Seems like Yes, perfect. Well, we'll get going. Okay,
so, thanks for having me. I'm about to bring order from university and then fn
Baba and today I'm going to talk to you about the measurement of disability in
phase. She is in the best sci fi channel by CMS at that theme TV. So, just the one
is light of introduction. Yes is a TV relating phase arising from the
interference between the SDKs proceeding directly and through mixing to a final
state. The Standard Model prediction is very precise. Putting a fee is roughly
equal to minus twice with S which is one of the angle or one of the angle of the
unitary triangles at minus 37 million or the one answer that is below 1 million.
New freezes can change these values via new particle contributing to the DSPs
guest parking for measuring vs. vs to zip defies the golden challenge since there is
no predicted direct civilization only one evaluating phase and it's easy to
reconstruct a high signal to background ratio. Also several other interesting
observable Can you measure with the same analysis such as gamma is DMS, DMS and
lambda where lambda is an observable which measures the direct CP violation and is
expected to be one for this DK. What do we need to measure? So first, we need the CP
Angus data of the fire state which is extracted via an angle and analysis. Then
we need the excellent primary solution to see the fast USPS model solution. We need
the highly efficient flavor tagging to infer the initial Yes, flavor and of
course, as all recipients we need as much statistics as possible. As for the
candidate selection, I think it's worth to spend a bit of time on the trigger
strategy for this analysis. So for the trigger for this analysis requires our job
site to mimic candidate plus some additional Mian that can be used to target
the flavor of the BS via sibenik tonic decays over the other B in the event.
However, the requirement for the third commune lowers the rate of selected events
because any events that do not present the moon at the opposite side is rejected by
this trigger. These lower rates also allow us to not apply our displacement cap on
the job site at HFT level. In summary, this triggers improve the tagging
efficiency that is the fraction of events for which we can give a tagging inference
at the cost of reducing the number of signal events. This is a schematic and
presentation of the events were the three new selected at the three gate level are
highlighted. Do we have a black sock. As for the offline selection, we apply some
kinematic cuts at the final state particle and DPS and our mass windows to the to
intermediate license and our city cap of 70 micrometer. We use a total of 96
inverse Phantom are collected in the 2017 in 2018 at 15 Db for a number of signal
candidates around 48 49,000. I also report here the number of candidates collected by
CMS in another one and you can notice that they are quite similar even if you're five
times integrated luminosity and twice the reduction of session from the higher
center of mass energy and disease, in fact that the effect of requiring an additional
noon at target level This is an example of a candidate events. We use it for this
analysis, of course, that the almost two years ago by CMS and the free noon the
true from the job site, they want to use it for the tagging are clearly visible.
Moving on to the efficiencies, the features in selecting and reconstructing
the SDK depends on the key length, but also on the variables as we see, we see in
the next slide. So, the proper fit to the decay rate model which have not described
here, but I have a backup but we need the parameterization of the decay length
feature which is done separately by 2017 and 2018. And fitted in the city range 70
meter meter which is the selection factor up to half a centimeter with our folding
exponential time such a VHF polynomial order therefore, this procedure has been
validated by fitting the D plus two Jeep psychic lasts a lifetime in a in a
Different the taking field and each of these eight data sets is roughly
equivalent to statistics to the BS sample used to extract
fears.
