Anyway, okay, Hello, everyone, I hope you're all doing well. Today I will
present the performance of hedonic reconstruction and identification at Atlas
and CMS. So first, just a quick motivation on why we are interested in looking at
house, Atlas and CMS. So one thing that's interesting about it, though, is that it's
the heaviest Latin and so it has the largest you color coupling to the Higgs
among the leptons, but even though it's only about 6% of the branching fraction of
x, okay, I'll come back to 60% of beak works you can see from from this
measurement of the coupling, that the resolution can can be made much higher.
One other interesting thing about towers is that they also decay inside the
detector, and then there will be one neutrino and this allows you to make Sep
measurements on The exposure by looking at angular distribution, which are sensitive
to the spin correlation of this decay, but also for several beyond the Standard Model
scenarios. You might have some interesting signatures without in the final states.
And a lot of these are well motivated Fatah's because they might have some type
of enhanced coupling to the first generation. And so before we start, also a
quick review of the top properties themselves, so it has a mass of about 1.8
gV, and this means it can decay to lighter hydrants like the pylons. And this is
indeed the dominant type of Saudi case. So if you look at this pie chart here, it's
about two thirds of the pies, you have at least 100 in the final state. And in this
talk, I will call this hydronic decay tau H or just tau. And then about one third of
the time you have either one moon or one electron decay from Stop. It also has a
very short lifetime but not too short. So it can still travel on on average one
millimeter inside the detector for typical energy of 20 gV. And just to compare, for
example, we had drones, we had drones, they have lifetimes, which are about 10
times longer, but thanks to this traveling inside the detector, there will be often a
secondary vertex which we can make use of in our education algorithms. Besides that,
because because it has a very light at these energies, they are correlated jets
with that are very isolated compared to for example, cork and glue on jets that
are typically much wider and have a lot of activity in this wider cone. So for
towels, you typically have different number of charged pylons, typically one
are free. And then you can also have sometimes an extra by zero which promptly
to two photons. So on this slide I just wanted to make a very rough sketch of how
the satanic indicators are detected that opposite CMS. So, typically, we start from
ak 48, which is used as a seed for the tau algorithm, where we reconstruct different
types of decay modes. And so typically, these are counting different number of
charge tracks that have some energy deposits in HDL. And these are assumed to
be charged pints, but sometimes they can also be by zero which quickly the case to
photons and then the sun energy deposit decal. And then on top of this, you also
have identification, which often are some type of MVA techniques that are used to
reject many jets but also electrons and neutrons that can fake or can be
misidentified as stars very efficiently sometimes, and these typically use
lifetime and isolation variables to to discriminate against jets. So before I go
in the in the details, I just want to give a very broad overview of the algorithms
that are available. So at CMS on the left, we have just one algorithm for
reconstruction which is called HPS. Whereas It seems there are two so one is
called the baseline algorithm which was developed during red one, and then they're
going to also now we are using the tau Particle Flow. And then on top of these,
we have classically abilities that are used to discriminate against jets, but
recently there also have been new neural networks that that improve the performance
of identification. So now I will first go into more details for CMS with hp hp s
algorithm and the dnn.
So HP S stands for pattern strips and as I mentioned a few slides back, start from an
ak 45 For jets as a seat, so in CMS these jets are already made from Particle Flow
objects, which are already ready hadrons or electron or a photon candidates. And
then you look inside this cone and you look for the track with the highest
momentum. And around this track you make silicone, which has a size of 0.2 or
smaller and then also an isolation cone of about 0.4 which is typically used to
define some type of isolation. Then, at CMS, the decay most of the TAs is assigned
by simply counting the number of church tracks inside this signal cone. And then
to count the different numbers of zeros, we look at the clusters in the email. And
what's particular about this algorithm is that you have these green boxes here which
are called strips, where you iteratively merge together like Concept photons,
because the main reason to do this like is because photons have a high probability of
converting into electrons, but it also helps reduce some of the the pilot.
