I'm sharing now let me know if you
Yes
Okay. Yes. So yeah a bit of CPM metrology but mainly focusing on VCB and metal
measurement of PCB with bs so adding a bit of strangeness into this game. So as we
very well know CGN mechanism very well established. So I'm not going to the data
here, BD Ks contribute to the measurement of angles with CP violation measurements.
We get also sides from simulation but then we got these measurement of VC V and VB
that actually have these long standing puzzle, which is the difference between
the increase even death because it's measurements that are here in the cross in
the in the plot here, eventually Measurement of up VCB. And this is the
average of the XYZ measurement. And these are in some disagreement, the level of
three C. This was just, let's say measurement for form at the factories, but
any CV entered the game a few years ago with the first measurement of VB over VCB
done with a completely different system. So, the lambda BD Ks are used here and
they bring a very independent formation with respect to what we use before. So, in
this respect, so bringing additional independent formations to the BS sector is
very promising, both for VB and VCP. For VCP, that is the focus of this talk. We
have some really potential here and these two together the indeed the theoretical
work we already heard the by Nico some of that and there are also on the market
lattice QC D collision that we can profit, some of them are already quite precise.
And we can profit those from calculation the full code spectrum for bsds new
decays. So we can use vsds new decays and yes, and to the S star indicates to
extract the first measurement of, of VCB. From from that. And, by the way, on the
last lap, this calculation of BS should be even more easy, let's say because of the
fact that the S stranger is earlier than the U and D that are the spectator cork in
the B and D is allowed to get
to have a more precise calculation principle. So, at the ECB, we have a lot
of potential on these because we produce a lot of BS medicine. And, and so which
means at the end of the day, we can store samples that comprise several hundred
thousand of the SDKs and indeed, the I will show you the first measurement of VCB
using vs yes and vsds starting in UDK is collected by any sibling. So, the first
style challenging in this kind of measurement is to know are many BSc
producing in your environment. And this is a problem because the cross section for
the production of these 1000 collider is not very precise these will bring the
largest uncertainty in your measurement of VCB. But you can overcome these by using a
normalization channel and explore it for instance a channel that is very well known
and that is very, very similar to your seat now. So in this case, we use the V
zero to Demian u and v zero to determine your DK which are re constructed in the
same data set. So we can send cross section we know in the ratio, but also we
also profit from suppression of systematics in the measurement because we
will use the same finite state for the GI. So, here you can see the very mass of the
kk five finite state and the peak of the DS that is our signal. And when you
combine these deals with immune displaces from the primary vertex, so to identify
the immune system coming from a B, yes, and the D Mew, so the D in the same body
mass of the kk pi, so, we use the cabbie Bo suppressive mode. So, we have
anonymization channel which bring less statistics than our signal This is not a
problem because we are not afraid of statistics here we are not limited by
statistic but by the external input that we have in our measurement. Mainly the
fact that we have any way to know which is the relative production of s to be zero.
So we need an input the FS over FB, the fragmentation fraction ratios, which is
known by precise measurement around the 5% and will bring a dominance of around 2.5%
on VCP. So, this is our sample that we will use. And then we have to of course,
discriminate our signal so that yes to DSP new MPs to the start menu decay from these
increases samples and since we are missing the neutrino here, we cannot close the
kinematics like in the factories and we don't have a clean beaker to identify our
signal for instant when you look at the visible mass. But anyway, you can recover
part of the of the missing neutrino with these additional valuable the carotid
mass. And so you you recover the part that is conserved along the direction of the of
the B and these allow you to have an hour pika and to discriminate the different
countries. In the increases sample, so you can see in the plot here, the bsts menu,
and that DPS, the star unions that are very well separated from the background
that are his physics background in combinatorics. Here with these a variable
that was already exploited in this paper, which was a measurement of DBS lifetime.
