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[Music]
the next talk uh we're going
to listen to carsten Bish and Stephanie
Todd and they are going to tell you
about taking extremely precise
measurements at the large hran collider
so conquering large numbers at the
LHC by carsten and Stephanie PhD
students from T Tran and members of the
atlas team at C please
welcome thank
you yeah thanks a lot uh we are pleased
to be here and um take the opportunity
to share some Insight uh into our
everyday work and most importantly into
the enthusiasm that we have for our
day-to-day work we would like to talk
about large numbers in the context of
particle collider experiments at the
large hard collider the
LHC
uh that is not
working ah no okay so what are we
actually doing we are doing data
analysis uh of particle collisions uh
within the atlas collaboration at CERN
at the L HC um in this talk we want to
show you different aspects of how we are
doing this um and giving you give you an
overview of the well going from the
ordinary hydrogen bottle to the final
data analysis of proton proton
collisions and let's start to um unfurl
the sentence from from the behind um to
First motivate why we are actually doing
potic collisions why are we seeking for
even higher energies because the LHC was
built as a particle collider that
reaches unprecedented energies and um
well the uh there is a simple
explanation uh it's the Einstein
relation of energy and mass E equals mc^
s c being the speed of light and this is
a very large number as you all know and
this means um in order to produce partic
decent mass at a collider you need a lot
of energy and in order to produce even
um more massive particles you need even
more
energy um and this is exactly why we are
doing particle collisions and why we
build the LHC because it allows us to
unravel the mysteries of the universe
how it was created maybe also find out
what it is made
of
okay um so we're studying collisions we
have broader shopping list what are the
ingredients to do this so first of all
we need protons with a lot of energy uh
then we need a place where the protons
Collide and we need a huge detector to
look at these collisions to snapshot
these and then finally we need the tools
and the infrastructure to analyze the
Collision data let's just start with the
first point and um to show you how to we
get uh the protons to travel nearly the
speed of light in an accelerator and uh
I've brought you a short video for this
uh and we just start the video and set
the scene we are at the border between
France and Switzerland uh at the CERN
lab which hosts the large hyon collider
a circular collider of 27 kilm
circumference um you can see this here
uh the airport is right next to this so
just that you have a scale um but the C
does not only host the LD but a complex
of
multiple particle colliders as you can
see here and it all starts with a bottle
of hydrogen where we get our
uh protons from and we extract the
protons from the hydrogen atoms and then
accelerate them using electrical
Fields
right after that uh they are passed to
the first circular device which is the
booster and here you can already see one
of the major things crucial things that
that happen here you have four stages in
this uh collider and um here they get
accelerated for the first time and then
they get passed on to the next bigger
collider and then to the next big bigger
collider and this is what you what you
will see here and most important thing
is here that you see that the proton
beam is not continuous right so you have
large bunches with a lot of protons uh
while the the second um circular
collider is further accelerating the
proton it also splits these larger
bunches into many small bunches um these
bunches then contain roughly 10 to the
11 protons a very large number um and
have a bunch spacing so-called of 25 NCS
right so here you can see the injection
already in the larger uh accelerator
complex and we accumulate a lot of
bunches until we have roughly 3,000 of
these bunches right and um we even uh
accelerate them further because we need
a lot of energy and once um we have uh
the the 3,000 bunches we can inject them
into the large Hardon collider in
opposing directions so that you can
allow for collisions and in the Lac they
reach their final um energy of
6.5 Terra electron W and um they travel
20 20 minutes uh to reach their final uh
uh
energy okay so now we have um protons
that are in bunches um 3 thousands of
them uh 3,000 of them with a bunch
bacing of 25
nond and um we are ready to deliver this
beam that we have created in the LHC to
the experiments that um are taking place
at the LHC there are four uh main
experiments um one of which is the Atlas
experiment and Carson will tell you a
little bit more about
this okay so as Stephanie said uh we are
part of the atlas collaboration and I
would like to show you a bit what the
atlas detector really does and how we
process the data in the atlas
collaboration so so uh once we have
stable beam in our in the LHC and we
have the full energy of the beam uh we
activate our so we start we cross the
the beams and we activate our detector
so the detector is sensitive and now we
have our first collisions so the
detector was designed not only to rec to
be able to reconstruct the paths of
