Python in Gravitational Waves Research Communities
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EuroPython 2016121 / 169
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00:00
Coma BerenicesPhysicsCollaborationismPhysicistUniverse (mathematics)Gravitational waveParameter (computer programming)Digital signal processingEstimationElectronic mailing listControl flowPhysical systemTheory of relativityTensorPhysicistGravitational waveGravitationMultiplication signSpacetimeGeometryStudent's t-testPoint (geometry)Virtual machineTheoryMassWell-formed formulaProjective planeGeneral relativityCoordinate systemPhysical systemResultantSignal processingFood energyWebsiteCollaborationismContent (media)Event horizonTheory of relativityGoodness of fitSet (mathematics)Sampling (statistics)System callWeb crawlerAxiomHypermediaSemiconductor memoryMathematical analysisRight angleForm (programming)Greatest elementMereologyKeyboard shortcutComputer animation
03:25
Presentation of a groupGravitational waveSpacetimeDisk read-and-write headMassVector spaceMultiplication signRight angleCuboidArithmetic meanPrime numberPoint (geometry)Lecture/Conference
05:05
Theory of relativityDistanceSound effectIdeal (ethics)SpacetimeMassView (database)Gravitational waveUniverse (mathematics)Universe (mathematics)InformationMassGravitational waveSound effectComputer animation
05:45
CollaborationismRotationGravitational waveSymmetric matrixCartesian coordinate systemSource codeRotationBinary codeEvent horizonExistenceRow (database)Set (mathematics)Computer animation
07:10
CollaborationismBinary fileSimulationEvent horizonGravitationPoint (geometry)Fraction (mathematics)AreaSound effect
07:45
PredictionBinary fileCollaborationismPhysicsPulse (signal processing)ArmMulti-agent systemLine (geometry)Physical systemEstimatorFood energyBinary codeGravitational waveInsertion lossPoint (geometry)Set (mathematics)Natural numberExistenceNumberComputer animationDiagram
08:36
Universe (mathematics)Sound effectEvent horizonCollaborationismGravitational waveWave packetGravitational waveSimulationDifferent (Kate Ryan album)CircleSound effectDirection (geometry)LengthOrder (biology)Software testingSpecial unitary groupSampling (statistics)Wireless LANDialectStatisticsCondition numberPredictabilityComputer animation
09:41
Software testingDiameterAtomic nucleusDisplacement MappingEndliche ModelltheorieCategory of beingAtomic nucleusDimensional analysisSummierbarkeitCASE <Informatik>DiameterError messageComputer animation
10:15
DistanceMeasurementDisplacement MappingMoving averageCollaborationismDisplacement MappingSoftware testingOscillationMassPosition operatorVelocityAdditionVideo gameAngleBitMoment (mathematics)Gravitational waveArmDirection (geometry)VideoconferencingComputer animationDiagram
11:40
CollaborationismState observerMereologyMathematical analysisConfiguration spaceSensitivity analysisInsertion lossSet (mathematics)Power (physics)Raster graphicsComputer animation
12:46
GravitationCollaborationismNoiseMultiplication signGravitational waveMatter waveWärmestrahlungVibrationNoise (electronics)View (database)Point (geometry)Reduction of orderCellular automatonRadical (chemistry)WordComputer animation
13:35
CollaborationismNoiseNoise (electronics)OpticsChainFrequencyMassCellular automatonObject (grammar)Vector spaceFactory (trading post)Natural numberComputer animation
14:39
CollaborationismOpticsPrice indexModule (mathematics)System callVector spaceVacuumMereologyComputer animation
15:19
NoiseGame controllerConnectivity (graph theory)Noise (electronics)Multiplication signBuildingOpticsPhysical systemDemo (music)1 (number)Content (media)
16:17
WebDAVWide area networkMenu (computing)Binary fileVideoconferencingInjektivitätDivisorPhysical systemBasis <Mathematik>Forcing (mathematics)Arithmetic meanClosed setArmOpticsBuildingComputer animation
16:55
Metropolitan area networkMenu (computing)Interior (topology)Wide area networkGravitationGravitational waveCellular automatonHand fanCollaborationismPredictabilityNoiseSimultaneous localization and mappingWärmestrahlungAddressing modeSensitivity analysisCurveMaxima and minimaHill differential equationGravitational waveAdditionMereologyPerformance appraisalNoise (electronics)EstimatorField (computer science)Sensitivity analysisSound effectArithmetic meanPlastikkarteOpticsState of matterDecision theoryPredictabilityInjektivitätSimulationLIGONumbering schemeLink (knot theory)View (database)Event horizonoutputPiSource codeComputer animation
18:39
Raw image formatControl flowServer (computing)Level (video gaming)GUI widgetAutomationGame controllerPoint (geometry)OpticsProcedural programmingVideo gameQuantumCausalityMachine visionPhysical systemDifferent (Kate Ryan album)Computer animation