The third challenge in this business is the fact that without the momentum of the
B, So, because of this Miss neutrino, you cannot have the rest frame kinematics that
he will use to make a measurement of VCB because we see V is extracted as a
measurement of the differential decay rate as a function of the recalled variable w
which is the energy of dt, basically in the rest frame, but you don't have this
frame. So you can work with approximation, but here we attempt a different approach,
which was a need a buddy you know, working very well Which is Okay, anyway, we get
accustomed to Bollywood which is fully reconstructed, and it's I liquidated with
the recoil valuables. So, this body where is the transverse momentum of the D, but
transverse with respect to the B fi distance. So, in this sketch here is
reported. And you can see in the plot in MonteCarlo, that Deezer bada bada which is
fully restarted at a very good resolution, then it's highly correlated with the
record Body Body simulation, and which means that at the end, you can use these
valuable to extract all the information that you will use neater with with that
view. And, and so, which means that you can extract the full factor which are
function of value using these these valuable and so perform a measurement of
ABCD and different factors by making a differentiation measurement in the LM
curve volleyball to discriminate signal and background and differential in this
paper to determine the CB and infer factors together, because we already heard
from Nico's talk that you need them to to instructor again VCB. So, by doing a
template feed in these two dimensional plane this space at the end you can assess
these information. And in our eyes We also use some constraints from the four
different factors. So to improve precision from these large disk cases, because
correlation that there was mentioning at the beginning, and of course, as I said,
you need some reference measurement, which is then the measurement of the ease of the
Bz v zero the case that I presented the missing sample. So through a similar
feature and another external input the are the potential ratio of course of your ddk
is of the zero maximum use as the case is as a reference and Fs over D, which are
delimitation at the end of this method. So, this is the result of defeat. So, you
can see the production of defeat in the two dimension the karate master and the
bird and you can see the good value of these feet
and at the end, we we are measuring for factors a serie BCD into form factor
parameterization because there was some idea some some years ago that by
reanalyzing Bell data that the discrepancy of of the inquisitive and ask is
measurement. Cool depends on how on how you put him criteria for factor. So, we
tested the measurements with different parameterization This is the more general
model as you can add the GL and nikora dimension it and this is the value of PCB
that we extracted from by by parameterizing the foot factor using the
bgl model. So is a measurement of VCD which is with a good procedure and but at
the end as you can see here we disentangled uncertainty contribution, so
static statistical only the systematic as usual but then you have these external
input contribution which are the dominant contribution and this is the result of
defeater when you do the measurement using the CLM parameterization. And at the end
of the day, there is no big difference in VCB. So even when you can see there the
correlation between these two numbers, they are perfect agreement. So we know
difference. Now dependents actually have VCB with respect to the parameterization,
used extracted dissolved, and you can see also these in the plot here that show the
projection of defeater with background subtracted. So these only signal on the
left for TBS to DSP new on the right for the DSPs 30 new when you compare the two
different feet down with the CLN or the bgl model, so the dots are the data back
on subtracted, and the projection is the result of the feet. As I said a systematic
uncertainty are just mediated by the external inputs. So I will not spend much
more time on these I just want to say that there's a byproduct of the analysis we
have also not only the measurement of VCB and four factor parameters, but also some
ratios and branchy ratio, which are very interesting. To test also order like su
three for instance, order theory assumption Okay. Then now I switch to a
related the measurement, which is a measurement of the W distribution in VST s
timing is the case and this concern basically deeper study of different factor
if you want to support the fact that indeed the demand the DCP extracted from
the previous analysis indeed, different parts are very well modeled. So, in these
analogies, we are using an independent data set that the one used for the PCB
measurement. And we are going to focus on bsds starting in UDK, where the d s star
is fully reconstructed in the case of the two IDs and a photon. So, the photon here
is Very soft as you can see from the PT spectrum here constructed on the laughter,
but we anyway are able to reconstruct the DSR peak that you can see in the body mass
of the DS gamma system in this plot and subtract the background here and using
just the signal in this speaker, we are able to perform a measurement of the W
shape. So of the record variable. As I said, before, you cannot reconstruct fully
this variable, but you can make a summation based for instance of some
multivariate analysis. And the idea that you you been in this approximate variable,
and you make a feat in each being for the sample composition using a crafted master.
And then you can extract the shape of your signal and unfolding these using Monte
Carlo to unfold the solution and efficiency. You can then use You have this
spectrum here constructed of W that you can compare for instance, with
parameterization. They're either from the VC v ni is in the GL and in the CA CLM
case, and you can see very good agreement. And with this background also
phenomenologist can play can work to extract or so
data for model. So, in summary, I just show you the first measurement of VCB
presented a mystery made another collider and the first one using VST s. And yes,
the star unity case, we get the Deezer result for the CLR and for the DCP in the
cillian and vgl parameterization that show no difference between each other and we
are in agreement with both the US presenting credit measurement and that
report here in this plot a summary of all the pieces Just measurements. So you see
all these point are the previous measurement done
at the factories, but actually
also, so let's say in a blasting mines Collider, and which average to the green
band here, which is the average of this
crazy
measurement
without the LSP booth and in the orange you have the average of the increases
measurement. You see our measurement is in between It's okay, it's maybe more or
green increases the point but okay, we don't have the precision here,
unfortunately to say something prompted. And that's all.
Thank you. You've got
other questions for me.
I have a couple question on page nine. Yes.
