Chair marva and we move to the corresponding CMS. Talk by Joe swan. Tomas
will skirt.
Okay, can you see the slides?
Yeah, but not in full screen.
Is that better? Yeah. Thanks. Go ahead.
Okay, so, yeah, I'm on your show, Thomas wolski. I'm going to give the CMS side of
the updates on the recent TD x measurements from CMS. Okay, so I won't go
into this again because there's been a very nice explanation and my math in the
previous presentation, but as you know, the dy dx is an important measurement
because it provides us with this direct proof of the topic our coupling due to the
top mass and and the fact that he Couples coupling is predicted to be proportional
to the fermion mass. So the teacher Higgs searches from the CMS side several
analyses, each targeting a specific x decay channel I performed and these these
analyses combine several all possible top key loads. And due to the DX production
having a relatively low cross section, we in order to observe the process we
required to combine several multiple multiple funnel states and these in CMS,
these funnel states 32 x two gamma gamma t x two BB, d dS multi Platinum TT zz for
lead time and you can see the the relative branching fractions for each of these
things that came out on the right hand side. Now, this led to okay this is now a
fairly old results, but this led to the first observation of the dy dx process on
the fourth of June 2018 by the CMS collaboration and the tdsb Combination
here combined 42 eggs analyses with seven, eight TV and 13 TV data sets and led to
signal an extraction of signal strength parameter 1.26 plus 0.31 and minus 0.26
which gave us 5.2 sigma and which was enough to declare
the clarification declare an observation
and you can see on the bottom left hand side of the slide the the major
uncertainties associated with this measurement. So, these are coming from the
signal theory uncertainty on the increase of tt externalization for the backgrounds
This is for the major uncertainties for theory from from theory come from the TTB
BCC predictions and ttv predictions are these the dominant backgrounds in the TT
TT Exmouth lepton analysis and then experimentally we struggle with the left
on trigger identification and isolation efficiency uncertainty and then the Miss
identify left on predictions. So, today I'm just going to highlight the the recent
updates since this combination, so this is the GTX multi lepton analysis that he
takes BP and the TT x gamma gamma. And you can see that for the multi taps on MDB
slightly older results using a combination of 2016 and 2017 datasets, whereas the GTX
gamma gamma studies are really recent result pretty nice result using a full on
two data set. So, from the TT Higgs Morgan Epson site we target the Higgs boson
decaying to a pair of vector bosons and Tao leptons, you can see the signal on the
Fineman diagram or an example of a signal final diagram on the top right. And the
benefits of this channel is relatively clean. However, the Higgs reconstruction
is still quite difficult. The backgrounds for the analysis are divided into
irreducible, irreducible backgrounds. Now the irreducible backgrounds are largely
from TTC entities, which are shown on the right hand side in the middle in the two
example, Fineman diagrams, and then the reducible backgrounds, mostly from events
where at least one reconstructed title On is not due to a prompt. So, these these
are the fake leptons and then other Higgs production modes in this in this analysis
are treated as backgrounds and set the standard model expectation. And on the
bottom of the slide you can just see some sort of schematics of sources of fake up
from so typically coming from the semi electronic the hadron okay photon
conversions are all miss identified like yes so on slide seven and so, just to
mention here that what we call tight leptons are typically defined by what we
have this specially trained leptons DDT. So if you look at the little cartoon on
the top right, you essentially just have this PDT that's used to describe
discriminate between prompt and fake leptons. And then then we define a count
on this BDT that defines our types when we compare leptons and then we have also
favorable leptons. So anti leptons are used to to define the event categories as
shown on the bottom right hand side of the slide. And then the fake leptons to these
fuckable subcategory used to define the fake background. So, the reducible fake
flips backgrounds estimate from data in this analysis using a fake factor method
whereas the irreducible background processes and model using Monte Carlo and
then in order to make sure that we don't have any overlap between our Monte Carlo
Monte Carlo for instance and any and what we actually use to model our fake
background we have this general Rico geometric matching of leptons in the
simulation. And then so as I mentioned events are categorized according to the
number of and the sign of the of the leptons. And then we also have these
additional btw TTC and divers on image control regions to constrain our
background processes. For the so in each category of BDT signal versus background
is defined who is trained, except from the four laps on channel where the will
because the, the channel statistically limited is difficult to train an MBA here.
