Good. Okay, you can start
Yeah, thank you. Hello everyone.
I'm glad to give the talk about KD performance in run to it are some
experiments today and I would like to introduce you quickly to the I outline of
the magic of particle identification at FCB. At first I will introduce the other
subnet detector and explain the main p ID observables. Those observables are
computed combining information from the CBP ID sub detectors, which are the rich
detectors in your chambers and the kilometer kilometer system. Then I will
introduce the general VP ID strategy including the key ID computing strategy.
And after that, I will talk about key ID calibration and calibration samples for
charged and neutral particles. and conclude my talk with an overview over the
pod performance. The party identification is a crucial ingredient of the physics
analysis at nsep as a flavor physics experiment, and one important field of P
ID is the flavor tagging of neutropenia zones. Furthermore, it allows for an
excellent reduction of combinatorial background and also the separation of
different planets states with an otherwise identical topology. Like for example, the
zero to kk versus B zero to pi k versus lambda B to PK or from eradicates B plus
to K B plus two k versus B plus two k pi pi.
Yeah, and
what you can see in the pop Below is a fit to the invariant mass of the PK mu mass
system of the latest measurement of Neptune for universality for the ratio
applique in blue Consider total signal which is mainly from the signal itself and
the other lines that are hardly visible or the background components we are not
interested in. And yeah, so the other signal detector in run tool can be seen in
the sketch on the slide where in yellow I have marked ring imaging to rank of
detectors. The abbreviate every variation is rich, and they allow for p ID for K on
pylons, protons and low momentum electrons which carry electromagnetic charge. The
Mune stations are colored in green and comprised of five different immune
stations and deliver high purity tip for nuance. And the kilometer system is
colored in violet and consists of scintillating the scintillating pet
detector, SPD, pre sharp detector PS anti electromagnetic and hydronic for renewals,
ecad and H color. And this carry meter system allows for p ID for electrons,
photons and neutral
ions.
To identify heavy flavor decays with large hydronic background the rich detectors
allowed to separate k ions pylons and protons over a very wide momentum range.
The first rich detector which one which can be seen in the figures in the figure
on the right hand side comes from momentum range of two to 16 gV with CF C for F 10
radiator and arrow drill, which was removed in the long shutdown to have the
Large Hadron Collider. This is why you don't see the original in the sketch. The
rich tool covers a high momentum rates range of 50 to 100 gV using a CFD radiator
the chunk of light cones are then mirrored outside as you detect acceptance and are
measured with a photon detector array. And from a combination of those photon rings
and to track momentum from detector components to lock likelihood
distributions for the charged mass hypothesis. calculated. The NSE media
ticker comprises five meal tracking stations with headphone absorbers in
between the first mewn station and one is positioned before the carry meats as can
be seen in the sketch hits in the Mune chambers are then searched around
extrapolated tracks for other detector components. Yeah, and it is important the
moon stations are very important for particle identification of neurons and is
they are also employed at the trigger number. The color emitter system itself
consists of the SPD and fnps with 6066 kilometer cells each the hydronic
perimeter with ties of iron scintillating material with 1400 88 kilometer cells. The
electromagnetic kilometer with especially Kirby's which is the current kilometer
from Latin single letter old 6016 seconds Carrie meter system itself identifies
electrons, photons and neutral piles with information from those sub detectors by
measuring the information for neutral objects and also triggers for electrons
and photons. Yeah, the perimeter system is the last building block of the particle
identification system I want to present today. Which brings me to the P ID
strategy of NFC. Where for the charged particles, the information of the signals
detectors subsystem is combined to powerful signal absorbers. The most
prominent ones are the so called DLL, which are the lock likelihood differences
of particle hypothesis of a particle x and pythons where the pylons act as our
reference. The DLL observable is computed as a product of the log likelihood of the
similar big sub detectors detectors mentioned before which can be seen in the
equation on the slide. another variable that is combining subjects textual
information is solved with the so called problem and observable, which is the
output of a neural net which is trained on simulation with input from the detector
components and tracking information and there are also dedicated neutral neural
nets for neutral and as simulation is not perfectly describing the P ID response, it
has to be calibrated for the data room calibration technique for For this reason,
data driven calibration techniques with high statistical ration data are applied.
Yeah, if you would not use this corrective simulation for the efficiencies you would
like to introduce large systematic uncertainties which should be avoided. And
those calibration samples are selected and on to as a part of the HA trigger which is
a software based trigger following the hardware trigger Zoo and this filtering
detector data the selection of the pod killed ration sampling is performed with a
dedicated stream at the so called telcos level which means total calibration. And
this is done to save resources to to Central computation and central control
systematic uncertainties for Mr. samples. Yeah, this stream delivers luscious
statistics, calibration symbols and symbols for high precision precision
measurements. Yeah, to dive a bit deeper into the calibration samples, I will focus
now on the charge calibration samples.
