Astron. Astrophys. 364, 769-779 (2000)

2. Observations and data analysis

2.1. The ISOPHOT Serendipity Survey

We have analysed ISOSS data of a 20^oee 20^oee field in Chamaeleon-Musca (see Fig. 1). ISOSS is a far-infrared survey at 170 µm, using the ISOPHOT C200 detector (Lemke et al. (1996)) on board the ISO satellite (Kessler et al. (1996)). ISOSS recorded the sky brightness during the slewing of the telescope between two pointed observations. The C200 camera is a pixel array with a field of view. It was used in conjunction with the C_160 broad band filter, which has an effective wavelength of 170 µm. The fastest read-out rate (1/8 sec) was chosen to achieve a dynamical range of . Long slews (exceeding  2^oee) are calibrated using actual detector responsivities, derived from a measurement of the on-board Fine Calibration Source (FCS) preceding the recording of sky surface brightness. For the remaining slews, default detector responsivities are used. Slews were excluded where one or more detector pixel(s) showed erroneously very high values ("hot pixel"). More details on the measuring mode can be found in Bogun et al. (1996).

Fig. 1. a  -  correlation plot for the Chamaeleon-Musca region. Surface brightnesses have been read out from a 20oee 20oee ISOSS slewmap and the ISSA maps at the slew sample positions. The linear correlation line is a least-squares fit for the whole field (continuous line). It has been used to differentiate between cold and warm regions. Data points with   5  above or below the line belong to cold or warm regions respectively, and are plotted in different colours (blue: warm, red: cold). The resulting 3 regimes have been fitted individually, using the ordinary least-squares (OLS) bise ctor method (dashed lines). The slopes of these lines correspond to dust colour temperatures of 13.9 K, 16.3 K and 19.3 K (see Sect. 3.1). b The distribution of the material in the Chamaeleon-Musca using the 3 temperature classes defined by Fig. 1a. The cold regions are the Chamaeleon main clouds Cha I, Cha II, Cha III. The warm regions on the other hand belong predominantly to the warm near-galactic plane region. Two compact warm sources are seen in Cha I (see text).

Approximately 15 % of the sky is covered with an angular resolution (FWHM) of 2´. The in-slew and cross-slew resolutions are similar, since the 1/8 sec on-the-fly integration time corresponds to an in-slew sampling interval of at maximum 1´. The positional accuracy was determined by comparing positions of point sources with positions of corresponding IRAS sources. An agreement of generally better than 1´ was found (Stickel et al. (2000)). The raw (ERD level) ISOSS data were reduced using a batch processing routine based on the ISOPHOT Interactive Analysis ¹ (Gabriel et al. (1997)) software package.

The photometric accuracy has been checked in three ways: First, ISOSS results have been compared with dedicated raster maps (mode AOT PHT22), showing an excellent agreement in surface brightness in the Chamaeleon I region. The sized 100 µm, 150 µm and 200 µm maps of Lehtinen et al. (2000) were compared to the ISOSS slewmap. The rms difference between the PHT22 based interpolated 170 µm intensity values and the ISOSS intensities is 10 % within the surface brightness range 40MJysr^-1 - 150MJysr^-1. The gain and the reproducibility uncertainties in the PHT22 absolute photometry itself are 20 % and 6 % respectively (Klaas et al. (1998)).

A second check has been made by comparing ISOSS with DIRBE data. We have created interpolated DIRBE 170 µm intensities from the 100 µm, 140 µm and 240 µm bands, assuming a modified black body spectrum over the whole FIR range. An agreement between ISOSS and DIRBE calibration within 30 % has been found. Finally, the reproducibility of the ISOSS measurements has been tested by evaluating the slew crossing points in several regions. The root mean square of the relative brightness deviation at crossing points is  15 % (slightly dependent on the brightness level).

2.2. Data analysis

Two main approaches were used investigating ISOSS data of a particular region: (1) Building up a map from all measurements along all slews inside the boundaries of the given field, or (2) analysing all individual slews one by one. In our Chamaeleon study, the large scale structures have been investigated using the mapping method (1), the small scale features have been explored analysing each slew individually (2) (see Hotzel et al. 2000).

