Tophat bands for Data Challenge
In the process of science book forecasting, we came up with eight bands chosen to split up the four atmospheric windows. These bandpasses are listed in Table 1 of Victor's 2016-05-31 posting. I used the center frequencies and fractional bandwidths (Δν / ν). I then shifted the 215 GHz band up slightly (to 220 GHz) and widened the 270 GHz band (to 22%) to close a small gap between those bands.
For each band, I calculated
- relative brightness of a dust-type signal with βd = 1.59 and Td = 19.5 K; compared to 353 GHz reference frequency
- relative brightness of a synchrotron-type signal with βsync = -3.0; compared to 23 GHz reference frequency
The scaling factors calculate for non-tophat vs tophat bands agree to better than 1%.
This calculation requires us to specify the convention that we use for our tophat bandpass. I define this tophat to be such that a single-moded antenna (AΩ scales as λ2) would have uniform response as a function of frequency to a beam-filling Rayleigh-Jeans source. Before we start generating signal simulations, it would be a good idea to check that people generating foreground models agree on this calculation.
The figure below shows calculated atmospheric brightness spectra (at zenith) for South Pole at 0.5 mm PWV and Atacama at 1.0 mm PWV (both are near median values). Atmospheric spectra are courtesy of Denis Barkats, generated using am. I plotted the tophat bands on top of these spectra, with the height of each rectangle equal to the band-averaged brightness temperature using the South Pole spectrum. Also shown (in green) are the BICEP2 / Keck Array 150 GHz bandpass and the Keck Array 95 and 220 GHz bandpasses, for comparison.
|Name||center [GHz]||width [GHz]|| dust scale factor
from 353 GHz
| sync scale factor
from 23 GHz
|Tsky (Pole) [K]||Tsky (Atacama) [K]|
Code used for calculations and plots in this posting: tophat_bandpass.py
Colin Bischoff, 2016-11-02