particles but also their energies and
then that mixture helps us in
identifying the different types of
particle we want to identify because all
of them have different signatures um and
we want to store the information of all
of these events for later usage so that
we can later on try to identify what
kind of events happen to cause these
outcomes we saw um and this is what we
can see here and uh these collisions
happen every um every second 40 million
of these collisions happen because you
have the 25 nond sponge spacing uh but
remember that all of these bunes consist
of a huge number of protons so uh you
have large even larger number of proton
proton interactions happening every
second so here in the video we show the
60 million collisions that was uh the
number the LHC was designed to deliver
however the LHC overd delivered so it
performed really really well and now we
are roughly at 1 billion proton proton
collisions per second um remember that
around 25 of these happens
simultaneously so we have we can only
measure uh the the overlay of 25
collisions every time um and so first of
all uh in our data taken period we want
to consider today this is the data
taking from 2015 and 16 um this adds up
to 2.5 * 10 15 proton proton collisions
that's a very large number we put it
here on the slides and as well we put a
slide a scale in here and every tick on
that scale is a factor of 1,000 so we
can really see that we go quite up here
and and we have this uh 2 and a half
quadrillion events on that scale shown
here uh just to get a grasp on that
number so if you imagine to have a huge
amount of sugar of plain table sugar um
maybe don't do this as an experiment
right now but if you imagine a huge
amount of sugar that would cover the
floor of the glass hole in front of that
hole and you would have a layer of
roughly 1 cm then each grain of sugar in
there would still represent 10,000
events so that's a really large number
uh as I said we cannot really
distinguish between the ones that happen
simultaneously so we go down the step
already we have now 10 to the 14 events
that could measure um that's the first
step you go down uh but you have the
problem that each of those events if you
would want to take the data for that is
roughly 1 Megabyte so one megabyte
events with 10 to the 14 number of
events uh in total that would be 100
exabytes uh at an average
sorry at an average rate uh of 40
terabytes a second we cannot afford that
um so we have to find a problem a way to
only find the interent the events we're
interested in we are a bit Lucky in
there because we have been studying
these collisions for decades now and
nowadays we only interested in the one
collisions that are quite rare and very
high energetic so we are only interested
in one in a million one out of a million
events where we have a huge energy and
which we haven't started really
before uh and we have we have really a
fast triggering system which helps us to
to make fast decisions to decide whether
to store an event or not uh and this the
first level of the triggering system is
um only applies really simple algorithm
so it really only tests sub parts of the
detector so it doesn't look at the full
detector only looks at if there's huge
energy deposit summary or not and uh
that trigger system is done in hard rare
and it accepts events at a rate of
roughly 100
KZ um so this adds up to an data rate of
roughly 100 GB per second and that is
something that we can um so we read out
the detector for each of those events
and we have this 100 gbt per second and
this we can pass to a huge cluster at
CERN uh where 30,000 CPU cars par um
process all of these event candidates in
parallel there we have a bit more time
we can uh take bit more time we can try
to identify the different particles in
there we can try to construct
observables using multiple objects in
the event uh and using these tighter
filters we can reduce the rate by
another factor of 100 and and that gives
us a final event rate of roughly 1 khz
and this gives you then the data rate of
R gigabytes per second which we can
comfortably store and we pass all of
that to the C data center uh so just to
recap that so the first level trigger
gives you an event rate of 100 khz that
already brings you down uh both of that
affects the file size and the events in
the same rate uh and going also the
second level trigger we're going to got
down to 1 khz and then we at a level
where we have uh roughly 10 to the N
events already and um so we have already
broken down six orders of magnitude
using these large filters um you might
wonder if we introduce a bias if you
apply such a filter if you only look
into the events that are have the high
energy and what we try to what we do to
avoid such a bias is to use a limited
amount of our bandwidth to essentially
stall random events and these random
events give give us the handle to also
have a look at these events which would
normally not pass our filters so now we
have all of these events passed to the
CERN Data Center and Stephanie will tell
you a bit more on what we do
there thank you exactly so uh I have to
swap the slides and um yes so the next
step is the data preparation once uh the
data comes into the data center um
there's one at CERN and also one in
Hungary that we are currently using um
there are two things that happen so
