The accompanying file 

   batse_tte_blocks.txt

contains the results of optimal segmentation of 
Time-Tagged Event (TTE) data for 532 BATSE gamma 
ray bursts.  This ascii file is meant to be read 
into data analysis systems, such as MatLab, IDL, 
Python, or the like.

WARNING: Some of these BATSE triggers do not correspond
to actual bursts, or only capture part of the burst.
The user of these data should carefully screen out
such cases.  In a subsequent posting, we will present
an cleaned up version of these data.

The raw data, in convenient ascii files for each burst, 
can be downloaded at the web site:

http://cossc.gsfc.nasa.gov/batse/batseburst/tte/ascii.html

For each burst, there are 10 sets of data, one for each
combination of the four BATSE energy channels, as follows:


# 1 [ 1 2 3 4 ]
# 2 [ 1 2 3 ] 
# 3 [ 2 3 4 ] 
# 4 [ 1 2 ] 
# 5 [ 2 3 ] 
# 6 [ 3 4 ] 
# 7 [ 1 ] 
# 8 [ 2 ] 
# 9 [ 3 ] 
#10 [ 4 ]


In each case the arrival times of all photons from the 
indicated energy channels are put together (and of course
ordered in time).  This selection represents all combinations
of channels adjacent in energy.  Note that the spectra of
the bursts dictate the signal-to-noise in the channels.
Since there is great variety of spectra, there is no universal
selection of bands to lump together, for example in 
determination of energy-dependent lags. Hence we have 
included all selections that we think will be useful,
omitting only those with gaps (e.g. [ 1 4 ] ).

The data for each of these ten selections is contained
in three lines:

Line 1: the BATSE trigger number, 
        the number of blocks in the optimal segmentation

Line 2: the times at which the blocks end;
        block 1 begins at zero
        others begin at the end time of the previous block
Line 3: the number of photons in the block

The height of the block, representing the photon rate 
in counts per second, is found by dividing the count in
line 3 by the length of the block, in seconds, obtained
from line 2.

There is no separation between the bursts.


The optimization was carried out using the maximum likelihood
fitness, or "cost", function described in an accompanying 
paper that will be posted shortly.  The log(gamma) parameter,
for the prior distribution of the number of changepoints,
was taken to be  the natural logarithm of the number of photons
in the data set analyzed.

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