Thank you. Yes, I will talk about Higgs measurements in a decay, um, to second
generation fermions at the LHC. Well, decays to the second generations are, of course,
more difficult to see at the LHC, because of very low branching ratio and
the couplings of the Higgs to the second generation. This is illustrated on the
figure on the right, where, um, particle mass and therefore coupling, which is
proportional to the mass, is shown. In this talk, I will present direct searches for
Higgs to cc bar and Higgs to mu mu decays. So, let's start with Higgs to cc bar.
And let's first look at the analysis by CMS. This analysis is done at 13 TeV with partial run two
data set. The search for Higgs to cc bar is done in the production of Higgs
associated with a vector boson, as shown on the diagram here. The reason for that of course is...
when Higgs decays hadronically, the dominant QCD background is too large, so we
have to have leptons from vector boson decays to tag an event, to have a cleaner
selection. So, three channels by CMS analysis are pursued. First is when
Z boson decays to two leptons; Z bozon decays to two neutrinos and a W boson
dacaying to lepton and neutrino. And here by lepton I mean a muon or an electron,
so, two channels in each category. Higgs dacaying to cc bar can be ...
and by CMS two analysis are pursued - it can be reconstructed as two small cone jets using
anti-kt jet algorithm with parameter zero ... with the cone parameter zero point four and then
a c-tagger is developed to tag both of these jets as c-jets or light-flavor jets,
or b-flavor jets. And the second approach is to look for a large cone... cone jet.
In this case it's anti-kt with cone parameter one point five, and in this case a different tagger has to
be used to tag a jet as two c-like object inside. It is more used to select boosted jets
boosted Higgs. The dominant background in all those channels is
Z plus jets and W plus jets, with some small contribution from tt bar and QCD in the
zero lepton channel. So first, let me talk about the first analysis, when two jets are
reconstructed, in this case a deep neural network tagger is used, which is depicted
here. It's rather straightforward deep neural network with four layers, each has
one hundred nodes using as input jet kinematics, tracks and secondary vertex inputs,
total of 66 features. And it outputs probabilities for a given jet
being B flavor, C flavor and light flavor. Of course, B-jets are background,
when we look for C-jets. So we have to look for two discriminators. One discriminator is
to separate C-jets versus light jets. And for that the variable C versus L is
constructed as a probability of jet being a C divided by probability of C plus light,
and C versus B, which is similar - probability of C divided by probability of C plus B.
Figure on the right shows the efficiency of this tagger in three dimensions.
On Z-axis is a C-jet eficiency and you can see equidistant
line of efficiencies. So for example, CMS working point is around thirty percent. And for each
point you can see what is B tagging jet efficiency or in other words mistag rate for B-jets,
and light jets on the Y-axis. And in CMS there is a working point used for
pre-selection of jets is about twenty eight percent C-jet efficiency with fifteen percent B-jet mistag rate
and four percent light jet mistag rate. Further selection involves of course selection of objects,
such as "two lepton", "no lepton" and "one lepton" in different channels.
In two lepton channel, you want to have a Z peak, so we select on dilepton mass
and we cut on pT of two leptons of 50 GeV, 150 GeV, so we
have two categories in this channel. And then by missing ... cutting on missing energy
in zero lepton channel and pT of the W boson candidate in one lepton channel.
And then we have a pre-selection of the jet - one of the jet from the Higgs, um,
on C versus L and C versus B taggers, as I described previously, and then later on these C versus L
and C versus B scores are used in the BDT. And finally, there is also a selection on
invariant mass of the two jets in this range. The backgrounds in this analysis are
constrained from control region, taking the shapes from monte carlo. okay, so, this
figure here shows how the control regions are defined, which is done in a plane of
C versus L and C versus B discriminator on one of the jets.
Signal region is shown here, as ... as I mentioned already, and then control regions are defined
inverting one of the cuts. So, V plus light ... light jet flavor is constructed
by inverting C versus L cut, V plus heavy flavor jets are constructed inverting
C versus B cut and then also tt bar control region, in addition, invert
vector boson invariant mass in two lepton channel.
We have another control region, which is V plus two C-tagged jets, which is the most
difficult one, because it basically is signal region but inverting Higgs boson
invariant ... invariant mass, Higgs candidate invariant mass. The first ... sorry ... the light flavor
control region and tt bar control region are very pure in light flavor and tt bar respectively.
