Okay, so I will just give you a brief report on the most recent multiple,
multiple measurements done by CMS experiment. myself with that, just a very
brief introduction. Before I go into more details of the two most recent multi
button analyses performed by CMS, which are the W plus W minus and they try both
and analysis, I will not talk about vector scattering in this talk. So, if you're
interested in that, you can follow Mario talk on Friday afternoon. So, just a brief
introduction and motivation while it's important to perform multiple
measurements, while the normal structure of the standard model allows for
interrupts telling directionality vector by zone, which can be principal triple and
courting any number of zones to connect it to the vertex and a procedural measurement
of the strength of this coupling is You put on tests of the electric sector from
the model itself. Moreover, as we already are in the previous talks, the indirect
sales for new features can be performed with these analogies because if we see a
difference between the predicted cabling and what we measure which which which we
call anomalous coupling, could hint to the prevalence of new physics. Another
important motivation to do these analogies, of course, that many multiples
on prophecies, actually backgrounds in the study of new physics, or for example, in
exit searches how these measurements are very challenging because as you can see
from the blog, the cross section four is processing very low, generally smaller
than peak about what to say. And so this is why we need to use the full power of
the full CMS atlases around two statistics, which is about 114% of them.
These are complex final state, because we have a higher high particle multiplicity.
And as I said before, these are high precision measurements. So we need to have
a good control on detector effects, kill factors and so on to the precision
measurements that we really want to the basic idea of all the analysis is to
measure the critical section, and hopefully find in our significant seven
evidence for an observation of prophecies that have never been mentioned before. So,
I will now talk about the first one to focus on which is the W plus W minus
analysis which has been published by CMS experiments using the data collected in
2016. So disease, an important background source in searches for new particles but
also, for example, indicating to WW. The production comes mainly from tiki bar or
intrusion if you consider higher order interactions. And this analysis has been
performed by CMS following two different approaches. One which is the sequential
cap one, which is let's say the usual way to do the analysis, and the other one that
exploits a random forest classifier and I will show you why these two approaches
somewhat somewhat complimentary and why they can be different results. In the
sequential exceptionalities. The selection is pretty much the standard one. He wants
to opposite charges related electronic work since you have to do both on the cane
and you want a trunk does not make a big transformation coming from the neutrinos.
The two main backgrounds come from Julian and teba are single top events. And as
usual, the single topic toolbar can be reduced by providing a beachhead laser
lights will Ditalion background and will be used to discriminate between signal and
background. As I said before, the analysis used is also another approach which is the
one of the run the first classifier. And random forest classifier is basically an
aggregate of boosting decision trees which each percentage entry does not use the
whole set of kinematic parameters, but a subset of them. And in this way the
overall results that it is you can get from all the trees together mitigates the
problem of overfitting. You can every single BDT
as before, it would mean backgrounds. Jolyon and tiki bar to run the first
classifier and been built for this analysis. And as you can see from the
blocks on the bottom left in the single region, which is the one in the orange box
So, you have a very high contribution from WWF signal and a very low contribution of
the Tiki Bar and this is after applying both the score of the random forest
classifier for the Julian and for the top backgrounds. It has been shown that the
random forest classifier analysis as a signal efficiency impurity higher is
compared to the center frequency counts anomalies. The backgrounds are the Tiki
Bar, the tread yacht and also the non prom events which are basically from these
analogies w plus Jessica and when one where one jet is reconstructed as electron
will not go into high detail, but basically for all of these contour contour
regions built in both the sequential account or random forest and these
backgrounds are obtained by normalizing the contribution in the Conservation
before estimate what is the contribution in the signal region. This is semantics
the telephone for the sequential caption you can see on the table below the
greatest uncertainty comes from the analyzation of the backgrounds. So, this
is the most delicate part of the analysis, while there is also a contribution from
the theoretical side coming from mainly from Halo the QED calculation of
certainty, the total systematic efficient is about 6% while the statistical one is
1%. So, this is a systematic dominated analysis. So, the first measurements that
came out from this analysis is the measurement of the topic resection with
the two different methods. So, from the sequential data analysis, all the
different possible channels are studying separately and then put it all together
where you can see The signal strength are measured separately for Vito jet one jet
sync same flavor or differently or channels and all the signal strength and
in agreement with each other with one face or with the Standard Model prediction and
this proves that there was a good consistency of the model and the overall
cross section is in agreement with the theoretical prediction. random forest
classifier is more sensitive to their weighting of the debug on spectrum tables
in terms of momentum spectrum that is used to take into account higher the QC D
calculation. And as a result, these sensitivity to this correction is what you
can see in the plot, which is that random forest classifier as a higher efficiency
at the low values of the momentum offers a voting system and this means that it is
more sensitive to Jessie banks and this is why the devalue the central value of the
measured cross sectionally leaking the differential prior to the sequential
Katelyn, it has taken to be taken into account that also this radical uncertainty
entire situation for the same reason for these analogies, also the new show and
differentiate perfection and be measured and these numbers are just there for
reference. And as you can see from the prop on the top right, there is a very
good agreement. For example, this is differential perfection in the dialect in
math, there is a very good agreement between data and simulation. And the
random forest classifier allows a lot over to the measurement of just multiplicity.