So the issue is a cutting count. And then the signal strength parameter is extracted
from a been maximum likelihood fit of the discriminants data, where no prior
assumptions are made on any of the TT W, WWE or TTC backgrounds. For the observed
signal strength in the 2017 data set we see 0.75 plus 0.46 minus 0.43 giving an
observed significance of 1.7 sigma. And then for the combined one this is combined
with the 2016 results that we see a signal strength parameter of 0.96 plus 0.34 and
minus 0.31 which gives us evidence of this process at 3.2 sequence significance
and just
a nice comparison with the previous speakers.
tGwU rate modifiers and TTC rate modifiers as well and we have the 2017 only for this
and we see a rate modifier of 1.42 on the TT w process and rate modifiers 1.69 on
the TTC process. Now, on the bottom right hand side of the slide, you can see also
that the theoretical sources of uncertainty are quite large in this
analysis, and also the uncertainty on the perfect background yield is quite dominant
for the TT x to be B analysis, and this channel benefits from a large project
branching fraction suffers from a huge TT bar reducible TT plus BB or at least one B
background and also QC D mochi a background in the fully hadron channel.
And then the combinatorics of the final state is very difficult. So, there's no
unambiguous way still to reconstruct the last speaker of the Higgs. And the
baseline selection is highlighted here in the table. But you have the three channels
basically fully hadron XM electronic and electronic which is essentially defined by
the number of leptons in the events. And then similarly, well sorry, in the in the
fully hydronic channel. There's a requirement on this clock, glue and
likelihood ratio Which is used to define to identify lights against your objects.
And you can see that there's additional control regions in order to constrain the
QC D background. The analysis is rather complicated. So, you have the stimulus on
a candlelit follicle, you have tronic channels or sub cat or further categorized
according to the number of jets and the jets and then in the semi electronic
channel and you have a detail on a multi classification deep neural network which
is which has six output nodes each node targeting either the signal process or one
of the TT plus jets backgrounds. In the dialect on channel you then have the the
up BDT which is trained, which is just a binary BDT in this case, which is then
used as the input to the funnel fit and then the fully hydronic challenge you have
the matrix element method for for this and then the sorry the the signal strength
parameter is extracted from above maximum likelihood fit to data and this channel
using the combined 2016 2017 results season observed significant significant
3.9 sigma and expected to 3.5 sigma extracting a signal strength parameter of
1.15 plus 0.32 and minus 0.29. And this large uncertainties from the normalization
of the dominant backgrounds being the TT ball plus jets backgrounds and the TT x
renormalization factorization scale. Now on to the final TD channel So, 32 x to
gamma gamma this analysis suffers from a small branch infraction but has a very
clean signal which means you can reconstruct the dye photon invariant mass
with excellent precision. The dy dx signal forms this narrow peak on the lifeboat on
straight and narrow divert on very mass peak on the smoothly falling continuing
background. And the main backgrounds are really from non resident die photon
production and also from non TT Higgs Higgs production. So, the analysis device
that is divided up into two channels electronic and hydronic category defined
by the number of leptons in the event and they also have a different slightly
different jet requirements, but both are requiring a dye photon event with photon
and very mass between 100 and 180 gV there's a dedicated binary BDT. So, the so
called BD background BDT which is used to find the signal regions as you can see on
the right hand side here and for the hydronic electronic channels. So, for this
the signal background are mostly modeled using Monte Carlo and this and this is
what's used for the training and the the backgrounds are either gamma plus yes
gamma gamma plus Yes, TT plus Yes, TT plus gamma t plus gamma gamma and v plus gamma
boots, because the Monte Carlo struggles to model this gamma plus jets background
process very well in the hydronic channel. This process is derived from data, the
categories then the category boundaries are for the signal strength and The RCP
measurement shown in the thin and thick dashed lines you can see on the right hand
side. So, you have hydronic one through four and then the CP half degrees on the
left hand block and on the right hand side you have the same to the electronic
channel. And it just worth noting that the gray region on the plots has basically
shaded out because the events are used in the analysis.