first of all we copy our r data to a
long-term storage on tape this is the
first thing that happens and the second
thing is the So-Cal reconstruction what
do we mean by this and uh for this uh
let's just quickly recap how collisions
event look like in the detector uh we
have the two bunches that Collide and uh
while they Collide they leave a burst of
particles in the detector right but the
particle itself are not visible to us
and also not visible to the detector
what we can see is the interaction of
these particles with with the material
of the detector and these leave
electrical pulses in the detector as you
can see with the orange boxes here um so
uh the aim is now to transform uh
transform the hits in the detector into
uh a data formus that we can use for
everyday physics analysis um let's go a
bit more in detail so what we do to do
this so we go from rate raw data to
physics object this is the the main goal
um so first of all we have to do uh
calibration of energies of positions and
everything that is measured in the
detector right so this is done as a as
as a reconstruction step then we also
have to assess whether the data we have
taken is actually good for uh physics
analysis uh let's just give you an
example for example um we have
ionization detectors and there are high
voltage lines and if one of these lines
were not operational then uh this part
of the detector cannot be used for
example or if uh the noise in one of the
cells is is just too high than what we
would expect on average then we also
mask these cells and these are then not
taken to into account for the physics
analysis so um we can either flag single
events as not good for physics analysis
but we can also flag periods of some
minutes already for not being used for
for data analysis so this happens from
time to time so we have to basically
select the good ones from the bad ones
right um and all these in addition to
other data that for example the detector
operation data which is for example the
trigger um conditions that Caron just
talked about we have to store this
together uh with the physics objects
into the file right and the major part
of the of the Reconstruction is the
transformation of the of the detector
hits into physics objects for example
electrons that you might know um just to
give you an example here of um this
scheme shows you how from the detector
hits we reconstruct trajectories in the
in the event this is done um on a
sophistic with sophisticated analysis uh
algorithms that take into account um
statistical tests to determine which of
the hits belongs to the same track and
then you can combine these hits and then
you form a track right this is how we
how we yeah basically uh reconstruct for
example tracks in the detector um then
each physics object has has attached to
it um a pattern of um directions from
the tracks and energy dep deposits and
this is what we also store into the
files right the physics OB object
objects with their features and this is
why the file size increases then
slightly again because we we we add a
lot of data to this already um okay uh
then the next thing is that you can
already see that the the the the file
size is still at one petabyte right so
this is not feasible to to analyze this
we just don't have the resources for
this and to um now we have to reduce the
size of the data and there are three
ways to do this in three columns so the
first um way to do is just to re reject
full single events that are just um
highlighted here in blue boxes and you
can just remove whole events what you
can also do in the middle row is you can
remove certain OB objects from certain
events and these objects are represented
as these colored circles here right and
then what you can also do is you can
remove observe variables that are
related to a physics object or that are
related to the whole event from the
event right the first row um reduces the
the the the number of events in in the F
in the files and so also the file size
the second two only uh reduce the file
size but not the number of events right
okay and uh these these three methods um
how do you select these uh which which
objects to keep which events to keep um
and uh which ones to reject um this is
based on a physic physical meaning right
so um we
then
yeah to in order to um make our physics
analysis we um make a two-step approach
first we make a rough uh physics
selection for that that can be used for
multiple analysis so multiple analysis
then use the same output from this rough
physics selection and um this um already
reduces in our case um in the analysis
that we are doing our PhD reduces the
number of events by 1,000 roughly and
linearly so also the file size um this
is done by just
rejecting single events and if you do
this the second and the third step
already you uh further reduce the event
size of to about one terab the the file
size to about 1 tab right and then there
is another step where you uh do a
physics selection that is analysis
specific so every single analysis has
its specific own uh selection so our
selection is different from any other
analysis that is pro that is done right
and um this then further reduces the
event size in our case to
140 events in our analysis signal region
and we are left with a uh fire size of
about a little bit more than 1