As for the angular efficiency, they are evaluated again separately for the two
year c means of positivity cos CT and 50. And the efficiency function is
parameterize that we've spherical harmonics and the measure and the
polynomial up to the order of six. Moving on to the flavor tagging as you can
probably imagine from the choice of the trigger for tagging is a core piece of
these analogies. As target we use the opposite side of the moon that use the
moon charged as tagging feature. The moon sees the moon is already selected to
collaborate we have we will have a very high efficiency that's been optimized in
simulated events and calibrated in that using the plus two k plus a setback
indicates the Figaro married is the user Are One is the tech empower, which is an
efficiency rescale the with the dilution efficiency is defined as the fractional
backing events. While the dilution takes into account the mistake events here we
can define both from a staggered fashion that is the fraction of events of target
events, which carries the wrong flavor information but also mistake probability
that can be evaluated on event basis. This Moon is reconstructed selected with the
global Munich construction. These are the construction that uses information both
for form the tracker, the CMS tracker system and the mirror system. We apply
fairly loose kinematic and fully lose kinematic selection and we also have
developer discriminator for soft noon to reject the background from light our drone
mostly charged with cams in charge of buyers, their immune cells As you can see
from the table is overall quite loose for maximum efficiency if use the new charge
as tagging feature at this stage so without having to miss tag we achieve a
tagging efficiency of 50%. So, one in every two events is tagged our Miss tag of
around 30% for up bagging power of around 7% but the parameter means like
probability enhance the total tagging performance is also an input feature of
the final feature. So, our fully connected Deep Learning Network is used to
distinguish mistake with event and evaluate but haven't missed the
probability at the same time. The imperfect it can be divided into classes
mean viable such as momentum or impact parameter separation. We've the signal bs
but also called environments which are constructed using information from all the
tracks that are found to be in a corner center and around the moon direction and
these can be for example, His relation. The charge of The corn or the ratio of the
energy between of the ratio between the energy of the moons and the energy of the
goal. The dnn is constructed in such a way that the article score F is equal to the
probability of studying the events correctly. So they mistake a probability
of that given event is simply one minus these predictions prediction is validated
and calibrated in data using tagging the case. In the two the two plots that I'm
showing here at the center of the slide the art of the 2017 and 2018 calibration
plots, where in the x axis, we have the predict the mistake, we have been of the
predicted mistake aggravated by the dnn, while in the y axis we have the
measurement mistake from mistake fraction, indeed, they read the line and represent
the linear function that we use to calibrate the response of the neural
network. And as you can see from the values of the A and the parameter is
compatible with the straight line by by sector y equal x. So we have an excellent
agreement within the prediction and measurement and also like to mention that
the dnn and these linear calibration updates table to statistical fluctuation
leading to various more systematic uncertainty related to different mortality
that in performance are evaluated in the same setting in data. And they have
reported here the figure of merit in 478,000. In the parameter Mr. Mobility
does not change the tagging efficiency, which is still around 50%. What's changed
is the overall tagging power, which goes from the 7% that quoted a couple of slides
backs up to 10% and improvement of more than 40%. They also put in here in the
last line, the performance of around one even if the comparison cannot be done
directly, because the data center where these performances are valuated are
different because you're on one we will have on addition, we will have a moon
candidate for each events while in around one when the moon and electron tangle were
used, there were not a specific selection on the opposite side. Anyway, we can do a
compare his own normalizing the performance by the event rate, which leads
to a 50% high performance of these run one of these franchise tagging algorithm
overview with respect to grandma
moving on to the feet and the results. This is the fit the model that we use, I
will not describe it in it is we have the secret PDF, which contains the efficiency
function, the differential decoded PDFs and then the PDFs of the various input
variables. We have a PDF that the mode is the main background which is mainly
combinatorial. And then we have a PDF that more than the picture Background from a
big YouTube site case study that became background from lambda be to say a key
proton is estimated to be negligible for the data set of candidates that we have
selected. This slide I'm showing you the one dimensional projection of the feet for
six of the input variables we have the master the key length and its uncertainty
and the three Angular variables. While in this line, I painting you on a summary of
the systematic uncertainty that we have estimated, there are plenty that I cannot
describe it described in detail, but I have highlighted in bold the red that the
leading systematic uncertainty for the most interesting parameter and the fourth
yes, these are the modern bias which is evaluated through MonteCarlo experiment
the end angle and efficiency When we propagate the limited statistics using it
used to evaluate the deficiency functions. As for the results, I have simulated here
on the left vs is measured to be equal to minus 11 million other with statistical
uncertainties are 15 illa ns is not a conservative 10 million other these
results is in agreement with the Standard Model prediction. We have also measure
DMS, which is again an agreement with standard model prediction gamma s which is
consistent with the world average and DMS, which is consistent with a vote every
debate as like tension lambda is also consistent with not direct specialization
