So it's my pleasure today to be here, talking about new measurements that Atlas
has been working on very diligently of event shapes and jetsam structure of
circles in the wrenchy data set. So in particular, I'll be talking about three
new measurements today. One is brand new for this conference that was just approved
this week, which is a measurement of event shapes that high energy scales and their
entry datasets. And then I'll also be presenting two very recent measurements of
jet substructure one of soft trough observables, which is sort of looking at
the same observables that we've already heard about from the least actually, the
CG and RG soft dropcam chips, and then another which is looking at the lunch at
plan which has already actually been introduced in the session. I just wanted
to say at the start of my talk that all of the results that I'm going to be showing
are unfolded. We use an iterative vision unfolding technique and Atlas for details
about the way this is done for one measure, mentor and other than that I
would please refer you to the individual publications. And I also wanted to say
that all of our metric data are or will be assumed available on EPA. And that we
provide our analysis routines and rivets and some of them are already in place,
actually. So moving on to event shapes. This is something that most people here
are probably already very familiar with. Then shapes are a family of observables,
which are sensitive to event wide energy flow. There's sort of a staple of GCD
programs, they have a lot of different applications from testing Monte Carlo
generators to measuring alpha s,
to even searching for physics beyond the standard model.
And there's been a lot of
improvement both experimental and in terms of Monte Carlo estimates. Since the last
time Atlas measured event shapes and Multijet events, which was in a data set
that we collected years ago this direction. So it goes without saying that
we have much more data in the renter dataset we did in the 2010 data set, which
enables new multi differential measurements to be made. We also have
better objects. So jet reconstruction has improved substantially. In the last 10
years, we now have percent level uncertainties on high PT jets. Here I'm
showing our in situ calibration and its associated uncertainty for our new
Particle Flow jets which were used to perform this measurements. For more
details on check calibration and performance analysis, I would refer you to
Peters talk from earlier this week.
So we look at several different events shapes,
I won't have time to talk about all of them in details. thrust is one that you're
probably familiar with, which interpolates between digests like events and isotropic
events. Other event shapes are defined in terms of the linear linearized scarcity
tensor, which is shown sort of the big equation in the middle of the slide.
Or
so that would be the sphericity and the clarity, which tells you how sort of
isotropic or planar events that
if you take its transverse projection
Then you can also look at other event shapes like the transverse sphericity, or
A, C and D, which are event shapes related to three unfortunate events. To illustrate
this, you could sort of think about the two events that I show on the right side
of the slide here. With this a five jet event with small thrust and small
transfers. sphericity is that this is a very digest like a pencil like event, or
the three jet event below it, which has large thrust, large transfer sphericity.
So this is a more isotropic distribution of energy. All of these measurements are
triple differential in terms of the ht, so the energy scale, the jet multiplicity and
the event chips themselves. So here
I'm showing one,
which is the rarity, so this interpolates between planar and less planar, more
isotropic events in the ht band from one to 1.5 T. In the middle of the slide, you
can see these distributions. These are the ratios for different numbers of jets
between the measured data And several different Monte Carlo generators which
we're comparing to. So you kind of have to stare at these plots for a long time. But
something starts to become apparent. There's a lot of information here. So in
particular, this event shape seems to be sensitive to the potential or algorithm
that's being studied. So here you can see a comparison of herb extended and run with
either an Angular ordered or a dipole shower and you can see for instance, in
the three jet in the purple in the green lines grow quite far apart from each other
as you move towards less planar arrangements of energy. pithy attempts to
describe the planar events quite well. Well, AMC anello integrates with 50 after
the proton shower also performs quite well in the low jet fins here and that low ht
This measure is actually dominated by modeling uncertainties, which is sort of
interesting to see.
If you look at other
events shape, so here, this is the transfers trust. So penciled like events
are on the left side of the plot isotropic ones on the right side,
you can see some similar
trends.
But the systematic uncertainties in particular here are very different. So
here we're dominated by the jet uncertainties, which is maybe what you
would expect for a measurement of event shapes. But this only happens in the most
extreme regions that face space that are probed by this measurements. In terms of
observations about the measurement of transfers, trust pythia tends to do quite
well in direct light topology, which is what you would probably expect. The
relative cross sections as a function of the jet multiplicity tend to vary quite a
bit for the different generators. So you can see that her weight overestimates how
many events there are in the three jet then and underestimates in the six jet
spin and Sherpa sort of shows the opposite trend here. I'm going to move on to talk
about our substructure measurements, starting with our mission and stop shop
observables. So before we can actually compare jet substructure theory we have to
do a little bit of work. Soft drop has already been introduced. So I won't spend
a lot of time talking about the algorithm itself. But just a little refresher for
those connected. This is an auntie Katie jet.
