Flux Calibration

Chapter 1 Cosmology

Z- Spec has been operating as a PI instrument at the Cal- tech Sub-millimeter Observatory (CSO) on Mauna Kea, Hawaii

3.3 Data Collection and Reduction

3.3.4 Flux Calibration

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Figure 3.6 Diagnostic plot for the nightly pointing of MACSJ 0744.8 on the night of November 4th, 2009. (Left) elevation pointing offset, (right) azimuthal pointing offset, (black diamonds) raw data, and (red diamonds) corrected data. Note the overall 30 arcsecond pointing offset in azimuth. When corrected with the pointing model, the residuals drop to about 5 arcseconds.

figure, these pointing erors can be well-modeled and removed. These models are accurate to

∼5 arcseconds, and this pointing uncertainty produces an effective broadening of the point- spread function (PSF). Specifically, an effective PSF is determined by convolving Bolocam’s nominal PSF, which has a full-width at half-maximum (FWHM) of 58 arcseconds, with a two-dimensional Gaussian profile with σ = 5 arcsec. Fortunately, this broadening of the PSF due to pointing uncertainty is small, and it does not have a significant impact on the derived results (especially for resolved objects like galaxy clusters).

Figure 3.7 Telescope pointing for all of the observations of MACSJ 0744.8 and the associated pointing sources. This data is used as the basis for the pointing corrections depicted in Figure 3.8.

Figure 3.8 (Left) Elevational- and (right) azimuthal-dependence of the observational point- ing error for MACSJ 0744.8 in a 2009 observing run. Black lines depict the best-fit pointing models. (Bottom panels) Histograms of the total number of observations at a particular ele- vation, left, or azimuth, right. (Top panels) Elevation pointing offset either uncorrected, left, or corrected, right, with the best-fit pointing models. (Middle panels) Azimuthal pointing offset either uncorrected, left, or corrected, right, with the best-fit pointing models.

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Figure 3.9 Flux calibration (mV/Jy) for the October 2009 observation run as a function of median DC bolometer voltage. For this particular run, we observed Uranus (red diamonds), Neptune (green diamonds), and a secondary calibration source (blue diamonds) given in Sandell [251] . The estimated flux is given in the legend, and the error bars are gener- ally larger than 0.00 for sources with unknown flux. Note how both the responsivity and voltage decrease with atmospheric loading, making the flux-calibration a linear function of atmospheric loading.

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Fig. 2.— Timestream noise PSD for a typical Bolocam detec- tor. The black curve shows the raw PSD recorded by the detector;

spectral lines at the fundamental scan frequencies are clearly seen above the broadband atmospheric noise. The red curve shows the noise PSD after subtracting the atmospheric noise using the aver- age signal over the FOV. This timestream is then high-pass filtered at 250 mHz to produce the green PSD. Note that there is very little cluster signal above ≃ 2 Hz, where there are some spectral lines due to the readout electronics. The dashed horizontal line provides an estimate of the photon, or BLIP, noise.

The raw Bolocam timestreams are dominated by noise sourced by fluctuations in the water vapor in the atmo- sphere, which have a power spectrum that rises sharply at low frequencies. In order to optimally subtract the atmospheric noise, we have used a slightly modified ver- sion of the average subtraction algorithm described in Sayers et al. (2010, hereafter S10). We have modified the S10 algorithm because these cluster data contain ad- ditional atmospheric noise caused by the Lissajous scan pattern. Since we are scanning the telescope parallel to RA and dec, the airmass we are looking through is con- stantly changing. As a result, our data contain a large amount of atmospheric signal in narrow bands centered on the two fundamental scan frequencies.

Following the algorithm in S10, we first create a tem- plate of the atmosphere by averaging the signal from all of our detectors at each time sample (i.e., the average signal over the FOV). In S10, this template is subtracted from each detector’s timestream after weighting it by the relative gain of that detector, which is determined from the correlation coefficient between the timestream and the template. We use a single correlation coefficient for each detector for each 10-minute-long observation. How- ever, a significant fraction of the atmospheric noise at the fundamental scan frequencies remains in the data after application of the S10 algorithm, indicating that we have slightly misestimated the correlation coefficients. There- fore, we modified the S10 algorithm to compute the cor- relation coefficients for the template based only on the data within a narrow band centered on the two funda- mental scan frequencies. The atmospheric noise power in these narrow frequency bands is roughly an order of magnitude above the broadband atmospheric noise at nearby frequencies; consequently, the data in these nar- row bands provide a high signal-to-noise estimate of each detector’s response to atmospheric signal. The narrow- band atmospheric noise features are completely removed using this modified S10 algorithm, and the amount of residual broadband atmospheric noise is slightly reduced compared to the results from the original S10 algorithm.

After applying this average subtraction algorithm to

the timestream data, we then high-pass filter the data according to

F = 1 − 1

1 + (10

^{f /f0−1}

)

^κ

with f

0

= 250 mHz and κ = 8. The value of f

0

was cho- sen to maximize the spatially-extended S/N for the typ- ical cluster in our sample based on tests with f

₀

varying from 0 to 400 mHz, and the value of κ was chosen to pro- duce a sharp cutoff with minimal ringing. Figure 2 shows a typical pre and post-subtraction timestream noise PSD.

3.3.

Transfer function of the atmospheric noise filtering