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Gravitational waveEvent horizonData analysisAstrophysicsSurfaceNoise (electronics)Point (geometry)Gravitational waveMultiplication signPhysical systemNumberLecture/Conference
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FrequencyRotationMusical ensembleCompact spaceAddressing modeCollaborationismCollisionEvent horizonRotationFrequencyNoise (electronics)Source codeFood energyMassMultiplication signAffine spaceDifferent (Kate Ryan album)VarianceWeightSignal processingWater vaporMathematical analysisAnalytic continuationPresentation of a groupNumberLogical constant
21:33
NoiseDistribution (mathematics)Mathematical optimizationDigital filterBitWell-formed formulaThresholding (image processing)BitMathematical optimizationWell-formed formulaNoise (electronics)Water vaporGraph (mathematics)Food energyGraph coloringSpectrum (functional analysis)DemosceneWeightEndliche ModelltheoriePower (physics)Population densityIntegrated development environmentMathematical analysisStrategy gameForm (programming)Template (C++)MathematicsThresholding (image processing)Perfect groupMatching (graph theory)Boundary value problemComputer animation
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Group actionCollaborationismExecution unitPiMatching (graph theory)Template (C++)CodeNoise (electronics)Game theoryVideo gameContent (media)SpeciesMultiplication signDemosceneNetwork topologyComputer animation
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Ring (mathematics)Library (computing)OvalWaveformCollaborationismSpiralTemplate (C++)Parameter (computer programming)SpacetimeWaveformBinary codePosition operatorMechanism designLibrary (computing)MassSet (mathematics)Similarity (geometry)DemosceneObservational studyWeightEinbettung <Mathematik>Computer animation
24:57
NoisePopulation densitySpectrum (functional analysis)Power (physics)CollaborationismPersonal identification numberSignal processingSpectrum (functional analysis)Noise (electronics)EstimatorPopulation densityNatural numberSet (mathematics)SummierbarkeitComputer animation
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Addressing modeBuildingWaveformCovering spaceSpacetimeParameter (computer programming)Insertion lossPointer (computer programming)Medical imagingSpacetimeParameter (computer programming)WaveformPrice indexMultiplication signMusical ensembleNumberUniformer RaumBitDemosceneVideoconferencingEvent horizonComputer animation
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NoiseFrequencyAddressing modeCollaborationismSoftwareEvent horizonInterrupt <Informatik>DemosceneWaveformProcedural programmingEvent horizonGravitational waveNormal distributionDistribution (mathematics)Interrupt <Informatik>Multiplication signNoise (electronics)AlgorithmProcess (computing)Domain nameMusical ensembleDiagramComputer animation
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WaveformNoisePower (physics)Template (C++)Generic programmingWaveletGravitational waveEvent horizonValue-added networkSurfaceNoise (electronics)WaveformSystem callHelmholtz decompositionSource codeWaveletDifferent (Kate Ryan album)Ocean currentQuicksortDemosceneLogical constantComputer animation
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FrequencyNoiseRange (statistics)Condition numberGravitationCross-correlationGravitational waveCASE <Informatik>Mathematical analysisMenu (computing)NumberSystem identificationNoise (electronics)NumberFormal grammarDifferent (Kate Ryan album)Survival analysisPresentation of a groupForm (programming)Computer animation
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Gravitational waveGravitationCodeInternationalization and localizationGamma functionSoftware bugMenu (computing)SoftwareHydraulic jumpWindowPosition operatorHypermediaSound effectElement (mathematics)DistanceVector spaceSpacetimeForm (programming)Gravitational wavePoint (geometry)Formal languageInformationMultiplication signDirection (geometry)CASE <Informatik>CodecSource codeGodAreaEvent horizonLink (knot theory)Triangulation (psychology)LaptopEstimatorLocal ringResultantError messageComputer animation
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Projective planeLine (geometry)Field (computer science)CollisionGravitational wavePredictabilityLIGOEvent horizon
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Food energyGravitational waveGeneric programmingPredictabilityCollaborationismEvent horizonWaveformEmailProcedural programmingPhysical systemDemosceneMereologyExecution unitDeclarative programmingGame controllerComputer animation