Megabyte these sizes can easily be um
analyzed on your laptop while the the
former ones that have just the first
step where you have still one terabyte
of of of data you would rather do on a
local
cluster okay um so once we have done
this full selection we have selected one
of 141 events uh one of which looks like
this so what you can see here is an
event display um you can see the scheme
of the detector in blue red and greenish
and you can see that this is cut in half
and the beam beam pipe go goes just
horizontally through the middle of the
of the scheme right uh what you can also
see is the remnants of the collisions
the tracks in the middle of the detector
and then some some yellow dots which
represent uh Energy deposits in our
calor meters on our some part of the
detector uh what you can also see are
these these reconstructed physics
objects and these are most interest
interesting to us and uh most
interesting to us in our in our specific
analysis are these yellow ConEd shaped
objects um and it is very characteristic
for the analysis that we are doing that
these cone shaped um objects are very
close to the beam pipe right um so now
I've shown you what um such event could
look like and we have selected the final
events that we want to use for our
analysis and uh Caron can now go on and
tell you how to do
this okay so now we have done all of the
hard work we have prepared all of our
data we have passed this to the
physicist now let's do physics so uh
what are the actual questions uh the
actual conclusions that we want to draw
from all of that data um that is one
question I would like to answer I would
like to answer to show you as well how
we try to uh see these
confusions um so let's pick an easy
example uh the the easiest thing you
could do is just to count the number of
events You observe in your data uh that
would be the first number to see and the
other one then you need to compare this
to a prediction so we have predictions
for your background processes and you
have predictions for your signal
processes um in in this easiest of
examples um imagine you have a perfect
prediction of your background you you
know that perfectly to be seven so you
predict to have seven events um in the
background and you your signal
prediction is one event just to keep it
simple and now you want to draw
conclusions from that so even if the
observed number of events perfectly hits
your background plus signal prediction
So You observe eight events and data
here that doesn't mean that you can draw
any conclusions from that so you cannot
see say here that you observed your
signal process just because it fits
perfectly to your data um the problem is
that the case the theory that your
signal doesn't exist so your background
only uh theory is still perfectly
compatible with your data so there's
within statistical uncertainties the
eight observed events in data are
completely fine with the prediction of
seven events that's like if you uh
imagine that on your way on your way to
work every day you on average observe
seven red electric cars uh however one
day you observe eight red electric cars
you wouldn't say that this must be A
Strange Day and something is completely
off because you know that this is just a
statistical fluctuation with no
underlying meaning and the same you has
to apply here so we cannot draw any
conclusions just from that so we need to
have more information uh and that we can
do by just not adding up all of the
events but by really looking at each
event and trying to see and to get a bit
more physical understanding of what
really happens in there and that we do
by looking at kinematic distributions uh
so let's look at the particular one
event Stephanie showed here um and let's
work through how we do it do that so in
this particular event Stephanie showed
you these two yellowish cones that we're
interested in and now we want to
calculate an observable from that um in
our case that just means we want to
calculate the invariant mass of these
objects uh unless you've been to the
introduction to Quantum Mechanics
lecture two days ago probably you don't
know what this is this inviron mass is
just the you can think of this is being
the energy you need to produce to these
two particles so how much energy do I
need to produce these particles in this
configuration and in this case um the
energy here we would uh would need is
2.8 Terra electron electron volts which
is a huge number this is this is why we
are so interested in these events
because we have these two very high
energetic event Jets objects in that
case so if you want to look at this kind
of events on here in that case this
event has 2.8
Terra electron volts so now let's
visualize this what we do is to plot
this in a histogram and what you can see
here on the xaxis is the observable you
want to look at so in our case this
invariant Mass um and on the y- AIS you
have the number of events in specific
regions of that invi mass so I point I
highlighted those different regions here
so you just count how many events have
are in this green regions how many are
in the yellow orange and red ones the
event we had before was comta in the red
region so we have our first data entry
here so now if we wait a bit more and we
take our data in Atlas we see all of the
other data events coming in we do all