Then finally, a maximum likelihood fit is performed on the divert in the very mass
distribution. And this is used to extract the cross sections on the branching ratio
and the signal strength. There's in the likelihood fits the dy dx signal is
parameterize using this double sided crystal ball same as using an additional
Gaussian function. And then the background is estimated by fitting various functional
forms to the dye photon mass distribution sideband read sideband data and this is
the functional form is derived using discrete profiling method. The dominant
theory uncertainty This channel is around 8%. That's coming from the GD x cross
section and the dominant experimental uncertainties around 260 Coming from be
tagging photo ID jazz jr on January scale to any resolution and under Lumosity. You
can see the extracted value for the cross section diamond branching ratio of 1.56
plus 0.34 minus 0.32 observed and then expected is 1.13 which gives us a sigma
parameter of 1.38 plus 0.36 minus 0.29. And this provides us with the the observed
significance of 6.6 sigma So, this is an observation and you can see here on the
top right hand plot the nice boyfriend on a very mass distribution and then you can
see the in the inlay the likelihood scan, not likely and scan you can see at zero
you got this nice well above Six Sigma, I'm crossing for the ECP study. So, the
three level topic our coupling and SCP structure also tested in this analysis
with us CP structure the TT x amplitude can be parameterized using this equation
here, where you can see capital T and Kappa till the T being the CP even CP or
decal couplings and in the Standard Model set to their respective values of one and
zero and then the CP structure is measured using this FCP htt variable where FCP Hct
being equal to one is the pure pseudo scaler CP odd mode model of the CP
structure and equal to zero is the pure CP even CP structure and then you also have
provided you with like sort of the intermediary model. Then in this the CP
analysis an additional BDT is trained for distinguishing CP even in the CP od
contributions whether it's PDT output is meant to represent this b d zero
observable and then a simultaneous So, essentially you have this categorization
according to the background etc and then you have a further categorization
according to the steezy urban these dizzy ribbons So, you can see in the bottom
right hand side you have the various bins in the prompt. A simultaneous fit to the
dye photon very mass spectrum is done with the signal strength parameter and the FCP
TT x unconstrained and the analysis is really statistically dominated here. And
yeah worth noting that the couplings to other particles is constrained to the
Standard Model values and the cavities trying to be positive. So, the observed
value of the FCP TT Higgs is equal to zero plus a minus 0.33 which gives us 95%
confidence level limit on the fcpa tt x to be less than 0.67. And this allows us to
exclude it 3.2 sigma the pure CDO scalar model
so I can finally summarize on slide 16.
Since the observation of the tedious process by both CMS and Atlas, the most
has really been targeting these single channel observations of TT eggs. I've
presented here the first single channel observation of the TT x process using the
CMS detector with a significance of 6.6 sigma observed and furthermore, the tree
level topic outcroppings and CP structure has been tested the pure pseudo scale
model of the CP structure and the htt coupling, FCP was TT x equals one is
excluded the 3.2 sigma level. The most recent publication from the TT is multi
lepton and BB channels have been using the 2016 and 2017 datasets combined and
reported and they both reported evidence in the individual channels. So the outlook
at the moment it seems to be that basically we're expecting some some
exciting updates from the multi lebreton and BB teams using the full one two
datasets. And then I guess in run three we'll start to look look towards
differential measurements in SPSS and things like this
Thank you very much. Thanks a lot. I already see 200. Laura, let me see you
come talk now.