Heavy flavor and C-tag flavor are not so pure. Nevertheless, they are very useful
to construct these corresponding backgrounds. Finally, BDT is trained to separate
signal and backgrounds, which use sixteen to twenty input features, including C versus L
and C versus B scores of all jets ... of both jets, and invariant mass of the two jets, um, and,
on the figures here you see the distributions of the BDT score after the final fit, when
background is also included in the fit. So they are normalized. And on the left you
can see in the two lepton channel dominant background is green, which is Z plus
jets, in different shades are different kind of jets - either B-jets, C-jets etcetera.
on ... in the middle is one lepen channel. So the dominant background is orange, which is
W plus jets, with some contribution from tt bar in blue - it's hard to see but it's in
a log scale - it does contribute here. And on the right is the zero lepton channel,
where dominant background is Z plus jets again, um, noticible here, there is
some contribution from QCD, although it's in a low BDT score, so, actually
khm, does not affect much the ... the sensitivity of the result. And the results for this
analysis is, of course, a limit of thirt ... of seventy five observed - seventy five times the Standard Model
with expected of thirty eight. So, there is a slight excess, although with ... with uncertainties of course,
so, we have a limit from this channel. Now, I mentioned in the first slide,
we have a second kind of analysis, when a jet ... Higgs is reconstructed using a
single large cone jet. For this a dedicated ... well, first of all, jet sub ... jet algorithms
is developed, but in addition jet B-tagging and C-tagging algorithm, using
rather complicated neural network is developed, so. It's described here - it uses
a lot more variables, hundred Particle Flow candidates, meaning all their four vectors,
using some, um, high level features, secondary vertices etcetera. It has more
complicated structure, using convolution neural networks. And more details you can
see in the talk by Lucas in a dedicated session. But the results for this analysis
is slightly less sensitive - is a limit of thirty nine expected limit of forty nine times the Standard Model
Nevertheless, we can use this analysis to combine with, um, um
resolved jet analysis by, khm
splitting events for more ... to more boosted and less boosted, using the pT of
the vector boson - so there is a cut of 300 GeV, less and above - and combining that we
can improve slightly the final limit ... and now it is thirty seven times the Standard Model
expected and seventy times the Standard Model observed. Now, this was analysis by CMS, here is analysis by ATLAS.
This is also done on the same data set - thirty six inverse femtobarns, however, only a Z to LL
channel is used by C.. by ATLAS. There are two BDTs developed for separation C-jets
against light and C-jets against B-jets and on the right there is a similar picture
depicting ... depicting efficiencies and the cross shows the working point, which
corresponds to forty one percent C-tag efficiency with twenty five percent mistag rate for B and five percent
mistag rate for light flavor jets, And, the results are then extracted by fitting invariant mass
of the two jets, which is shown on the plot figure ... on the figure here. And ... yeah, the
normalization of the dominant background, which is Z plus jet is also floating in
this fit. So it's kind of constraining the background here as well. And the result by
ATLAS here is a limit of one hundred ten times the Standard Model
with expected of one hundred fifty
Allright, this was Higgs to
cc bar. Now let's move on to ... to Higgs to mumu. Higgs to mumu is a ...
in some sense, more straightforward - is a fit of dimuon invariant mass, looking
for a bump at one hundred twenty five GeV. The difficulty here is to find the right model to fit the
background. In CMS, in this analysis, this modified Breit-Wigner used with [...]
multiplied by Bernstein polynomials. BDT then used to categorize the events, enhancing
the VBF, when there are two jets in the events, eta of the muons are also used to further
separate high ... better resolution events with less ... better ... and the ... the results of this
is an upper limit of about three times the Standard Model. And by ATLAS, there is a
result using the full statistics of one hundred forty inverse femtobarn. And here I just show some
improvements that has done with respect to thirty six inverse femtobarn analysis, which is using
BDT for categorization - there are four ... four categories, depending on number of jets
and, in each BDT - high, medium and low categories used. Here are some variables
that are used in this BDT. Another improvement is including QED FSR recovery
from muons, which is shown on the picture here. It does not affect many events,
but it gives some improvement to the final results. And then, there is a background
function is Drell-Yan lineshape convoluted with Gaussian and multiplied by
power of ... power functions. And this is the result by CMS [edit: by ATLAS]. On the ... on these two figures
are shown are events in all categories - on the left is without weights and on the right
weighted in each category by signal over background. And the result is the limit on
on mu of one point seven times ... times the Standard Model. It's dominated by ... by statistical
uncertainty as ... as in CMS as well. In this slide, I show the summary of previous
results for Higgs to mumu research as well. I showed the ones shown ... depicted with the
star. Unfortunately, there is no yet results for full run two data from CMS.
