Okay, Can people see the slides now?
Yes, it works perfectly fine. So please go ahead.
So, so thanks for the introduction. As I said, I will be talking about probes of
perturbative qc D with vector bozos photons and Jetson Atlas. Today I'm going
to focus on two topics. The first is have a heavy flavor production in association
with z bozos, and in particular, z greater than one is greater than to be jet cross
section and differential distributions. The second is measurements of isolated
photons plus to jet production, distributions and direction fragmentation
has features like correlations. Just wanted to point out that there are
additional measurements that I'm not discussing today that will be presented in
other kcd sessions that were precision probes of jet substructure that will be
presented by Matt LeBlanc on Wednesday and yesterday You heard about z and draw em
cross sections and P t distributions. Okay, so let me get to my first topic B
production in association with C bozos. z plus b production is an important test to
determine if QC D the calculations have been done to nll accuracy. They just both
in the four and a five flavor number schemes. The five flavor scheme, by
definition uses be quirks in the PDFs that means that you have to have massive,
massive speak works in the in the hard scattering although masses are used as
part of the showering of the events. In the for flavor scheme. The B corpse come
from blue on splitting and it's possible to include massive fees. It's a benchmark
process for testing performance for MonteCarlo generators and an important
background for many bsm searches. The performance of the calculations in both
schemes and for different generators has been Compared for a large number of
different distributions, both the one and the two, tag bhujette selections, and
unfolded differential distributions are available. So let me begin just by showing
you what the fiducial cross sections look like. The selection cuts for the fiducial
region are listed on the left, I'm not going to bother to read those. Also, I
know it's a bit tomorrow, I apologize, you can see the different MonteCarlo
configurations that are used for the calculations. So as as shown, they're both
for flavor and five flavors in calculations. And the labels make are a
little difficult to understand because many of these are matched and merged
calculation. So listed on this table, not only do you see the order of the
calculation, you also see for the multi leg cases, how many legs are in the
calculation in particular, let me point out the cases of Sherpa where there's a
merging process so the lads no For small numbers of Park towns merge together with
lowest odor calculations for for more park on legs, the PDF sets used in the
calculations are also indicated here, shown on the right hand side, the cross
sections for at least one and at least to be just satisfying the fiducial catch on
the left, as you can see from this flap before flavor scheme tends to
underestimate the cross sections and the region was with at least one beach yet,
but it's consistent with the data for at least to be jets. The five flavor scheme
predict the cross cross sections reasonably well in both of these regions.
You should also note that the statistical uncertainties are quite small there the
yellow band, so the data are totally dominated by a systematic uncertainties at
this stage. Okay. Let me quickly show you a few distributions. And typically in this
talk, what I'll do is first I'll show the distributions and then I'll have A summary
slide giving some some overview of what's seen. So here you can see in the case of
at least one v jet kinematics, the distributions for the PT and the Z and for
the leading v jets that PT on the top of the distributions themselves, the cross
section as a function of the variable being measured, and on the bottom are a
number of predictions of MonteCarlo divided by data. They're just put on
several in sets, because there are too many points to show on a single insert. As
is often the case, it's difficult to reproduce the bpt as a GPT distribution,
very low up PT, that's a region where non perturbative effects and retardation are
important and in particular, regeneration matters a lot in that region.