And, as already mentioned, those samples are from a real time selection and based
on offline or sorry online construction with no requirements on p ID for the probe
techs, and they are high purity and high statistics samples with final states as
charged particles only. And also they are required to be modes with low multiplicity
and large branching ratios to have a really good purity for the electron for
electrons DKB plus projects K whether the job size came to two electrons is used for
the moon dedicated side who is employed as can be seen as plus for the pylons the
decay case for two pi for normal metal tracks and East and D star to be zero
pi pi is
whether the zero decays to a calendar pi on this implied and this decay is also
used for calibrations for the calibration for decay ones for protons the decay
lambda 02 proton ion and lambda c two photon k on ion for high momentum ranges
used for the neutral PRD multivariate classifiers combined variables which are
describing energy deposits in the kilometer sub detectors and allows for
discriminating photons from head runs electrons and high energy, neutral pylons
and as a killer As calibration samples for photons the case d s two prime plus d s
two gamma d s gamma and B zero to k star gamma used. And for neutral piles to decay
D zero to k plus pi minus Pi Zero is used, as can be seen in the plot here. Yeah, the
performance of the FCB particle identification can then be assessed by
those pure calibration samples. And here I'm showing the P ID efficiency over the
track momentum range for two given cuts on the DLL variables, I described those
variables above as the difference of the likelihood of the particle to select and
the pion and only for the moon plots as an additional requirement variable is on this
prompt here as well. So the red empty circles show that efficiencies for us are
cut into solid red dots for a tighter cut. And as a take home message it can be seen
that those variables show an excellent discrimination Over a very right kinematic
range for the leptons The same can be said for the headlines where I'm showing here
the efficiency for K ions and protons. The neutral performance can be measured using
three different neural networks which are trained with simulation to separate the
photon signatures from our from other species which are is not hadron for photon
versus hadron is not electron for for transfers electrons and is photon for
photons versus neutral piles. And in the plot below, you can see the signal
distribution in blue and the background distribution in red for the Islamic E and
is not age training forum one based on simulation. And from that it can be said
that as a particle identification also provides a good separation with neutral
objects with dedicated towards In addition to that, a dedicated di to run p ID,
where a dot run consists of a proton and a neutron
was developed and typical use cases are baryonic case of the kind, lambda v to
doctrine, anti proton, or cross section measurements of doctrine productions. And
in the property law, I'm showing the property and distribution, which is the
output of a dedicated neural network to separate the particle type. And this
neural network gives you a value between zero and one as the probability to be a
doctor. It was trained with daughter runs for London to go to an anti proton and for
the other tracks, the decay the energetics of decay lambda C to proton. My minutes
left. Thank you. Yeah, in my game time, you can see the problem I'd run
distribution which is peaking at one as it is expected and in contrast the
distributions of the proper end washroom classifier, which is then applied on other
tracks which are proton k on pion goes check where it goes tricks, tricks, tricks
in which less than 70% of the hits are from the same tricks are shown. And you
see that for those other track types, the peak is at zero and on an all it shows
good separation performance for doctrines as well. Yeah. To give a summary of my
talk, it can be said that precise PhD performance is crucial for LSU flavor
physics. The particle identification is provided by a dedicated pod system which
is built after rich detectors, the current meter system in New York chambers yeah and
dedicated towards to Combine the input rom the signal sub detectors to powerful o
servers also using multivariate classif ers and machine learning for op
imum of separation, quality are emplo ed. And assimilation is not descr
bing it perfectly calibration tools w th data driven techniques are applied.
nd they are using high statistics pure int gration samples with large face base cover
ge. And probing those probing DFP efficie cies on those samples shows that Sydney is
showing a high pod performance for larg mutual articles as a key part of the MCP
physics program. And with the increased CD data set on free it will be possible to
develop and improve tools
or e
en more precise p ID calibration and there will be also have updates on the P ID d
tectors which which will increase the overall quality of P IDs And also the
omentum coverage. Yeah, there are there are two other talks I would like to poin
you to which are on SCP real time recons ruction and I'm in calibration tool by
he Oto. And tracking on vertices v rtex in performance and developments
r on to another city by the nada, which re also covering parts of the productio
of new calibration samples and reconstru tion of for example, el
ctrons. Yep, thank you ve