You can tell because the heart energy clusters have been
sort of associated with each other first and then stuff things have been clustered
into those. If we want to perform subtract them into this chat, what we have to do
first is re cluster the constituents of the jet using the Cambridge jaken
algorithm, which gives you an Angular order. And then we can apply the soft drop
algorithm moving from the widest angles ambition, so this would be following the
nodes on the graph moving from the left to the right. at each step, we can check
whether the softer subjects satisfies the soft drop condition, which you can see
pictured on the slide. This depends on two parameters set cap and beta. Beta in
particular is important for the measurement that we'll be presenting.
higher values of beta mean that we grew less, so beta zero is destroyed. just
screaming that we'll be talking about. When a smiting satisfies a softer
condition, we stopped. And this is the grim jet that we can actually measure the
properties of and compare them to analytical predictions. So we tried to
fully characterize this splitting within the jet, where the originating part time
emits a large fraction of its energy which is enough to stop this after after main
procedure. So, we mentioned three observables, the massive the jet. This is
the first substructure of circle which was available with a theoretical position
above leading loads and resubmission. And it's the one where the most precise
calculations are available. We also measured the opening angle or Archie. This
is a variable where new predictions that next deleting log were available. And we
also look at the PT balance of the heart splittings, the CG observable that was
introduced in the previous talk, which is also interesting to look at. So we measure
these quantities in many different ways. I won't be able to talk about all The
results in this paper today, but we study performance of measurements made up of
clusters that the calorimeter are charged on a track jets. They're all done teaching
differentials starting at 300 gV with R equals 0.6 chips and going up. And we
studied multiple different stuff drop settings as well. So here are a comparison
of the three variables that we're looking at. between data and Monte Carlo
generators, you can see performance, broadly speaking, is quite good. Although
agreement between the different generators tends to degrade a bit when you enter
regions to face space where there are larger non perturbative effects. So places
like a spa uses a mess. But we have very asymmetric splittings in this energy
distribution. The agreement here could be a little bit better. Moving on to the next
slides. Here I compare the data to the analytical justice About the analytical
calculations that we show in the paper, the masses on the left, you can see three
different predictions are compared to the data. And the important region of this
plot is really the central region where I've highlighted with a box. So this area
is dominated by resubmission. And it's where the calculations are sort of
designed to predict most accurately at lower values at the mass number attribute
as effects become large and higher by instant master sort of dominated by fixed
earth fix. The equipment in resubmission region is quite good. And the precision
here is also quite impressive, sort of comparable between the theory and the
data. On the right side of the of the slide, I show you two values of RGB. So
this is the green jet radius or the opening angle for different values of
beta. So there's more grooving on the top and less turning on the bottom and you can
see that as we proved less, non conservative effects become larger and the
agreement between The data in the theory sort of degrades here.
Now moving on,
we can talk about more than just the hardest lifting with the ninja. And in
fact, another measurement that we've done studies the entire jet cluster in history.
So you might want to think of it a jet here as a cloud of soft glue in admissions
around a hardcore, which would be the originating parts on, you can parameterize
those submissions in terms of their relative angle and energy to correct the
check. We get this in practice by looking again at our camper, chicken cluster jet
history. And each node in the jet looking at the
software subject.
Looking at that subjects relative angle and energy to the jet core and taking this
as approximately the mission, you know, in our case, and using this to put a point in
the lunch at plane so this is a face space, which is sort of spinning these two
variables, the relative energy In the angle of the admissions within the jet. So
you can see for one jet, what the plane looks like on the right side of the slide.
So this is a set of points in this 2d space. And here we show it at a critical
level and detector level. So each node in the graph in the middle of the slide
corresponds to one point as we filled the template. Now this is what it looks like
for one jet. If you do this for 30 million jets in the red two data set that high PT,
this is what you end up with. So this picture is actually quite interesting if
you stare at it and think about GCP for a while, so I'll walk you through some of
the observations that you can make. So the way this works is that wide angle then
even splitting so towards the bottom left corner, as you move from left to right,
you look at more collinear splittings within the jet, and as you move from the
bottom to the top, you look at softer splittings within the gym.