Then once you have your tau
candidates, you apply some some type of identification. So the newest thing is
this thing called Deep Tao. And this is a computational deep neural network and so
it uses just like the older PDT is high level information, like the lifetime
isolation variables and variables related to the kinematics of electrons and
photons. But what's new is that they also use these compositional layers with as an
input, the particle for Particle Flow hadron smoothens electrons or photons that
fall into this grid or this screen of cells in either five and then as an
output, it's it gives you the probability that you're talking about is a real time
when we own electron or jets. And so, basically this is one of the classifier on
the bottom here I show some some examples of the performance in terms of
misidentification versus efficiency. So, in blue is the new deep neural network and
you have to compare this to the green one, which are the previous identification
algorithms. And in particular, the most importantly, is the anti jets
discrimination here, where for a very typical efficiency of about 6%, you have a
misidentification probability of 0.6 or inversely, this is a rejection factor of
about 170. Okay, so this was CMS. Now I will talk a bit more about Atlas starting
with the baseline algorithm and the RNN. So for Atlas, there was this baseline
reconstruction developed during round one Since Atlas doesn't have Particle Flow, it
instead instead has four jets that are made out of clusters. And just like CMS,
there is a cyclical in defiance of a fixed size 0.2 and then isolation colon of 0.4.
And then looking inside the signal con, there is a BDT with set of BTS which is
used to classify the tracks. And then based on the output from this, you can
assign the decay modes to either one or three prongs. So, prong here means just
the charge track. And one thing that's important to notice here is that for
baseline reconstruction, deployment is simply the sum of the energy clusters
here. And so this is inclusive in by zero, which is not separately identified as, for
example, photons or electrons. Then on top of the baseline reconstruction algorithm,
there is the neural network, which is a recurrent neural network. That's your to
discriminate against jets. And this uses just like CMS high level variables, like
the risks related to the towel lifetime isolation, but also different variables
related to the calorie meter. And what's special about this neural network that it
has several recurrent layers that uses as input this low level information about the
different tracks and clusters. On the bottom here I have on the left one plot
that shows the efficiency just after baseline reconstruction, and you can see
that it's around 74 for this algorithm, and they compare here the the real one
from Murfreesboro. Then on the right, there is the performance plots of the
neural network of the rejection of jets versus efficiency of towers. And so here
again, for a very typical marking point of 60% efficiency, you have About a 1%
misidentification probability which is on the spot here, I think about 80 rejection
of a factor of 80 Okay. So, so, this was the first algorithm reconstruction
algorithm, the second one is called the top Particle Flow, and this one was
developed to improve the, the momentum resolution of the tau. This time instead
of using a naked for jet as a seat it uses the baseline top candidates. And it looks
inside to identify and reconstruct the individual particles. So, in case of the
church pions, you simply identify or you associate the different tracks which are
in the cartoon, the red dots here with different energy clusters in blue. And
then all the remaining clusters that don't have a church track associated with them
are assumed to be coming from Pi Zero and there's a dedicated BDT that is used to
identify These clusters and given probability that they are indeed by zero,
then then once you have these different constituents, you have an additional set
of entities that assign the right to K modes using these as an input. And this
indeed improves the the moments of resolution, mainly below a PT of 100 gV.
So on the bottom left, I have one example for the for the angular distribution for
in terms of phi. So here you have the resolution for tau Particle Flow, which is
black and you see it's much more much narrower than the rest, which is the
baseline reconstruction, but also in terms of the transfers energy on the bottom in
the middle, if you've just focused on the bottom few lines, which are the core of
this resolution distribution, where you have about 68% of the sales. If you
compare the black again, which As the top article fall to the red baseline, you can
see that you can improve by quite a lot, the momentum resolution. And on the right
here you see efficiency of assigning correctly, the different true decay modes
to the reconstructed decay modes and so you see that you have a very nice, the
agonal with a strong correlation between the different decay modes.
You have a bit less than three minutes.
Okay, good. Thanks. So, for the sector's description, we typically make an
efficiency measurements using the towels and so here you have Zeebo isn't the king
to two towers, one tower was to Milan one goes to hadrons and here to Milan is used
as a well measured tech and then the other The, the hydronic indicates how is your
probe and then you use some type of maximum like Fitz, to do some
discriminating observable to to extract the scale factors. So in terms of atlases,
In case of outliers, they use the number of tracks inside the cone of 0.6. And so I
there are no plots for the newer neural network. So I'm showing you the ones for
the BDT. But I can say that for the neural network, the scale factor is close to one
with about 3% of certainty in case of CMS using event mass between the moon and Tao,
so this is called the visible mass. And again, I only have two BDT plots, but the
scale factor for the Ico network is also close to 0.9 and one with about 6%
uncertainty which shows that you have a very good description of the detector. So
this was the reconstruction and identification. one extra thing I want to
go into is the energy calibration. So for outlasts there's dedicated energy
calibration using a boosted regression tree, and they use interpolation between
the calorie meter base and top Particle Flow. based at and then they have targets
the true PT and then train this train this boosted regression tree for this
resolution is about 6% on the on the momentum and the energy scaled and
MonteCarlo is typically one or 3%. For CMS, we already are using Particle Flow
constituents which are already well calibrated so there's not much extra
calibration needed. And again, the PT is well modeled with about 10% resolution.