But I think this table gives you some idea of what would you expect from CMS
comparing ATLAS and CMS results ... from previous years. And this is a summary.
I've presented you recent results on CMS, and AT... from CMS and ATLAS on Higgs boson decay
to second generations. There is no yet significant excess observed against the
backgrounds. The best limit on Higgs to cc bar is from AT... from CMS and it's seventy times
the Standard Model. There are other ways to constrain coupling of the Higgs to
charms, which I didn't describe here. For example, most notable is from Higgs pT
distributions, which is mentioned in ... in the plenary and also in the dedicated
talk in the parallel session. There is also Higgs to Jay Psi Gamma dacay, constraint
from width of the Higgs and from global fits. As for Higgs to mumu the best results so far
is from ATLAS and the limit is one point seven times the Standard Model and we are looking
forward to CMS result with full Run two data. Thank you.
Thanks a lot Andrey, thank you very much for this very nice talk. So, any
questions? Yes, please, feel free to raise your hands and we can take questions.
Okay, then.
Fabio, please go ahead.
Hi, Andrey, thanks for the nice talk. This is Fabio Cerruti. So, I ... um ... well, maybe
it's more a general comment. I mean, if I look to the performance of ... this is true
for ATLAS and CMS ... of the c-tagger, essentially. You have a factor
two inefficiency for B versus ... for beauty versus charm. So considering that in
the Standard Model the two branching ratios are ... I mean, Higgs to bb is twenty times the Higgs to charm, it's
expected. Does it really make sense to try to measure Higs to charm? Wouldn't be more
meaningful to try to make a simultaneous measurement of the two, maybe binning in a
kind of more charm-like, more bottom-like ... selection, because if I guess yeah,
you fix Higgs to BB to Standard Model, but after your selection, you still have ten
more times Higgs to BB then Higgs to CC and the mass is very similar, right? So ...
Yes, you're correct. You're correct that we do fix Higgs to BB to Standard Model.
That's how we deal with it now. And you're correct that it would be better to have a
simultaneous fit for Higgs to BB and Higgs to CC bar, and there is such a plan for
legacy analysis at CMS, at least, because I'm from CMS, I can tell you. It's of
course, more difficult to do for many reasons. But yes, you're correct. It makes
sense to have a simultaneous fit for Higgs to BB and Higgs to CC and have an analysis
that aims to do such a thing. It's not trivial, but - yes.
In addition to that, I can say that there are some improvements from CMS and probably from ATLAS in
the taggers. So in this CMS analysis, we use so called Deep CSV tagger. There is now a
new tagger called Deep flavor, or sometimes called Deep jet, which is a bit more
elaborate Deep Neural Network with more features and it gives some more
performance to separate C versus B and also C versus light, so, there will be
some improvements there.
Thanks a lot. And we have another question from Benedict. Please go ahead.
Yeah, for the Higgs to CC, can you comment on what the relative contributions of
systematic and statistical uncertainties are to ... yeah, limiting the precision of
the analysis?
Yeah, this is an interesting question. I have this table from CMS and the limiting
factor here is statistical. So, this is here shown on mu - best fit mu value
which is in this case thirty seven, u-um ... expected, I think, yes. And then statistical
uncertainty are dominated. With ... with experimental uncertainly coming from
mainly charm-tagging efficiencies, but also from theory. So for example, theo... signal
theory make some large uncertainties. However, I also have ... yes, this table from
ATLAS, which is again, breakdown of the uncertainties and here statistical
uncertainty is ... is forty nine percent, but systematic uncertainty is dominant. And the dominant
here is the flavor tagging uncertainty. So ... this is something that I expect ATLAS to
improve - bring down this uncertainty on the flavor tagging, and so that it is
become statistically dominant as well. I don't know if that answers your question.
So there are different splitting of uncertainties between CMS and ATLAS and
CMS, we managed to bring down a lot of systematic uncertainties, so statistical
so far dominant one.
Okay, thanks a lot for that. Okay, so we should co...