So the bottom left corner is
Cool, this region gets populated relatively uniformly by nice perturbative
qc D emissions, which is sort of what you would expect if you think about looking at
the lead plan in terms of the leading luck prediction, and it's nicely protected from
non perturbative effects which enter the rest of the picture. The feature in the
center is actually caused by hybridization. In this region, the
probability of a jet emitting some soft radiation increases. This is mostly due to
the running of alpha s, and the fact that hadron ization happens. This region is
particularly sensitive to the choice of hybridization models and sort of soft
dynamics within the region beyond this feature, a strongly suppressing submission
so there's very little in the upper right corner of the plot. If you take a profile
through the lunch lens, so here I'm showing you a plot on the right side of
the screen. lives where we've taken a slice horizontally, then you can actually
show some other very nice features of this observable. Here you can see that
potential our models tend to differ quite a lot on the right side of the ledger plan
and Hedren ization models tend to differ quite a lot in terms of their prediction
on the right side. So these are the differences between
two photon shower algorithms on the left.
They open and close Greenmarkets and 200 ization models on the right the open and
close orange methods whatever the green markers come together, where the pattern
ization models
split apart and vice versa.
So in this way, we can talk about how this observable factor has its different
physical effects. We achieve a typical position of about 10% as a function of the
legend claim. This uncertainty is by and large dominated by MC modeling effects. No
single simulation out of the six that we compare to the unfolded data really
describes
the entire metric region perfectly.
Okay, and I've already talked about the factorization.
to really emphasize the way that the lunch at play and factor is, is different
physical effects, I have these two extra slides or two extra plots on slide 21. So,
on the left, you can see the actual 2d ratio of the lens land for the different
potential our algorithms that we have in Hurley. And you can see this region of
sensitivity in the bottom left corner where we expect these sort of harder wider
angle emissions to be. And on the right slide, on the right side of the slide, I
show the ratio of the two different Sherpa models and where the heparin ization
effects different than most disappointment to play. And you can see that these are
basically completely different regions which is really nice.
Okay, so chicken Sorry.
Hello, all fine, please go ahead. I remind you, but you have one minute left.
Ah, perfect. Okay, great. Yes. Okay, so to wrap up, there's a rich program of jets of
structure measurements being pursued by all four Elysee collaborations in Russia,
I think this is very clear, actually from watching a lot of the other talks at this
conference this week. And I think that Atlas is contributing very novel analysis
to these efforts, which is very nice to see the larger entry data set, and the
really, really nice Atlas, Jet reconstruction performance, make this time
periods, sort of the perfect time to revisit some of these old bread and butter
measurements like event shapes. We're starting to do that as well. In many
places, I've mentioned this a couple of times, we're limited by large Monte Carlo
modeling related uncertainties. So I also wanted to mention As I wrap up that we are
quite interested in cooperating with the wider community in order to try to improve
the position of our simulation, because this is limiting the position of our
measurements. So we're interested in knowing what the most helpful data we
could provide in order to facilitate that would be
okay, thanks for your attention.
Thank you much for this very interesting results. So now the wider community please
ask questions.
Can't believe there are no questions.
I okay. So let me ask I had one question, but it's more of an outsider question to
your slide 14. Okay.
Yeah, exactly. So I'm just wondering, what's the difference between the purple
on the left hand plot between the purple and the red because I suppose, and Ll is
also in the purple also includes leading others. So the only difference is the non
perturbative effects as opposed.
Okay, I'll try again. I'll turn it back. So the different predictions are provided
by very different groups actually. So they're using different methodologies, the
NLL here. I think it's actually a prediction for inclusive jets. So it is
an nl nll it is exactly what it says here. You can
see also on the right that this is an nll prediction for for RG
which is also a prediction done for inclusive jets and then we
compare it to this measurement which is actually done in digests.
Whereas the red part does include a sort of
the Kickstarter part.
I see. Okay, thanks. Yeah, I thought the fixed other part that always always
included but okay. Yes, thank you.
Yeah, I'm not an expert, but this is my understanding of the difference between
these predictions.
Any other questions?
No.
Then Thank you Mark. Again.