that is lambda equal to one. I'd also like to mention that is the first measure in
biosimilars of E m s. And that is out of these analogies are in agreement with the
one presented by CMS at the at the end and For a combined or systematic uncertainty
are considered uncorrelated in the bottom right plot, you can see the true the CS
Lambda Gamma Contoso for the 30 dV results in blue, the eight dv one in green and the
combined in one in Atlanta and the Standard Model prediction in in black, as
you can notice from the sides of these contours the new trigger strategy which
trades the number of events for lagging power space off for us in fact that even
if we have the same number of events, the uncertainty on PS is reduced by a factor
of two basically, why does not improve the gamma So, which do not make use of the
flavor information. And therefore, it is mainly driven by Statistics and since we
have the same number of events in a TV and 13 TV, you can see that in the gamma
direction. There certainly is Medical. This leads to my last couple of slides for
the slide of summary. So, the US and the UK with different DMS are measured using
40,000 can be selected at 15 dV corresponding to an integrity nosy or 96
investment in events are selected using a non displaced trigger that requires an
additional means which exploit which is exploited to be fair the flavor of the
stretch pays off in terms of lagging performance leading to a significant
reduction of the PS and certainly however the limited the number of events prevents
improvement on the gamma is the results of this analysis are combined into Windows
painted at a TV and are in agreement with the standard. As for the outlook and the
future prospects, here I have a table with the comparison with other LSE experiment
the for the best which upside kk Chairman around the fee master all of these results
are combination of round one and partial
round two results. I would also like to mention that the new D gamma is
theoretical prediction with smaller uncertainty is available the pre printed
dates, I think December 2019. And the same method is at both the 15. tv one and the
combination is in agreement with this new prediction which has almost half of the
certainty of the previous one. It's also easy to see that the DMS show some
tensions between experiment and hopefully a foreign measurement will clarify the
situation as for the CMS food plan, CMS plans to analyze the following two dates
ending a complimentary figure that they're required to displace in job site plus two
charges tracks the electron and in that charge a flavor tagging algorithm will be
used in this sample that do not have a specific selection for the opposite side
and the effective statistics. Number of candidate events times again, power is
expected to improve by a factor one and a half true with respect with these results.
And thanks for your attention.
Thank you very much, Alberto. And thank you for being on time. Other comments or
questions for the speaker? Starting from the attendees? Please remember to raise
your hand. If not, I had a quick question on the dnn usage, I see that it's
including the use of variables that are sensitive to the overall event shapes,
such as the isolation and so on. I wonder if this can introduce a potential Angular
effect on the analysis and if that can if that is taken into context we look into or
maybe it's just limited
while it's taking into account because the PT and the ADA But also the separation
with respect to the signal our input feature, so even if you do not input soul
parameters so we have some variable that take into consideration the kinematics of
the however we did not explore in detail. If the tagging depends on
on the angle of the angles on
that chord evolution will support a bias in your angular distribution in the fit,
right.
I don't see why because we input all the angles we input to every event is used for
the analysis even the impacted one so no event is discarded by the tagging. In
fact, as I said before a DMS do not use any tagging information. The tagging
information is spread Until in all if I show you the key rate model, the the
tagging inferences I lighted but this key in Switzerland only in only alpha of the
PDF, which is sensible if you go through the tables to vs. While bigamous is not
sensible. So I don't see the pattern we don't see why you should introduce an
Angular bias sees all the information always visible from the feet.
All right, what are we going to take these often in this? Yes, yes. Is there any
other question?
I have one.
Yes, please.
Okay, so it's very interesting your, your trigger strategy.
Besides very impressive result, I was I was just wondering about the efficiency.
So you it's It's clear you sacrificed statistics to have such a good target
power. How much do you sacrifice? Because I'm thinking if he you could, for example,
have another sample with the most statistics and slightly less or quite a
bit less taking power and still improve your sensitivity. Maybe not to fight us
too much but to delta mass for example. Yes, you have completely arrived. In fact
that is exactly the CMS plan for the fuller untrue analysis because we have a
trigger that as you say, that has a very high statistics and we have no position on
the opposite side. So we are more obviously sat standard t good Wi Fi
statistic and standard tanking power. And when we add these two trigger together, we
will have
benefits from our Both of them
as for the statistics that we sacrifice, I think it's a factor of five of leverage
and having trade should be a factor of five or between these two trigger. So if
you look at the tagging power, which is around the 10%, okay? Which cheese's
roughly a factor of between seven and 10 with respect to one on one, and then if
you divide by five, which is their interest, you sacrifice, you still have
a 50% higher,
bagging power with respect to run one. So, it's still Worf to deployed these three
gear to improve the tagging power, but of course, to perform the measurement at
best, let's see. Also they are there Trigger should be used. So, you would have
offered a lot of statistics. You will have an electron and a jet that that flavor
target but you will also have a very high neon tagging tagging very high efficiency,
very high performance innocent
sounds great, thanks
efforts, thermal other questions then thank the speaker again and move on to the
next door clothes