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PredictabilityCollaborationismArithmetic meanLie groupTime domainPredictabilitySign (mathematics)Analytic continuationTemplate (C++)Line (geometry)Universe (mathematics)Multiplication signFrequencyComputer animation
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IntelCollaborationismAddressing modeMenu (computing)MassEvent horizonDistanceTotal S.A.Electronic data processingEvent horizonMultiplication signStatisticsMassFrequencyWaveformDistanceBitMathematicsNumberDifferent (Kate Ryan album)Internet forumDatabaseEuler anglesVelocitySpecial unitary groupElement (mathematics)Insertion lossDemosceneComplex (psychology)ImplementationSampling (statistics)Point (geometry)Computer animation
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Software development kitScalable Coherent InterfaceIntegrated development environmentNoiseSystem identificationMathematical analysisLibrary (computing)CodeGeneric programmingWaveletMachine learningAddressing modeCollaborationismMetropolitan area networkFrequencyField (computer science)Noise (electronics)Library (computing)Gravitational waveMathematical analysisEvent horizonIntegrated development environmentNatural languageVirtual machineSignal processingSoftware developerVector spaceNumberComputer animation
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CoefficientSpacetimeParameter (computer programming)Raw image formatScalable Coherent InterfaceSoftware development kitQuantumSocial classVirtual machineFrequencyMultiplication signDifferent (Kate Ryan album)Vector spaceDemosceneFunction (mathematics)WaveformSocial classComputer animation
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Addressing modeConditional-access moduleFormal languageCollaborationismSigma-algebraEvent horizonDigital signalUniform resource locatorOpen setWeb pagePhysical lawSpecial unitary groupSignal processingOpen setEvent horizonLink (knot theory)Revision controlSet (mathematics)Computer animation
37:24
LaptopAddressing modeCollaborationismEvent horizonMenu (computing)Digital signal processingOpen setMetropolitan area networkFile formatNumerical analysisTemplate (C++)Theory of relativityRadio-frequency identificationMaxima and minimaNoiseMathematical analysisAstrophysicsParameter (computer programming)Musical ensembleLine (geometry)Raw image formatSeries (mathematics)Maß <Mathematik>WaveformSimultaneous localization and mappingLevel (video gaming)PlotterDifferent (Kate Ryan album)Event horizonLine (geometry)Power (physics)FrequencyFrame problemNoise (electronics)Source codeWaveformDomain nameSignal processingSpectrum (functional analysis)Multiplication signPopulation density2 (number)Term (mathematics)Library (computing)File formatSensitivity analysisSimulationProcedural programmingSound effectInheritance (object-oriented programming)Incidence algebraSet (mathematics)Information securityMereologyAnalogyGame theoryGoodness of fitPhysical systemComputer animation
41:17
NoiseSpecial unitary groupInternet forumScripting languageMetropolitan area networkCollaborationismPredictabilityGravitational waveGravitationEvent horizonAddressing modeMenu (computing)Execution unitUniform resource nameComputing platformCellular automatonGroup actionFormal languageStaff (military)Scripting languageComputer animationSource code
41:52
Form (programming)Operator (mathematics)Sensitivity analysisFamilyHypermediaMultiplication signSignal processingSpacetimeService (economics)Event horizonComputer programmingPhysical systemArithmetic meanGravitational waveLevel (video gaming)Presentation of a groupSemantics (computer science)InferenceMereologyCivil engineeringOscillationPropagatorSoftware testingMassAsynchronous Transfer ModePhysicalismSymmetric matrixFrequencyTheory of relativityComputer animationLecture/Conference
Transcript: English(auto-generated)
00:00
So welcome to the next talk of this session it will be about Python in gravitational wave research communities and Ladies and gentlemen, please. Welcome our speaker Elena Coco Good morning, everybody. Thanks to be here to for my talk of Python in gravitational wave communities
00:25
Before starting something about me. I am a physicist. I working as a data scientist at European Gravitational Observatory In Italy, I'm a member of LIGO Virgo collaboration and I'm also the scientific coordinator of European project graviton
00:42
Which is the aim to train 14 PhD student in Europe I am also a machine learning passionate So during my free time I participated to Kaggle competition and I am also in science outreach Passionate this is me while dancing with my colleague during an outreach event at Virgo site
01:06
Why gravitational waves you Have heard that this year. We gave the announcer the first detection of gravitational waves a new era just started in September we
