## 2. Observations and data analysis## 2.1. The RXTE/PCA observations4U1915-05 was observed by RXTE on 19 occasions in 1996: on
February 10th, March 13th, May 5-6th, May 14th to 23rd, June 1st, July
15th, August 16th, September 6th and October 29th. The log of the
observations is presented in Table 2. An observation has an
exposure time of roughly 8000 seconds and is provided as typically
three data files with gaps between files. These files are refered as
segments of observation in our analysis. We report on the data
collected in the 2-60 keV range with the PCA. It consists of 5
nearly identical large area Proportional Counter Units (PCU 0 to 4)
corresponding to a total collecting area of
cm
The all-sky monitor (ASM) aboard RXTE consists of three
coded-aperture cameras mounted on a motorized rotation drive, allowing
to view different regions of the sky during a satellite orbit (Levine
et al. 1996). The instrument is sensitive in the energy range
2-12 keV. Fig. 1 is
the ASM light curve of 4U1915-05 during 1996. The binning time is
one day. The times of the PCA observations are flagged with arrows.
Four X-ray bursts were recorded during the PCA observations: on May
5th, June 1st, August 16th and October 29th. With the exception of the
first one, they began during a dip. A total of 58 primary or secondary
dips (or parts of dips) were observed, lasting typically 400 seconds.
Dips are thought to be due to occultation of the internal emitting
region by vertical structure at the outer edge of the accretion disk
(Walter et al. 1982; White & Swank 1982). Primary dips occur at
intervals consistent with an orbital period of
50 minutes. Secondary dips are
"anomalous" dips, narrower than the primary ones. They occur
irregularly at 180
## 2.2. Data modesThe ## 2.3. Data analysis## 2.3.1. Light curves, color-color and hardness-intensity diagramsFor the light curves, color-color and hardness-intensity diagrams,
we have used the As all the PCUs have different energy responses, the absence of a PCU can mimic spectral variations. Thus, we used the data collected with the first three PCUs since they were continuously operating during all our observations. Differences between response matrix of gain epoch 1 and 3 introduce differences in the boundaries of the energy channels used. This instrumental effect introduces shifts between the colors or count rates obtained from different epochs. We quantified these shifts using the Crab nebula which is a steady source. We find that the correction factors to apply to gain epoch 1 data in order to make them match the epoch 3 region are +10.1%, -1.5%, -1.1% and +2.8% for the count rates in the energy bands 1.7-3, 3-5, 5-10 and 10-30 keV respectively. The correction factors are -10.5% and +4.0% for the soft and hard colors respectively. We applied these correction factors to 4U1915-05 data. ## 2.3.2. Power density spectraTo investigate the variability of 4U1915-05, we computed PDS using
the FTOOLS To reach timescales ranging from roughly 10 ms to 4 minutes, the data were rebinned to a ms resolution. Then, each continuous set was divided into segments of 65536 bins. A fast Fourier transform (FFT) of each segment was computed yielding a low frequency PDS in the range Hz. To reach timescales ranging from roughly 0.25 ms to 1 second, the data were rebinned to a s resolution. Each continuous set was divided into segments of 8192 bins. A FFT of each segment was computed leading to high frequency PDS in the range 1-4096 Hz. Then, the low or high frequency PDS obtained from a given set of observations, a given observation or a given segment of observation were averaged together to obtain a final PDS representative of the set, observation or segment. The PDS were normalized according to Leahy et al. (1983) so that the average power expected from a Poisson distribution is 2. We have checked in the high frequency PDS that the average white noise level above 1500 Hz was indeed 2, indicating that deadtime corrections were not needed. The error on each PDS bin has been set to where M is the number of raw PDS averaged together, W the number of raw frequency bins averaged together (Van der Klis 1989). is set to the Fourier power at the point considered for low frequency PDS (where the power can differ significantly from the white noise level 2 because of the source aperiodic variability) and to 2 for the high frequency PDS. Both logarithmic and linear rebinnings were applied. Different components were summed to fit the resulting PDS: a constant for the white noise level (C), a power law for the VLFN (PL), a cutoff power law for the HFN (CPL). The functional shapes used for the latter components are and respectively, where A is the normalization, the power law index, the frequency and the cutoff frequency. C is a free parameter for the high frequency PDS and is set to 2 for the low frequency PDS. We used Gaussians (G) to describe the QPO features. Note that Lorentzians are also currently used (Van der Klis 1995). All the fits were performed using a minimization technique. The errors on the analytic model parameters correspond to a variation of 2.7, equivalent to the 90% confidence region for a single interesting parameter. To compute the RMS, the PDS were normalized according to Belloni and Hasinger (1990) taking into account the total and background count rates. The RMS was then obtained by integrating the normalized PDS in the appropriate frequency range. We have also computed PDS of data from the bursts. For the bursts
recorded in the 122 ## 2.3.3. Search technique for quasi-periodic oscillationsTo look for weak QPOs in the PDS (as in the case of 4U1915-05), we
developed an algorithm which computes for each PDS the width of the
window which maximises the signal to noise ratio (SNR) within the
window. Assuming that a given window of width where P This algorithm does not take into account the number of trials in
determining the significance level. To determine the real significance
of the detections, we follow Van der Klis (1989) and define the
confidence level
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