| Age | Commit message (Collapse) | Author |
|---|
|
|
|
Thanks clang!
|
|
Previously I was calculating the expected number of delta ray photons when
integrating over the shower path, but since the delta rays are produced along
the particle path and not further out like the shower photons, this wasn't
correct. The normalization of the probability distribution for the photons
produced along the path was also not handled correctly.
This commit adds a new function called integrate_path_delta_ray() to compute
the expected number of photons from delta rays hitting each PMT. Currently this
means that the likelihood function for muons will be significantly slower than
previously, but hopefully I can speed it up again in the future (for example by
skipping the shower calculation which is negligible for lower energy muons).
|
|
|
|
This commit speeds up the likelihood function by integrating the charge along
the track inline instead of creating an array and then calling trapz(). It also
introduces two global variables avg_index_d2o and avg_index_h2o which are the
average indices of refraction for D2O and H2O weighted by the PMT quantum
efficiency and the Cerenkov spectrum.
|
|
This commit speeds up the likelihood calculation by eliminating most calls to
acos(). This is done by updating the PMT response lookup tables to be as a
function of the cosine of the angle between the photon and the PMT normal
instead of the angle itself.
|
|
Previously I was computing the fraction of light absorbed and scattered by
calculating an average absorption and scattering length weighted by the
Cerenkov spectrum and the PMT quantum efficiency, which isn't correct since we
should be averaging the absorption and scattering probabilities, not the
absorption and scattering lengths.
This commit fixes this by instead computing the average probability that a
photon is absorbed or scattered as a function of the distance travelled by
integrating the absorption and scattering probabilities over all wavelengths
weighted by the PMT quantum efficiency and the Cerenkov spectrum.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Based on some initial testing it seems that the subplex minimization algorithm
performs *much* better than BOBYQA for multi-particle fits. It is also a bit
slower, so I will probably have to figure out how to speed things up.
|
|
|
|
|
|
|
|
|
|
|
|
To enable the fitter to run outside of the src directory, I created a new
function open_file() which works exactly like fopen() except that it searches
for the file in both the current working directory and the path specified by an
environment variable.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
In the processed zdab files (the SNOCR_* files), the first logical record just
has a run header bank and no EV bank.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
This is so that in the future if we only integrate over the path in the PSUP we
don't overestimate the Cerenkov light from delta rays.
|
|
This seems to speed things up a little bit.
|
|
Previously the find peaks algorithm would ignore any PMT hits within the
Cerenkov ring of previously found rings. This had the problem that occasionally
the algorithm would repeatedly find the same direction due to hits outside of
the Cerenkov cone. The new update was inspired by how SuperK does this and
instead we "subtract" off the previous rings by subtracting the average qhs
times e^(-cos(theta-1/n)/0.1) from each PMT for each previous ring.
Based on some quick testing this seems a lot better than the previous
algorithm, but still probably needs some tweaking.
|
|
This commit updates the likelihood function to take into account Cerenkov light
produced from delta rays produced by muons. The angular distribution of this
light is currently assumed to be constant along the track and parameterized in
the same way as the Cerenkov light from an electromagnetic shower. Currently I
assume the light is produced uniformly along the track which isn't exactly
correct, but should be good enough.
|
|
|
|
|