01:21
Made the first detection of gravitational waves in December We made the second detection of the gravitational waves So I'm here to try to explain why? Gravitational waves in this event was so important Spoiler alert, sorry, but this is a spoiler of the keynote talk of tomorrow my colleague James
01:47
That is here some warning warnings About my talk in 45 minutes. I will try to explain everything On the gravitational waves, so it will be almost impossible So you have question I'm here during these days and also today
02:04
This talk is meant for beginners, but I can avoid to Introduce technical details while explain everything why you're at a robot on Because we use it also Python to achieve these results in everyday
02:21
Working activity in our labs we use a Python in control room doing senior processing of controlling our system I try to while explain What are gravitational waves how we detect them? I try to Point to all the Python usage we made
02:42
You know Virgo and LIGO and for sure it is not excessive least over there The Python usage, so let's start what is the challenge first of all what are the gravitational waves and how we discover them? so in 1915 this guy that might be you know Albert Einstein
03:03
Introduced that the theory of a general relativity. He said important things they say that The geometry of a space-time is linked to the content of a massive energy of the space-time So there is this strict relation that is so expressed in this beautiful formula that links the geometry to the mass
03:22
in some way mass energy, so Just a little joke if you want to play with me, so I need a some volunteer Don't be afraid you have only to keep in your head the space-time. Please come here so come I
03:41
Have the space-time Please Come on I need the other one So keep it as flat as possible So this is the space-time in some way so you can think and in the absence of any mass
04:02
It is flat, but if you have a very massive body That is in the space-time It Became scar. This is what I succeed if There is a present of a massive body in the in the space-time It curves the the space-time sir, but I will try this is an experiment if
04:27
There are also mass that moves in your flat space-time. You can see that maybe you know They can see
04:41
a Little deeper that are Creating the space-time that moves in the space-time itself. These are the gravitational waves. Thanks And that's what we're trying for many years to detect The research of a gravitational wave detection has started many years ago
05:04
I Said I wrote this article in just 100 years ago The the problem of the gravitational waves that they produce a very tiny effort in the space-time so the challenge is to detect this small effort, but as a
05:25
Side effect the fact that they interact so little in the with the mass Can help us in understanding much more about the universe itself because it They can bring information that otherwise we cannot access
05:46
so since they are so Small so tiny We should think to astrophysical phenomena So that massive body that I show you should be very very big
06:00
So we should think to star very massive star I Show here the main source of gravitational waves that we expect from There are the rotation rotating in the other star the so-called pulsar that while rotating around its
06:20
Symmetric Symmetrically around A Axis can produces producer gravitational waves there is the violent phenomena that are called the supernova when there is an implosion of a big Star and during this phenomena it can produce
06:44
Very intensive gravitational waves Then there are the the event that we detected that is the black hole colliding The collection binary the compactor collection binary and I will concentrate on my talk around this phenomenon and then we
07:01
Taught on also to the existence of a gravitational wave background the one that is remnant from the Big Bang So this is a simulation of the Phenomena that we detect these are two black holes that rotating one around the other and
07:22
Why? They rotate they become closer and closer because they are losing energy and that at some point the Gravitational attraction is so intense that they collide and then that was the event that we detected in September
07:45
so why we were so confident that the gravitational wave exists because we Are looking for them for many years because in 1993 these two guys won the double the Nobel Prize
08:01
Because they prove in either indirect way the existence of gravitational waves they observed for many years to a binary system the energy loss by this binary system and Estimate the quantity of energy that can be
