Top Processes
Different ways to produce top quarks, singly or in pairs.
flag
Top:all
(default = off
)
Common switch for the group of top production.
flag
Top:gg2ttbar
(default = off
)
Scatterings g g → t tbar.
Code 601.
flag
Top:qqbar2ttbar
(default = off
)
Scatterings q qbar → t tbar by gluon exchange.
Code 602.
flag
Top:qq2tq(t:W)
(default = off
)
Scatterings q q' → t q'' by t-channel exchange
of a W^+- boson.
Code 603.
flag
Top:ffbar2ttbar(s:gmZ)
(default = off
)
Scatterings f fbar → t tbar by s-channel exchange
of a gamma^*/Z^0 boson.
Code 604.
flag
Top:ffbar2tqbar(s:W)
(default = off
)
Scatterings f fbar' → t q'' by s-channel exchange
of a W^+- boson.
Code 605.
flag
Top:gmgm2ttbar
(default = off
)
Scatterings gamma gamma → t tbar.
Code 606.
flag
Top:ggm2ttbar
(default = off
)
Scatterings g gamma → t tbar.
Code 607 when g gamma → t tbar and 617
when gamma g → t tbar.
By default top always decays to a W and a down-type quark.
It is possible to switch on the t → H+ b decay mode.
Note that its partial width is calculated using the tan(beta)
value stored in HiggsHchg:tanBeta
, so that it can be used
without having to read in a SUSY parameter file. For the H+ to
decay also Higgs:useBSM = on
is necessary.
Threshold enhancements
In the article "On the threshold behaviour of heavy top production",
[Fad90], cross section enhancements in the threshold region
were discussed. Recently both CMS and ATLAS have found signals for such
enhancements. The old equations, partly but not fully available in PYTHIA 6,
have therefore now been reimplemented in full. The above-threshold
enhancements are straightforward to implement, but the below-threshold
"toponium" are less transparent, and different scenarios are explored.
Relevant code is implemented as a new class TopThreshold
in SigmaQCD.h/.cc
, which is accessed by the internal
gg → ttbar and qqbar → ttbar classes
in the same files. The implemented scenarios and free parameters
within them are as follows.
mode
TopThreshold:model
(default = 0
; minimum = 0
; maximum = 4
)
The choice of threshold behaviour for the g g → t tbar
and q qbar → t tbar processes.
option
0 : no modifications to threshold, i.e. pure Born
(leading order) matrix elements.
option
1 : the simple Coulomb enhancement, which only works
above the threshold, E = mHat(t + tbar) - m(t) - m(tbar) > 0.
option
2 : the Green's function correction in the threshold
region, for the positive part only, E > 0. At larger
E it transitions to the simple Coulomb enhancement, see
width
below. The reason is that the Green's function
is only valid in the threshold region, and diverges above it.
option
3 : the below-threshold part of the Green's function,
E < 0 is mirrored into the positive region,
E' = -E > 0. This negative part is damped-out for
small E, see width
below. Note that subset of
events generated with options 2 and 3 can be combined to a complete
scenario.
option
4 : the Green's function in the whole threshold region,
both E > 0 and E < 0, with transition to
Coulomb above and damped-out below (see width
below).
The procedure is to first pick m(t) and m(tbar)
by Breit-Wigners, and then pick an mHat distributed all
the way down to m(t) + m(tbar) - 2 * width. If then
E = mHat - m(t) - m(tbar) > 0 everything works as in
option 2. If not, then a new m'(t) < m(t) and a new
m'(tbar) < m(tbar) are picked according to Breit-Wigners
under the requirement that E' = mHat - m'(t) - m'(tbar) > 0.
Finally the event is accepted or rejected according to the naive
cross section reweighted to the Green's function value.
This handling of the E < 0 part is more realistic than the
previous options, and the best bet, is but not perfect.
parm
TopThreshold:width
(default = 10.
; minimum = 5.
; maximum = 20.
)
the Green's function, when used, is assumed valid in the threshold
region [-width, +width]. Above threshold, in the region
[width, 2 * width], it linearly transitions to the Coulomb
expression. Below threshold, in the region [-2 * width, -width],
it is linearly damped to zero.
mode
TopThreshold:alphasOrder
(default = 2
; minimum = 0
; maximum = 2
)
the order of the running of the alpha_strong, used for the
top threshold factors (and nowhere else).
option
0 : no running.
option
1 : first-order running.
option
2 : second-order running.
parm
TopThreshold:alphasValue
(default = 0.118
; minimum = 0.10
; maximum = 0.25
)
the alpha_strong value at scale M_Z^2, that then runs
according to the order defined above.
parm
TopThreshold:ggSingletFrac
(default = 0.28571
; minimum = 0.
; maximum = 1.
)
in the g g → t tbar process, colour factors gives 2/7
singlet and the rest octet, but dynamics might modify this.
parm
TopThreshold:qqSingletFrac
(default = 0.
; minimum = 0.
; maximum = 1.
)
in the q qbar → t tbar process colour arguments gives
all octet and no singlet, but again modifications are possible.
Note 1: Model 4, for E < 0 redefines the
t and tbar mass values in order to achieve a new
E' > 0. In order still to have access to the original
quantities, before the new masses were selected, the following
quantities are saved and retrievable:
pythia.info.toponiumE
the original threshold energy
E, which may have either sign;
pythia.info.toponiumm3, pythia.info.toponiumm4
the two
original t and tbar masses, which are larger than the
m' ones found in the event record.
Note 2: When generating only the below-threshold region, which
is nonzero only in a limited energy/mass range, the initialization may
miss to sample this range and conclude that the cross section vanishes
everywhere. Therefore we want to set a nonvanishing smallest value during
initialization, but not during event generation. To this end a new method
pythia.info.getInInit()
returns true
in
pythia.init()
and false
in
pythia.next()
.
Note 3: Four main program explore the usage of this code:
main368.cc
plots the shape of the pure singlet and octet
enhancements;
main369.cc
and main370.cc
histogram
a few quantities for many different scenarios, to allow direct comparisons,
with code for parallelization using either OpenMP or
PythiaParallel
, respectively;
main371.cc
histograms a wider set of quantities, but only
for one model at a time, which can be chosen among a smaller selection.