the same procedure for all of those
events and we end up with a distribution
like that and here you see this is the
actual distribution that we measured in
data in the atlas measurement um but
still with only the data you cannot draw
any conclusions so what you again need
is some
predictions um so for the predictions
what we do is to generate events from
our Theory So based from our Theory uh
from our underlying Theory we generate
events and then in order to be able to
compare that to data which has some
effects of the D of the detector in it
as well we need to simulate the full
detector as well so we not only need to
generate the events but we need to know
how would the reactor uh the detector
react on the on these kind of events and
then we pass the generated events
through the same chain as we did for the
data processing so we do the same object
identification the same analysis
algorithms and we apply all of them in
the same way so uh then we know that we
can compare the two numbers
and
um yeah uh that gives you however a bit
of a problem in terms of file size
because if you want to have enough
statistics so that your final result is
not really limited by the number of
events that you generated which would be
a bad thing um we have to see how we can
can reduce our file sizes so the the
good thing is that we don't need to
generate all possible processes just
because only a handful of processes
really contribute in our final signal
region so we generate all of them we
generate all of them in a comfortable
amount so here we put roughly 1 million
events or something uh for all
background and Signal samples and then
uh we also obviously store the
information of the particles that we
passed into the detector simulation so
that means that the file size per event
is is a bit higher we store more
information uh and this is why we have a
slightly different ratio here although
it's probably quite hard to see um so if
you now draw in all of these predictions
for the different back for the different
processes you have uh these different
colored bars they are just added on top
of each other so the lowest one you have
all of the background processes and the
orange one on the top is your signal
process that you're actually interested
in um and now let's do the same exercise
before so your background only
hypothesis is given by the upper end of
the yellow bar this is what you're
interested in for the background only
hypothesis and if you want to look at
the signal plus background you just add
your signal again up on this so you have
your signal plus background giving by
the up the upper end of the orange bars
again so now what you do is to scale
your signal up and you scale it down and
you see which one Compares fits best to
your data always comparing it to the
background only hypothesis you always
have to compare this to make sure that
the background only is rejected while
you keep your signal plus background
hypothesis
and then you back with some scaling of
your signal process which fits best to
your data and this is then you observed
um um yeah it's not the final one
because for the final full statistical
calculation we don't have perfect
predictions we need to know how
uncertain those are um so for instance
if we simulate our detectors and we
would want to compare this to data we
need to make sure that the data we
simulate the the the detector with the
same conditions as we have when we
measure our data but we don't know those
conditions because we generate the
events before we take our data so we
have to make some assumptions and
afterwards correct for any differences
so then you have some correction factors
and also these correction factors have
some uncertainties with them so what you
do is to change your calibration of a
certain type of object and see what is
the effect on your final distribution so
you really pass it again through the
full chain of analysis through the full
chain of reconstruction to see what is
the effect of the different calibration
and you do that for all kinds of
different effects
we have something in the order of 100
different variations that we pass
through all of that again so your file
size goes up um because all of them we
have to store again uh this is again
roughly a factor of 100 here uh and if
you combine all of these variations in
the end in a proper way you end up with
uncertainty band like that and if you
have the uncertainty band um You can
compare this to data and then you can
really see how much of your signal
process has to be there to explain the
data and in this particular example we
were able in this year to claim the
observation of this process so we had a
sensitivity of Six Sigma or more than
Six Sigma uh we claim an an observation
as soon as we we pass a threshold of
five Sigma um that just means that the
probability of observing something like
the data distribution assuming that only
the background exists assuming that you
don't have a signal that the probability
for this is something in the order of
10us 7
so we're really sure that it's not just
a statistical fluctuation but we can
really reject the background only
hypothesis and we were really able to do
that we are quite comfortable with that
um so we we claimed the the observation
of that
process uh now you can do that for all