And just like Atlas, the energy scale in MonteCarlo, is very close to one or 3%.
And then just to show off in the last two slides, the performance of these different
algorithms, I have some plots. So in this slide, we have Thomas. And what's really
cool about these slides, sorry about these plots is that you can see for the
different the different components of Trillian decaying to tell us and you see
the decay modes of these charges. So, so, for example, you have the Pi Zero mass
here and intermediate resonances select the row or the a message. And then on the
last slide you have the physical the physical mass of the of the Z boson. And
just to give you an idea, you have about 66 pure purity of about 66% at the peak
here for this plot for the deep Tao algorithm. So, hopefully I could convince
you that thoughts are interesting for looking for new physics, but also doing
Hicks measurements. Each detector has their own unique approach. So same as many
uses, exploits the fact they have Particle Flow, which already provides for
calibrates to constituents, whereas Atlas starts from a baseline which is more
calorie meter base, but has its article flow developed to exploit tracking for
improved resolution and I also showed that we have an excellent understanding of the
detector. good description and also showed that recent developments of networks have
been able to improve the Jets reduction with about 1% misidentification for 6%
efficiency. So thank you for your attention.
Thanks a lot, Zack. Is Do we have any questions from from the audience?
I don't see raised hands. Yes, there is one. So bedtime, I should be able to talk
now.
Hi, can you hear me?
Yes. Okay. Yeah. Thanks a lot for this nice talk. I had one question about the
energy calibration. At high PT. Don't you need anything special in your calibration
in CMS? Or you just rely out of the box on the polygon fluid constant because I'm
asking in Atlas What we get from the particular Kobe construction is very good
at low PT but degrades a Thai PT because the the track momentum is less accurate.
Yeah, so for CMS, we, we don't have dedicated energy calibration at this Hyper
V. So we indeed still use the Particle Flow constituents. And indeed, you see
that the resolution goes down, I believe at higher. But I don't have a I don't have
a blood for this.
Okay, thank you.
Do we have more questions?
Yes. Can I ask a question? Everything? Go ahead. Yes, thank you. So what I would
like to know is how much
the reconstructions relies on a good calibration in CMS
which means that you need to be reconstructed
calibrate objects with the refined calibration or calibration you get from
the data Yeah, so
the hazards performance.
So, so, you could you could already in principle use the prompt data but
typically when once once we make really measurements for for physics analyses as
the as the top group we typically do this based on reconstructed data so, so already
you have better calibration in particular for tracker which is very important to get
a good resolution on the or description of the lifetime. So, typically we use more to
reconstruct its data but it already performs pretty well on on prompts I would
say.
I was also curious about the about the ego part
for CMS, you Yes.
That's a good question
that I'm not sure about about. But I believe it's similar. So I think we
probably already have a very good calibration prompt. But, but I'm not an
expert on that. So. Okay. Thank you.
Thank you for the question.
Thanks a lot. I have myself a question. So, on slide seven, you show that the dnn
performs much better than the BDT. Do you have any idea why it is so?
Yeah, so like I said, that this deep network is a compositional, deep neural
network. So it basically it looks at really the substructure of the different
constituents inside this in this cone. So it doesn't just look at the lifetime
isolation, but also the relation between all these different tracks and I mean,
hadron send me once and electrons that that's the fall inside this
code so
so it kind of makes it kind of learns the patterns of its image. And so this really
helps to exploit you get the factor to increase the performance here.
And the for the BDT you used to use a specific library
uh, I guess I guess it was simply the team MVA library routes, okay. Yeah. Okay.
Thank you.
Do we have someone else Let me check the attendees know from from the pain list.
Now, it seems not So thanks Tor display