Well, one would expect better preservative production at the high end. The overall
levels of course, just reflect the differences in the MA predicted cross
sections on the previous page of particular notes. is a case of a lead
leading BGP t, where you can see that, that the multi leg calculations, do a
reasonable job all the way out, perhaps beginning to disagree as you get to very
high PTs and then typically do better than no calculations that don't involve
matching emerging. If we move on to the region of greater than to be jets, then we
can start looking at some more interesting distributions. In particular, the
correlations between your server will show here, the correlations between the two B's
in the left and five, and in the end the rate for the invariant mass of the two
B's. The five distributions between the two B's seem to be reasonably well
reproduce the shapes are similar for all of the MonteCarlo predictions. In the case
of MBB. One can see that none of the MonteCarlo is over a good job at the
invariant mash of the bees being quite large. Okay, so just to summarize what we
see for these equal speed results. In general, the five flavors game and flow
calculations predict uncouth cluesive cross sections for both the one and the
two jet regions while the four flavor schema underestimates the values,
multiplex calculations model the region of large bpt better than the piano postpartum
shower. The sharper nll five flavor scheme with up to two port times at low anello
and up to four partitions at lowest though, to describe most of the
differential distributions within the experimental uncertainties. And all of the
generators disagree with the data at large in various massive a big bar. Now, let me
move on to the second analysis. photons was to jet production. I've shown the
examples of the Fineman diagrams from the two categories of production here. And
just to give you a little bit An introduction from photonic was to jet
production is a rich system to discuss perturbative q CD. Because we have three
particles in the final state, it's possible to look at a variety of Angular
Carlo simulations as well as P t distributions, and a particular lowest
order. There are two production mechanisms. Of course, it mlo talking
about separate production mechanisms doesn't make any sense. However, this
lowest odor structure will still reflect itself in the peaking region to the
kinematics. So those two or photons from a hard scattered self and photons from
fragmentation those are shown in a and b above. And of course, the treatment of
this fragmentation is one of the things that is traditionally made understanding
direct photon production, a difficult and specialist phenomenon. Here on the next
slide, you can see the definition of the fiducial regions Because of the trigger
the photon 80 is required to be greater than 150 gV. There is an isolation
requirement this isolation requirement is is placed on the on the truth and the
experimental data are corrected to match that isolation requirements and all the
Jets are required to be greater than 100 gV weather affinity less than 2.5. The
separation between the photon on the Jets is also good. comparisons are made with
three separate MonteCarlo predictions. The first is almost a straw man it's a it's a
patio where opinion itself is doing the hard scattering so it's a lowest odor
calculation as compared to using a lowest odor PDF. The second is emerged lowest
odor calculation from Sherpa 2.1 or the merging is done in a two to N process
where n can be between two and five part times and the final state here. PDF is C
tech 10. And the final is SharePoint 2.2 point oh, this involves an nll merging for
two or three jets, together with a lowest order emerging for three and four and the
whole thing's done in a consistent way with an nll PDF. as one would expect the
lowest order calculations have a large scale uncertainty they therefore always
displayed using a K factor to take into account the fact that the actual choice of
scale leads to larger uncertainty in the rate and the values of the K factor that
give them the best data are shown on the class. In the theory to data comparisons,
the shaded area so it shows the NLL theoretical uncertainty so that's the
uncertainty
obtained with a Sherpa and a low calculation. The uncertainty shown in the
error points include both the statistical and systematic uncertainties on the data
themselves. So just as an example, this shows the plot for all of the data for the
end of the photon. As you can see, aside from the very highest CTS, the
calculations all do a reasonable job on that. So one of the interesting things
about this measurement is the fact that that regions of direct and fragmentation
contribution are selected. Now, of course, that's only an enhancement because the mo
diagrams don't separate in that case, but you can still pick regions where one or
the other diagram dominates. The fragmentation enhance region is selected
by requiring the photon at the less than the end of the sub leading jet while the
direct enhanced region has a photon eta greater than the 80 of the of the leading
jets. The total includes both of these reagents as as well as the additional
reagents not covered by either one. So let me just show a variety of distributions
from this. This slide here, you can see the distributions for again the total
phase space, the fragmentation enriched and the direct enriched region for the ETA
of the photon and PT of the jet. Again, the three calculations are shown here. And
these single variable cases, aside from the highest at all of both versions of
Sherpa do a good job, you can already begin to see that the lowest odor pythia
perhaps not unexpectedly, does not do as well. Moving on to more interesting
distributions, shown here, the angular distributions comparing the two jets in
the event if one compares the fragmentation rich and the directed,
enriched regions you can see there are quite significant differences in shape
here. As expected that fragmentation and rich Do you see peeking in the digestive
region refer close to being at the back. That's because the photon is essentially
coming from a broom strolling off of one of the initial parkins. While in the
direct enhance region, you see much less peeking and it's enhanced, basically in a
region of smaller delta phi. Again, the two sharper calculations which are matched
and merge do a reasonable job and both of these regions and there's no real
improvement with the envelope calculation in terms of the shape, and the pythian
does a rather poor job. The bottom shows similar distributions in the case of the
delta phi between the two jets. And here you can see a delta y excuse me between
the two jets and here you can see that especially in the fragmentation region,
none of the predictions do a particularly good job at Delta. Watch I
have about three minutes left. So
the next slide shows similar distributions for the case of m j, j and m gamma j j,
the invariant mass of the two jets and the invariant mass of the of the three part
argon system. Again, for the total Face Face on the left the fragmentation and
rich region in the middle and the director and rich region on the right hand side.
