| Age | Commit message (Collapse) | Author |
|---|
|
|
|
|
|
|
|
This commit adds lots of comments to sno_charge.c and makes a couple of other
changes:
- use interp1d() instead of the GSL interpolation routines
- increase MAX_PE to 100
I increased MAX_PE because I determined that it had a rather large impact on
the likelihood function for 500 MeV electrons. This unfortunately slows down
the initialization by a lot. I think I could speed this up by convolving the
single PE charge distribution with a gaussian *before* convolving the charge
distributions to compute the charge distributions for multiple PE.
|
|
This commit adds Rayleigh scattering to the likelihood function. The Rayleigh
scattering lengths come from rsp_rayleigh.dat from SNOMAN which only includes
photons which scattered +/- 10 ns around the prompt peak. The fraction of light
which scatters is treated the same in the likelihood as reflected light, i.e.
it is uniform across all the PMTs in the detector and the time PDF is assumed
to be a constant for a fixed amount of time after the prompt peak.
|
|
|
|
integral
|
|
This commit speeds up the likelihood function by about ~20% by using the
precomputed track positions, directions, times, etc. instead of interpolating
them on the fly.
It also switches to computing the number of points to integrate along the track
by dividing the track length by a specified distance, currently set to 1 cm.
This should hopefully speed things up for lower energies and result in more
stable fits at high energies.
|
|
particle id
|
|
To characterize the angular distribution of photons from an electromagnetic
shower I came up with the following functional form:
f(cos_theta) ~ exp(-abs(cos_theta-mu)^alpha/beta)
and fit this to data simulated using RAT-PAC at several different energies. I
then fit the alpha and beta coefficients as a function of energy to the
functional form:
alpha = c0 + c1/log(c2*T0 + c3)
beta = c0 + c1/log(c2*T0 + c3).
where T0 is the initial energy of the electron in MeV and c0, c1, c2, and c3
are parameters which I fit.
The longitudinal distribution of the photons generated from an electromagnetic
shower is described by a gamma distribution:
f(x) = x**(a-1)*exp(-x/b)/(Gamma(a)*b**a).
This parameterization comes from the PDG "Passage of particles through matter"
section 32.5. I also fit the data from my RAT-PAC simulation, but currently I
am not using it, and instead using a simpler form to calculate the coefficients
from the PDG (although I estimated the b parameter from the RAT-PAC data).
I also sped up the calculation of the solid angle by making a lookup table
since it was taking a significant fraction of the time to compute the
likelihood function.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
This commit updates the PMT response bank and absorption lengths for H2O and
D2O based on the values in mcprod which were used for the LETA analysis.
|
|
Doh! I was previously using the tube number variable!
|
|
When testing out the fitter on 500 MeV muons, there was at least one event
which started out at a position very far from the true position. This event had
a secondary electron like ring which is what I suspect caused the fit to start
out in a position far from the true position.
This fix correctly starts the minimization close to the true position. In the
future I should look at updating get_direction() so that it finds the largest
ring direction instead of just doing a weighted average of all the vectors from
the position to the PMTs.
|
|
|
|
|
|
|
|
This commit adds the ability to run the fit program with the
--skip-second-event flag to only fit the first event after a MAST bank. This
way we avoid fitting secondaries like Michel electrons when fitting MC events.
|
|
the quantum efficiency
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
minimization phase
|
|
|
|
|
|
This commit adds code to read in the PMT response from the PMTR bank from
SNOMAN. This file was used for the grey disk model in SNOMAN and was created
using a full 3D simulation of the PMT and concentrator. Since the PMT response
in SNOMAN included the quantum efficiency of the PMT, we have to divide that
out to get just the PMT response independent of the quantum efficiency.
I also updated the likelihood calculation to use the pmt response. Currently
the energy is being fit too high which I think will improve when we update the
solid angle calculation to use the radius of the concentrator instead of the
PMT.
|
|
This commit adds a fast function to calculate the expected number of PE at a
PMT without numerically integrating over the track. This calculation is *much*
faster than integrating over the track (~30 ms compared to several seconds) and
so we use it during the "quick" minimization phase of the fit to quickly find
the best position.
|
|
|
|
This commit updates the initial guess for the energy using a simple heuristic
of ~6 hits/MeV. I also updated the initial phase where we do a bunch of "quick"
minimizations to loop over a series of starting positions and automatically
calculate the approximate direction and t0 for the event.
|
|
This commit updates the fit_event function to first do a series of "quick"
minimizations to try and find a good set of starting parameters for the "real"
fit.
I also updated the bounds on theta and phi since having hard bounds on these
angle coordinates could prevent the fitter from finding the minimum.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
This commit updates the likelihood fit to use the KL path expansion. Currently,
I'm just using one coefficient for the path in both x and y.
|
|
|
|
|