Thank you. So the fifth The following software basically not. Okay. I think it's
very good, very nice analysis, just to understand that you're here you're not
normalizing to be zero into star. So, I think that you use these normalization not
only for the branching ratio, but also for B Yes, that you have in these in about
this case, since also the the rate of B zero into the star new depends on VCB. I
think that you should have some similarity here or at least you lose a beat to the
sensitivity in the ratio of the two
or not. So the point is, is the fact that we are using the measure the consideration
Okay, we are not making noises of like here the composing heat as differential
decay rate or inferring the potential ratio of the pieces From the failure of
the CPM for factor measure the factors. So this is a just a measurement that is used
just to set the scale at let's say. So there is no silly circularity because they
use the measure
number.
I don't know if I'm clear enough.
not usually don't use a DDD user that you add in is
yes, you use the EDA, of course. But this is a measurement of the so you just see
the upset of Is that so?
So you don't have an explicit dependence on the No, no, no. And then a second
question. Sorry, Jonathan. To understand better in you use these two variables that
are I think, strongly correlated in the feet that because you obtain one in terms
of the other no is not usually one would like to have in DC would like to have some
variables that are not so correlated in this case, of course, okay you cannot
factorize them you use a plain English. Yes. But in any case you have a strong
correlation between them, because you obtain the incorrect internals of the
other.
So, yes, you're right that they are highly correlated.
So, I yeah, they are very correlated, say, but the there is one fact that they didn't
present here that they didn't put any plot because of Of course of time constraint in
this plane, this to the plane, the good thing is that
the signal and the background are committing
very different region. So it's not only anchor alone that allow you to separate
the different component but it's actually the to the plane first point, second
point, you That they even if they are correlated, we don't care because we are
doing we are modeling this correlation. So, the full information is inside in the
2d template. So that so you are not losing, actually you are gaining it
because the anchor allows you with the people to either battle discrimination of
signal and background in the plane, but the anchor didn't don't bring any
information the fourth factor is barely dependent on the fourth factor, while on
the other end the pair is highly depend on the four factor so he brings all the
information on the form factor.
Looking at again,
curiosity on page 20 like you can go to Yes. Okay, yes. So basically you can hear
separate things out for the start is in the Yes or no? Do you think that you have
a son? Yes, that come from the star key. So.
So yes. So, so basically you have to separate the two sample.
Yeah, yeah, but you do it in defeat. So they you are feeding the inclusive sample
all together. So here in these the peak of the DS, there is of course, everything. So
there are also DS coming from the esta which is the dominant component. And then
when you look at in the in the 2d plane here shows us the projection, but when you
look in the plane of the increase sample, then you can discriminate the different
components. And this is actually the points know that, indeed you can you're
making a simultaneous analysis of PS, DS and PS DS star, where vcv is a common
parameter. So you're you're profiting From analyzing them simultaneously and these
can be done because they use these people if you have to make the approximated w for
one or the other you will lose a sensitivity is one of the two because you
have to make a choice to approximating the increase and polar for to have a gain
sensitivity in one sample on the other through these people you can simulate a
fit them, analyze them without losing sensitivity in one of the two.
Okay, thank you very much.
Are there more questions?
Yes, sir. Mark, please. You should be able to ask.
Okay. Yeah, thanks. I was just wondering if you could mention a few words about the
process. For the future given the statistical uncertainty is already already
the smoothest
Okay, so, about prospect here. So, first comment is that the same method can be
applied to the zeros, finding the proper minimization, let's say which will be
another channel but we have already the idea here and you then don't you get read
from the input data subjective which is the dominant contribution. Then the other
systematic that you have here are just, you know, on the experimental method that
you use that can be of course improved. And so in that case would be zero the
dominant contribution will be the brain shift ratios of the channel, normalization
channel that you will use. And at the end of the day, you will love precision which
is leaving Bye, bye bye that external input, so it will be the same of
measurement at the factories. So yeah, that's a limitation of this method, of
course. And here for DBS First, you have to improve on Fs over fd. So for instance,
with new data in two MB around three and beyond the first year to improve on your
input, and then you can improve on this measure, or you can exploit maybe other
other thing like for instance, we know that Fs over fd depends on the beauty of
the BS. So you can think of making these measurements in beings of the beauty of
the BS and measuring then there's loads of these dependencies in an in a separate
sample like we already do, using BST Say file that is 02 tips ik for instance, then
you can get this load from these external measurement which can be done at very high
precision and measure here simultaneously VCB vs Fs over fd using that though the
input as an
external measurement.
Okay, very clear. Thank you.
Additional questions or comments?
If
not done
maybe
Maybe we can close the session here.