SAND project

Spectrophotometer for Aerosol Night Detection

See the presentation made at 2005 Fall Meeting of the International Dark Sky Association
Information on the 2005 intensive light pollution measurement experiment in California-Arizona-Utah

Objectives:

The final goal of this project is to develop a methodology to monitor aerosol optical properties during the night. This methodology benefits from the presence of aerosols in the atmosphere and their impact on the level of light pollution (artificial sky radiance, see project ILLUMINA). In the shorter term, we consider that the challenge of detecting light pollution represents by itself an important scientific problem.

SAND usefull links

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SAND-4 spectrometer

Previous version of SAND and OBSAND


SAND-3B running in Hefei China

SAND-3A (out of order)	SAND-2: Light pollution spectrophotometer Opto-mecanical design and realization: M. Aubé / M. Fréchette

OBSAND-1: First Light pollution spectrophotometer observatory	SAND-2beta: The unit that was used to monitor Arizona & California sites in 2005

SAND-1: First prototype with SBIG camera	SAND-0: A very basic first version...

SAND is a diffraction grating (600 lines per mm) long slit spectrometer. Spectral information is collected by a CCD camera.

SAND-4

We are now using version 4 of SAND. In this new version, we use the DSS-7 spectrometer manufactured by SBIG and replaced the ST7 by ST-402 CCD camera. Along with that modification, we designed a new portable observatory utilizing an acrylic transparent dome to protect the instrumentation from bad weather.

Typical data acquired by SAND

Figure 1: Average of 509 lines of the spectral image from mount Palomar observatory toward Los Angeles (30 deg. elevation angle) as detetected during may 2005. A certain number of spectral lines were identified on this figure. The vertical axis represents the averaged calibrated spectral luminance whereas the horizontal axis represents the wavelength (which is related to the position in pixel on the CCD). Most important spectral lines have been identified on this plot.

For each acquisition, a spectral image is produced (figure 2). On this image, spectral information is distributed along the horizontal axis (lines).


Figure 2: SAND-2 spectral image of light pollution above Anhui Institute of optics and fine mechanics , near Hefei China. This image corresponds to an integration time of 1500 seconds.	Figure 3: Thermal noise image with an integration time of 1500 seconds and a CCD temperature of 0 ^o C.

We must remove the thermal noise (dark frame). The dark frame is obtained by the acquisition of an image of the same integration time, while maintaining the obturator closed (figure 3).


Figure 4: Spectral image of light pollution above AIOFM, China (integration time = 1500s). This image was corrected for thermal noise.	Figure 5: Spectrum extracted from fig. 4. Data from each line have been averaged.

The gray vertical lines on figure 4 represent emission lines of various chemical elements (mainly those contained in lamps). When the spectroscopic module of the SAND is well oriented, these lines are parallel to the columns of the image. The spectrum present on each line of the image spread out from ultraviolet to near infrared. If we choose the period of observation to avoid the presence of the moon, the spectrum of each line is the sum of the spectrum of light pollution, of the spectrum of stars, the spectrum of the interstellar objects, the spectrum of the extragalactic objects and the aurorae located along the line of sight. Light pollution shows a diffuse pattern in the sky, its angular variation is very smooth (especially if the spectrometer slit see a small fraction of the sky). On the other hand the stellar, interstellar and extragalactic contents are highly variable from one place to another along the slit. To produce a light pollution spectrum, we are averaging several lines of the image. This operation increases the signal to noise ratio (S/N) and an average indeed tends to emphasize the stable characteristics of the spectrum. We thus obtain a better contrast between the spectrum of light pollution and the remainder of the spectrum.

The following step consists in carrying out spectral calibration. The issue being to associate a wavelength value for each position in pixel on the image. To achieve that operation, we use a light source of which we already know a good number of lines. A 2^nd order polynomial is fitted to the pixel vs wavelength dataset. This polynomial is then applied to the sky spectrum. After the spectral calibration, we proceed to the photometric calibration. To perform such calibration we need a file containing the ratio radiance over digital count for a given integration. Then if we know the sky image acquisition time we can convert digital count to radiance for any spectrum acquired with the same instrument. The result is shown in fig. 7.


Figure 6: Spectrum after spectral calibration. We are replacing pixel by wavelength. This is possible after characterizing the relation pixel to wavelength with help of a compact fluorescent bulb.	Figure 7: Spectrum after photometric calibration. At this step the vertical units are radiances.

Then in order to be able to compare many spectrum and perform some mathematical operation on them we are resampling the horizontal axis to a constant wavelength step of 0.5 nm starting at 400 nm. Finally we perform a polynomial fitting on parts of the spectrum known to not be contaminated by spectral lines. This polynomial is then substracted from the spectrum. The remaining spectrum is then only from street lamp spectrum.


Figure 8: Spectrum resampled to a constant spectral step of 0.5 nm.	Figure 9: We removed the continium radiation. In that case this radiation is predominantly coming from moon light. The remaining signal is the only coming from light pollution.