Can you hear me? Yeah. Hello. Hi, Josh. I have a question from the
simple one. One thing that
I was wondering about is just this measurement
it seems to extract from
studying the background for teaching, how do they compare with genetic variation so
that we have heard about top number one is this. Are they I mean, do they? Are they
in agreement? So you see an excess on both sides or not? So I don't think Sorry,
Laura a little bit, but I think I know what you're asking. You're asking about
the compatibility between the direct measurement of the TV and the and
yeah, yeah. Yeah.
So typically, yes. The TT TT TT z tend to come out in both the direct measurements
and the measurements that we saw these on. It's worth noting These aren't cross
section measurements, right? these these are, these are roadmap rate modified
measurements, so you can't do a direct comparison. The uncertainty is quite large
on these as well. But yeah, in general, the ttv measurements and the teeth and the
extraction of the rate modifiers tend to be above above the standard predicted
values.
In both cases, both cases Yeah. Okay. Thank you.
Yeah, I see more questions. Fabio.
I describe your maturity. Thanks for the nice talk. Well, maybe should come in and
request on the show. comment is that of course was assess results on CP
interaction topics but was shown in another session. So I don't know how these
sessions have been organized. But the question is on slide 10, when you show the
results, I mean, they did the 2016 2017 results are quite far apart. Of course, if
you take the total uncertainty, they are reasonably in agreement, but this is
dominated by the systematic says must be somewhat correlated. So did you check the
compatibility between the results of the two year
take into account potential correlation with this system? it?
Yeah, I think this I think this was indeed checks on but this is essentially a reload
of the 2016 analysis, I think. So there's been no real optimization. I think they
were combined at the data card level. I mean, one of the analysts might be able to
correct me here. But there there would have been updates to the, to the analysis
in the 2017 which I think included this dnn in some electronic journal but I mean,
I leave that
to one of the analysts is if see if they actually check
any further.
So, okay, Kirsten has a question. And
yes, I have a question on slide age for the th movie leptons giving the rate
modifiers for TT W and TTC for 2017. I don't know if that means that you're have
a different fit model for the 2016 since you're not giving these and if so, how do
you then combine the two years given that these icons are really important?
So these rate modifiers were not floated freely in the In the 2000, and sorry,
2016 2017 final result, what we do is we do a check in, what we did is we did a
check on the 2017 data set in order to extract just the 2017 signal strength and
then the rate modifies only in the 2017 result.
So this was this was kind of done
after the after the fact, if you see what I mean. So, the 2000 so the 2000 there's a
final result with the 2007 16,017. They're not floating but they've done floated in
2017 only fit in order to check the values. So,
maybe I can figure a chance to comment on this actually in the in the combine the 16
Plus, I mean, we have both numbers. So, we can follow up maybe in a further
discussion session about exactly all the details.
Okay, so last question from Lisa.
Yeah, I have two comments. First to Lauer's question about modifiers that come
out higher
than expected in CMS. They come out higher in Atlas two, we actually had special
comparison during the Elysee top working group meeting. So it's quite consistent.
Maybe the magnitude is a bit different. However, the trend is clearly the same.
And in fact, the last question about floating modifiers I thought it since I
was reading this paper 2016 carefully, again, starting with these modifiers I
think 2016 paper has two results. One is fixed t Tw and another was floating to Tw.
And floating t w has smaller significance because if you float it in w it goes up
and the signal comes out a bit smaller. So that one that option was used for the, for
the, for the combination. No.
Yeah, yeah. So I think with the 2016 2017 data the both workloads in the India
Exactly, yes,
yes, I come from that and the numbers are in the available
documentation in the paper. Yeah.
Yeah. Okay,
so I think we should really move on otherwise, we will be late for the plenary
session.
I see I still see a hand from Laura. Is it urgent?
Or is it just okay, no, sorry. Left. Tomer sorry okay.