And it is very important because it's a probe of theoretical calculation that
ultimately go into the event generator. And so it's important to know how the just
multiplicity is precisely and as you can see from the block on the bottom left Also
you're going to live by the computer simulation and the data and the lasting
results with analysis also, believe it or not most coupling have been measured. And
as you can see from the block the highest sensitivity is the highest value of the
time with myself today electrons and
limits on triple gauge Captain are measured for the values because at the one
presented on the Fineman diagram, and the plus on the left represent a scam when
you, Barney, just one parameter or capital of them together. And the table on the
bottom presented the results obtained by this analysis for the limits on the
anomalous capital. We talked about about the other analysis. That is the most
recent one we published, which is the tribalism analysis that is done with the
fool around to that effect. The goal of these analogies was to measure both the
inclusive tribalism production protection and the the single line for the all the
different processes. So, types mainly, yes, thank you. Mainly There are of
course, all the possible combination of W's and D and the w w w production has
been divided into two channels depending on the chain. So if two of the W SDK
electronically one and radically or all three of them decay electronically, and
for the flexion. The main backgrounds come from what is called the last lesson. So
three lesson processes, where one lesson is not ever constructed for exception or
efficiency problems, and the non prompter background that I already described, while
on the processes where at least one debit spread in the background is lower, because
it's possible to constrain The simpler approach to electron mass within the value
of the set mass and basically on the WWE channel that main backbone comes from that
production while on the other two channels the backs on each individual even though
the cross section and rushing ratio for these processes are very, very low. So, as
I said before, you know complex from background is one of the main sources and
has been estimated from data to boosting decision trees have been used to better
discriminate between signal and background. And for example, in www and
actually this one was a decision tree is used to enhance the discrimination between
signal and non from background and one for all the other backgrounds. And you can see
the result of the BDP score on the plot on the bottom left you can see that in the
signal region, there is a higher signal to noise ratio was for the full electron
factory, the main background contribution comes from the dead end. Of course
background especially in the case where it's well known that electrons are
opposite side fine. And as you can see from the plot of the transistor, the
mission transfers momentum. This is a good viable where place a cap to get a higher
sensitivity to the signal. They may systematic uncertainty as for the previous
analysis come from the background estimation and the limited statistics in
the control region estimation. This is the results of the analysis but there's a lot
of information. But basically this is these are all the categories for all the
different channels and the decay and the decay mode or all of them. And as you can
see, there are some channels that have a very high significance that is The plot in
the middle the brownish one. And as I said before, the main background for the www
channel comes from the last lesson, while for the WWE that comes from the
background, you can see that there are many channels where the sensitivity is
very high to the presence of these presence of these processes. So, these are
the results of this analysis, all the production as we mentioned, and you can
see them on the table on the left and so, both the
signal cross section and the big red one, and they are in agreement with the
Standard Model prediction. And you can see on the table on the bottom right, what is
the significance of obtaining with the two different methods for getting counts and
the boosting decision tree. And you see that usually that was the decision to
hire. I think music as compared to the cutting count one and another thing The
test to be noticed is that the sensitivity for a company www Chanel is much higher
when using the full run to that effect, it has been done for this analysis compared
to the 2016 only data Finally, going to add slightly older analysis which is the
www analysis potential statistics 16 with turning 16 data Sorry, I wanted to include
this because besides the limits on almost cabling, it started also the craving of a
possible action like political production in associate in association with the W
boson, which is another thing that can be done in multiple alizee cities, which is
test precise models. And as you can see from the Brazilian plot on the bottom
right, these production the progress of this particular has been included up to
480 gb approximately. So, this is my summary. With the LLC run to data
properties are accessible even though they are rayar the perfection of the measure
for many processes, not just the two represented here, but for many others, and
the limits on the animal's capital is becoming more and more stringent. Also, in
the better buttons capturing processes, some efficient models can be tested. And
also another thing is that information of prophecy that critical level, for example,
they want to prevent obesity can be extracted from the internet. Thank you.
Thank you, Alison, for this very
concise summary. So we have time for some questions.
Are the question on page 11?
Yes,
so it's not your villain talking. So here's what you have on the top, top left,
you have the signature where you have to send sign leptons and then To neutrino to
jets. So actually this final status exactly vector wasn't catalysts inside. So
I'm wondering how do you track it echoes and scattering contributions? Because in
principle you cannot distinguish an experiment. So they also contributed to
the same order. Yes. Do you treat a signal or background or you checks it is
irrelevant into space space.
There are some cats that did not report here which are that the nGj mark is
required to be below 500 gV. And data preparation is more than 2.5 which is
basically the opposite of what you do for a vector both an analysis has been done to
reduce the contributions as much as possible.
And then in this case, this is the technical
sorry. And in this first case, to set tripod on Facebook, this is the critical
channel. You could Do just consider this as background though.
I think the contribution is very small compared to the
the picture by production text
question on slide eight
don't understand correctly this plot on the right that at the low PT, random
forest gives you better performance, but at the high PT is your worst performance.
Yes, so did the IPT of the WWE system. Yes.
Yeah. Can you can you describe a bit how do you construct uncertainty when you
train BDT in one region and you apply it in another region? What are the
uncertainties? What are the contributions don't say? How do you estimate
okay.
Noticing person so I don't know if someone is connected again answer to this question
because this is beyond what I know about this.
Sorry.
Can you point me to the reference in the talk when
it's on like to leave a link to the documentation is clickable.
I can I can tell them a little bit on this if you want. I think this is, um,
shouldn't necessarily be interpreted as where the random forest is better
performing. It's simply showing which events it selects. So while the while in
principle, the random forest is trained on on inclusive events, because it tries to
find regions, but it's most sensitive to separating the WW from the teeth. bar, it
ends up selecting the lower PT events. So this is just showing where the events come
from. And then as to how the uncertainty is calculated. The biggest impact of the
uncertainty in the random forest is when you look at the PG rating of the WWE
system that comes from the higher order calculations, and its most sensitive to
this topic collectivities
Okay, if there are no more questions, let's thank Alessandra again. Thank you