kinds of processes and this is summed up
in this plot um this is some preliminary
measurements as well included from the
atlas measurement uh and this shows you
some cross-section we will talk about
that in a minute um for all kinds of so
you always have the theory prediction in
the gray bars compared to the orange
bars which gives you the measurement and
you can see very good agreement over a
huge range of cross-sections note that
the uh the Y AIS is broken down so this
is you just you cannot really just count
the Tex ticks but it really the orders
of magnitude are a bit larger here um
coming back to the cross-section so told
you that this is crosssection this is a
value we use in our theories but you can
translate it just to the probability of
OB observing this process if you uh if
you divide your cross-section that you
observed by the cross-section for the
total proton proton collisions and that
cross-section is shown you on the top
left of that slide and if you now look
at only the process we interested in
this is the one here on the bottom right
side uh and you just ignore the others
for the moment you just divide the two
cross-section by each other and you see
that this is an difference of 14 orders
of magnitude so we see that the process
that we observed now um has a
probability of many zeros and then a one
so it's really 10 to Theus 14 this is
and this is also the reason why we need
so many events why we need to take to
run the LSC for so long because we need
to at least have a handful of those
events to really see what the detector
tells us and to really see with
statistical accuracy that this process
exist and we were able to do that for
the first time here uh also bit further
to the bottom right side you see one
that is even a bit further down there we
had also an observ ation there this year
with a quite similar process and I
personally think this is a a very nice
plot to see how impressive this theory
is because you have predictions really
over 14 orders of magnitude and all of
them are very very well and they all
agree with data and that is really nice
to see um that sums up the main talking
points of the talk I would like to show
you something else uh that is the
screenshot of the public uh status dis
display of the LHC a couple of weeks ago
uh namely four weeks ago we ended our
second major dat data taking period um
and over the next 2 years we will
upgrade our detector and the LHC as well
to perform even better and to take even
more data
and this gives you the opportunity to
really go there go to the LHC and look
at these machines so if you're ever in
the Geneva area drop by sign up for a
guided tour and try to see these
detectors it is really impressive so in
the interest of time I will just leave
you with some further links for further
reading and we would like to thank you
for your
[Applause]
[Music]
attention that was wonderful we have
some time for questions there's four
microphones two in the front and two in
the back so please queue up and we're
going to take three or four
questions
first we have a question from the
internet do you use some sort of deep
learning algorithm to find the most
interesting
events
um for the event analysis yeah this is
kind of The Next Step so in the first
step I showed you that you first sum up
just every event that you see and then
in the next step you would look at each
event to see what the actual kinematics
are in there and then kind of The Next
Step would be to not only look at one
observable but to use multiple of those
and combine them using deep learning or
bdts or any multivar techniques
um it's it's getting more more common
within Atlas uh the problem is that you
cannot really compare this to Theory
predictions so if you want to look at
the distribution something like a BDT
score or something or the output of a
neuronet you cannot calculate this
without having the detector installed so
this doesn't really help theorists in
comparing this to our measured data it
is really helpful in finding processes
so something like that uh could really
benefit from from
um in finding the process so claiming
the the observation there we really need
uh we do multivar techniques um but for
kinematic distributions and so on for
comparing to the theory it's not really
helpful thank you um microphone number
three in the back hi uh you could
mention the open days in September for a
specific spefic date for
visit um no I mean there is no specific
date I me there the period of two years
now where the tectors are closed and in
principle you can just go to the the web
page at shonia visit. CERN um and just
sign up for a public tour so uh they
they offer that I I think they offer the
one years so as far as I know they
almost every day you can just drop them
a line and then you get sort sorted to a
certain slot and then you can just come
and visit does it's as easy as that
maybe a bit in advance not in the
same yes thank you microphone number
one um thanks for this wonderful talk
first of all um I was wondering so the
last really big thing that we as a
public heard um from CERN was the
discovery of the hicko Zone in 20
2012 and from what I heard um uh string
theory is pretty much impossible to
prove with those energies and uh super
symmetry well from what I heard um um
you people have tried to um uh prove