Both methods make use of the ISSA maps at 100 µm. In order to achieve absolutely calibrated brightnesses, the ISSA maps were rescaled to agree with the DIRBE calibration: . The actual scaling law was resulted from our comparison of ISSA data with the zodiacal light subtracted DIRBE data of the Chamaeleon-Musca region. They are in accordance with the ISSA - DIRBE transformation found by Wheelock et al. (1994), who however performed an all-sky comparison of the two data sets.

Investigating the large scale structures we have used the whole field of covering Musca and Chamaeleon to find characteristic FIR colours of the galactic background and of the clouds. About 15 % of this large field and  25 % of the actual Chamaeleon clouds are covered by ISOSS slews. 64 % of them (accounting for 90 % of the covered area) are calibrated with actual detector responsivities. A composite ISOSS slew map of the region has been produced and has been smoothed to the 5´ angular resolution of the ISSA maps.

To access the differences in temperature on large spatial scales, we have read out 170 µm and 100 µm intensities at the slew sample positions from the FIR maps. Their scatter plot (see Fig. 1a) shows distinct fingers, representing the different temperature regimes. Straight lines have been fitted individually to these fingers, using the ordinary least-squares (OLS) bisector method (see eg. Isobe et al. 1990). The slopes of these lines have been converted to colour temperature in the same way as the / ratios in the temperature map.

Variation of dust colour temperature was plotted from background corrected 170 µm and 100 µm intensities. The brightness of the assumed uniform background (20 at 170 µm and 8 at 100 µm) was derived from Fig. 1a. The ISOSS slew map and the ISSA 100 µm maps have been combined to a colour temperature map by assuming modified black body radiation over the whole field, and applying colour corrections for the specific ISOPHOT and IRAS bandpasses. This way we get an overview of the spatial colour temperature distribution of the large grain dust component.

Searching for cold cores the slews are searched individually for sources of high / ratio as shown in Fig. 2. The ISOSS slews are smoothed to 5´ to match the IRAS resolution. Doing so, a two-dimentional gaussian smoothing function is centred on the position of the detector centre, thus reducing the data stream to 1 data point per sample (ramp). Corresponding IRAS 100 µm intensities are extracted from the ISSA maps using a linear interpolation algorithm. Intensity peaks along the ISOSS slews are located by searching for local maxima in the second derivative of as a function of sky position. All peaks exceeding 2  () over the background are selected as ISOSS detections. The cross-slew size of a source can not be derived unless it has been crossed by two or more slews. The in-slew size of the source is defined from the profile alone, and is assumed to be the same at 100 µm. For each source, a straight line is fitted to the versus data points, applying the OLS bisector algorithm. If the variation of background intensity is small and the source is optically thin in the FIR, the fitted line slope reflects the ratio of background corrected intensities. We denote this quantity as colour parameter (CP). The colour temperature of the source is derived from CP, assuming modified black body radiation with emissivity law, , and - again - that the source is optically thin. As before, colour corrections for the ISOPHOT and IRAS bandpasses are applied.

Fig. 2. a The IRAS 100 µm and ISOSS 170 µm intensities along a slew crossing Cha I. The ISOSS data are smoothed to match the IRAS resolution. The IRAS 100 µm intensities are scaled by a factor of 5 and a constant is added. Three sources were detected. Peak `B' in the middle is due to VCC no. 4 in Table 1. b vs. plot made with data points towards peak `B'. The slope was derived, using the ordinary least-squares (OLS) bisector method. The slope of this line is referred to as CP (colour parameter) which corresponds to dust colour temperature.

Error estimate: The uncertainty of the surface brightness values of individual ISOSS slews is around 20%. The accuracy of the absolute calibration of the IRAS 100 µm values is the same as the DIRBE 100 µm to which it was scaled. According to Hauser et al. (1998) the uncertainty of the DIRBE gain at 100 µm is 13.5%. The derived slopes have an uncertainty of 1% to 19% due to statistical errors. Accordingly we estimate that the total errors of the slopes in Fig 1, and the CP values are 35% to 50%, and thus our 170/100 colour temperatures derived from the scatter plot slopes and CP values have uncertainties of 1.5 K to 2 K.

© European Southern Observatory (ESO) 2000

Online publication: January 29, 2001
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