Study of the Pi+ Pi- P events from g6c run of CLAS
The purpose of this study is :
- a) to setup and test the software framework to be used
for future analysis of more complex topologies;
- b) to come up with some reasonable procedures and cuts for
- c) to verify that PWA results for this well-known topology
do make sense.
The initial data sample consisted of events in which incoming
beam photon and outgoing proton and pi+ were positively identified.
A loose missing mass cut for (gamma p -> pi+ p) was applied. There
are 34567 events in this sample. It can be divided further into events
with pi- and without pi- as well as into events with some extra particles
(denoted X) and without them. Here is the breakdown:
Therefore, "clean" events of type (A) constitute only 12% of the initial sample.
It would be nice to recover some events from (B), (C) and (D) to increase
the final statistics. In case (B), this is a question of how well assigning
the missing momentum to pi- works. In case (C), it is a question of how
to distinguish "real" extra particles X from those which do not belong to
a particular event. Case (D) is a combination of (C) and (B).
Study of extra particles
Bottom right plot on Mass plots in (C) and (D) shows the invariant mass
of all extra particles. Those are mostly photons, with a significant fraction
of neutrons as well. Kaons are also seen. Also, there were events with
antiprotons, De, extra p, pi+ or pi- as well as "unknown" particles
(their contribution is not that big and they were discarded for now).
A few conclusions:
Almost all extra neutrons are "fake". When they are disregarded,
all (C) or (D) distributions look similar to (A) or (B).
Their inclusion, to the contrary, shifts missing mass significantly
away from the expected values.
There are almost no kaons in (C). However, there are a lot of K-
in (D) when pi- was not detected. This points to a case of
mistaken identity. Simply reassigning pi- ids to those K- works
well and produces distributions similar to (B).
Initially, it was hoped for most of the photons to be "fake" (out
of time, noise, ADC pedestals, etc.) in order to "recover" those
events by simply disregarding photons in them. However, there is
a clear pi0 peak from the 2-photon events (bottom right on plot (C)).
This rules out non-physical origin of many "photons" (aka noise,
pedestals). Also, a strong pi0 peak in (C) is almost gone in (D).
I don't know of any reason why there should be a correlation
between pi- and "background" pi0 (but I can imagine a correlation
between pi- and pi0 from the same event). The problem here is
that missing mass is not very sensitive to those pi0's, and
even more so to photons in single-photon events (which are by
far the largest fraction of those "extra X" events - see the
leftmost bin on the above-mentioned plots). I'm looking now
at the possibility of applying non-kinematic cuts (i.e., TDC
time window) in order to distinguish real and fake photons and
to recover events with the fake ones.
This plot shows M(pi+pi-) mass before and
after acceptance correction for events (A) and (B). The good news is that
initially non-similar distributions look more similar after acceptance
correction. The bad news is that what looked like a strong f2(1270) peak
is much less prominent after acceptance correction. Of course, nothing
wrong with that (I don't know rho and f2 photoproduction cross sections) but...
We shall see what comes out of PWA.
Missing mass studies
To my surprise, I was puzzled for quite a while with such a simple distribution
as a missing mass spectrum. Missing mass for
events gamma p -> pi+pi-p (A) (top left) is unusually narrow while
missing mass for events gamma p -> pi+p (B) (bottom left) is
much-much-much wider (even so it peaks at the pion mass as expected).
Naively, I was expecting to see about the same missing mass resolution for
events (B) as for events (A) assuming that the only difference between
two samples is in the detection of pi-. Dissimilarity in the widths of
two distributions was attributed to a large fraction of junk in
sample (B), and I spent a lot of time trying unsuccessfully to clean it out.
Finally, I took sample (A) and artificially turned it into type (B) by
simply discarding a pi-. Surprisingly, the resulting "missing mass"
(top right plot) was anything but narrow. I was so amused that I even
did a primitive "kinematic fit" to a zero missing momentum for events (A)
(see bottom row of this plot). Logically
enough, a delta-function for MM2(gamma p -> pi+ pi- p)=0 resulted in a
delta-function for MM2(gamma p -> pi+ p)=M2(pi-). However, a barely noticeable
widening of the former resulted in a significant widening of the later.
Two implications of this result:
1. It looks like some kind of a cut on MM2(gamma p -> pi+ p) was applied
to the initial sample. We may want to relax this cut: it looks like a lot
of type (A) events were killed by this cut (see top right plot) even if
the overall missing mass for those events is almost perfect (top left).
2. Because a narrow appearance of a total missing mass distribution
does not necessarily lead to narrow distributions in other variables we may
want to consider implementing some kind of a kinematic fitter.