Here one sees a significant discrepancies between the data and all three of the
generators in particular in the case of large m JJ, and in the case of large,
gamma and JJ none of them do a perfect job. It's interesting that the Sherpa
lowest odor merge sample appears to do the best job among the three generators. One
wouldn't necessarily expected that but, but it seems to be a fact when prepared
the data to the To the generators. And again, we see that there's a rather poor
job that's being done by Pipi on its own. Just to summarize the results on the
photon plus two jets, the measure distributions and direct and fragmentation
enhance regions exhibit features that are expected from the underlying processes.
The tree level lowest Oda Sherpa gives a good description of the shape of the data,
except at high et gamma, delta y j, j and m, gamma j, j. And of course, because it's
a lowest order calculation, a K factor needs to be applied to get agreement in
the overall rate 58 where the sub leading gender originates from part time shower
does not describe the distributions well. The mlo Sherpa describes they've
adequately both in terms of shape and normalization accepted Hi, ET gamma, delta
y j, j and m gamma j j. Although the lowest started Sherpa seems to do a
slightly better job in reproducing the shapes and theoretical uncertainties
rather than experimental ones dominate all of these measurements. So let me just
summarize with some conclusions. Recent Atlas results on jets photons and bozos
probe perturbative. qc with high QC D with high precision c greater than one v jet
and greater than two B jet kinematic distributions probe sensitivity of the
predictions to the flavor number scheme. The prompt photon was to jet measurements
test nll calculations and regions where direct and fragmentation photons dominate
the ability of the theoretical calculations and MonteCarlo generators to
model complex UCB systems in general good, but some discrepancies with the data
remain, and a wide range of observables available to tune MonteCarlo generators
and to explore regions where theoretical calculations are difficult or problematic.
And that's the end
Many things module for very nice and clear presentations. We don't have some applause
function here in zoom. But can you imagine that you have received this? And now it's
time for questions. So I will remind you that the attendees can ask questions using
this raise hand function in in zoom.
Please don't be shy.
Otherwise, the panelists and the hosts can also ask questions they can directly
unmute themselves.
So I see Tomas, so
Please go ahead. Yes.
I think I'm unmuted now. Okay. When you say, for mutation in rich and static in
rich samples in the photons, isn't there a perspective that you may actually from
from this data on, more and more refined measurements with higher statistics have
an independent measurement of the proctor for permutation functions because all that
we have now is still dating back to lead times and is not of that terrific point.
that's a that's a good question. I, I'll try and answer but if there's anyone else
from Atlas, who would like to give a more detailed answer about this, I think if one
were going to do it, it of course would have to be done within the context of one
of the MonteCarlo generators. You Because one needs to, of course use those in order
to do the unfolding. Do I have a good answer to this question? So I don't think
the issue is statistics. There's plenty of statistics here. I think the the issue is
is whether or not one could reliably model the transport translation between this
isolation cut in the data and in the MonteCarlo. And perhaps someone else from
Atlas who would like to comment in more detail whether or not they see your
prospect to do that unfolding in a in a reliable and consistent way.
I'm not especially expert or expert on this topic, but what I know is that we
have some ongoing effort to try to harmonize as much as possible this
definition between the truth level and the reconstructed level and this is is somehow
related to minimizing the modal dependence that we have when doing the unfolding and
also minimizing the sensitivity to this model dependence when doing the data to
theory comparisons later on. So I know that there's some context with the
theories to try to make some developments on this.
Yeah, I believe the hardest part will be understanding how to how to do the
unfolding. And and to what extent the unfolding is different for different
showering Monte Carlos. So one of the things that might make it more possible
than it would have been a few years ago is the fact that the MonteCarlo generators
now have many more switches to allow you to change the shower in itself. So the
ability both to change the new scale within the shower, and also to switch the
type of shower and so forth. Jacqueline in her wanting to be able to switch between
Angular and dipole or in the case of pythia to be able to switch between dire
and and standard 50 on string fragmentation well at least allow one to
understand the sensitivity to the systematic uncertainty so but certainly
the first thing I see is in the shower, how much it affects the unfolding Yeah.
Well, I can see the generator side of it if you if you really after the after
fragmentation function, then the extraction should be largely insensitive
on the on the generator that you're that you're using for the unfolding.
I would hope so. But I don't know if anyone's check that
okay.
This is more space for for for work, I guess. Yeah, I think I think you're
perfectly right. I mean, this is something that requires some careful checks in any
case. Okay, thanks. Thanks for the question. Any further questions now?
I don't see any. So you can also think about questions during the following talks
and that said, we will have another discussion at the end of the session. So
many things. Many things Marjorie again, so we move now to the