation of the PySM function by executing it here:

K_CMB_to_MJysr = ((1*SR).to(u.mK_CMB, equivalencies=u.cmb_equivalencies(freq)))/(1*SR)
K_CMB_to_MJysr

[2.253665, 2.420677, 2.646055, 2.9379134]mKCMBsrMJy

Integrating the K_CMB_to_MJysr conversion factor is wrong, we always need to do the integral in power, therefore we integrate the inverse and then take its inverse.

np.trapz(K_CMB_to_MJysr * weights, freq)

2.5541738mKCMBsrMJy

1/np.trapz(1/K_CMB_to_MJysr * weights, freq)

2.5357578mKCMBsrMJy