super super symmetry for a while and um
it seems to be pretty much impossible as
well uh so correct me if I'm wrong so my
question is um what what next big
question um are you people at c um
working
on if you what I can so well it's a huge
question right so um as you saw already
in the plot we are doing many many
analyses and we of course also search
still search for super symmetry but we
also S search for any kinds of Exotics
models as we call them like for example
composite Hicks where you um imagine
that uh there are exist other hick bones
that are made of even smaller particles
that we have not yet found so yeah in
principle so we are searching for yeah
we are still searching for all kinds of
new physics I mean we're not only
searching for super symmetry we could
have found it already but it wasn't
there so it's hard to disprove that it
exists but we didn't find any signs yet
um but still we have plenty of physics
work ahead of us with the LHC and the
existing standard model so we have still
quite a few measurements you mentioned
the hix bosone so for instance we don't
know how the hix bosone interacts with
all of the other particles so we have
still some work to do there
um so we we hope we think we can keep
ourselves busy over the next years as
well um but it would be nice to find
some BSM things over the next years but
no signs that I know even more
interesting if you don't know what
you're looking for then you can expect
anything and um that's quite quite
interesting to me as well right thank
you apparently people watching the
stream are just raving about the so
we're going to take one more question
from the internet
people do you have any idea if and how
many interesting events you might have
missed in a discarded data and does
discarded really mean thrown away for
all
time so that depends uh for the
discarded if you mean the trigger um
then if we didn't didn't didn't uh uh
didn't actually do the the measurement
in the detector then obviously we cannot
get this event back so if if the trigger
didn't fire then we don't have the event
um from the as I said we have these
random triggers that essentially or the
triggers that essentially give you
random events to store that um as far as
I know Atlas hasn't really found
anything there uh that's not really our
field of expertise but I mean we also
take into account so everything every
rejection every selection has a kind
certain of certain efficiency right and
also our trigger system has a certain
efficiency so that means you miss you
certainly Miss some of the events that
could have fallen into that trigger but
um we can simulate this as well and at
least what we can simulate we know what
the efficiency is of our trigger and we
can correct for
this thank you microphone
too thank you for your talk I have a
question about your hypothesis test
because you said that you rejected the
hypothy there that it's just background
so you could say okay there has be
something else but could you say that
your prediction is true I mean how do
you handle this can intrinsically never
really say that this is the only
possible explanation so there are always
other explanation that could give you
the same result um that's intrinsically
in science you never can do that so
obviously we cannot do that as well uh I
hope that answers your question
so yeah but we can me we you have to
phrase it in proper way being that you
can reject the background only
hypothesis with that certainty or that P
value or whatever and that was Six Sigma
or something so the background only is
rejected should be or the signal model
is consistent with what we see so it is
very consistent but obviously we cannot
say that this is the only
explanation I didn't hear that
sorry okay thank you one last question
microphone three so make it count okay
um I just have one practical question
after the data is reduced um it's still
very big how do you transport the data
to those who want to work further with
it do you use internet do you use snail
mail or smoke signals something like
that well um the LHC um has its own
Computing grid uh this uh consists of
the data center that I've just talked
about the one at Surin and also the one
at Hungary and then we have this is the
tier zero and then we have 11 bigger
data s sign data centers that are spread
all over the world where the uh yeah
data is transferred to and then also
reconstructed and so there are running
several jobs over the whole world um in
these different data that we attached to
the this this LHC grid so to say and
then usually if if you're looking at
that level where you have that uh where
you starting to the do the first physics
selection where you really need a
cluster or something then it's usually
the case that we send the job to the
data rather than using the data
downloading the data every time so we
can send our jobs to America and run
them there for instance wherever the
data is and this is handled in our grid
uh but obviously this is why we do all
of that pre physic selection that we can
uh reduce the amount of data that we
actually need and to run that on our
laptops or something that is obviously
much more capable and and much
faster thank you thank you for the
amazing talk and please give a round of
applause a huge round of
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