08:21
Lost as gravitational waves and these are at the point are the Mission they made while the blue lines is the prediction and as you can see the feet is almost perfect so we know they exist and We try to detect them. I
08:41
Will skip the first experiment and I will concentrate over the recent experiment how we can detect them we can Use the effect that they made on a free mass That the cane they can eat while they passing through. So this is a simulation obviously and while a
09:01
Gravitational wave eat somebody can stretch in one direction and elongate in another direction. This is a schematic effort on a tennis ball if we think to some test must put around the side a circle while they are Invested by this gravitational wave it can start oscillating and we can detect this is more
09:24
Difference in length About the length itself and this is what we call the strain of the gravitational waves the problem with this train is that is a very very small it is the order of 10 to minus 31 just to let you let you understand of which are the
09:44
dimension we are talking of If the diameter of a human hair is a 10 to minus of 50 feet 5 meters Sorry, the diameter of an atom is a 10 to minus 10 meters the one over the nucleus is a 10 to minus 14 meters at the diameter of a proton is a 10 to
10:02
minus of 15 meters and we are trying to detect a small Displacement that is a 1000 over the dimension over the diameter. So the Proton so this is this was our challenge And now in which tool we use it to detect this is small
10:22
Displacement we use the module interferometer. We we think to this schematic Position of some test mass and we try to detect this small displacement using this oscillation of the test mass and
10:41
They get advantage over the phenomena of the interference over the the laser I Will explain better using a video that one of our colleagues colleagues producer This is a lacer that was sent in To through a beam splitter. So the first mirror that it occurs is the device the lacer in two
11:07
Direction the one that are the two perpendicular arms of our interferometer. So this allows go back and forward while the gravitational waves It the interferometer it
11:21
Start moving the two test mass so we can see light here or don't see Following the movement of this mass. So this is this was the idea we can detect this small movement by Looking at the light that Appeared at the end of the center for a meter, but this is a schematic
11:45
You know, we add an extra mirror in both beams in this configuration The Aspirin is much more complicated than the one that I show obviously to have a better and better sensitivity we want to that this lacer make many part you in a
12:06
What-we-call cavity better subject of cavity. So the light became more intense the part that the lacer made is longer, so the sensitivity at the detection
12:20
Bench will be higher then there are a father Mirror that are part of the optical setup that are passed to clean as much as possible the raster itself to Augment them to make higher the power inside the interferometer and that more or less this is the how it works, but
12:45
This is the ideal worth The real world is that we have a noise that is much more higher than what we are looking for so we know that many other things that can move this mirror that are not the gravitational waves, but are
13:03
Seismic noise that are a thermal noise that are due also to the hair that the lacer can meet during this travel so we should take care of Reduce as much as possible the noise we can do this
13:22
from experimental point of view so while projecting our detector or in us When we analyze our data, so I will show how we do this The first accuracy that the first care that we put in reducing the noise was
13:44
To reduce as much as possible of the seismic noise So you see our heart is so continuously moving. So all these optics were shaken by the seismic noise and We want that the mirror should be
14:03
Keep at rest at mass as possible. So we use this Instrument that we call a super attenuator. The mirror is hunged to this chain and all the optics in Virgo but also in LIGO detector are suspended in such a way that they can be
14:21
Can be considered In The same place as much as possible so we know that what we see is not Seismic noise at least at some frequency one more
14:42
Think that we made a so to put all the optics in under vacuum because we want that the lacer go through The cleanest possible part so all the optics are were Covered put in a tank that this is the real and this is the tube with a Virgo tube
15:03
And we are here. We have one in Europe one of the biggest the biggest Vacuum experiment under vacuum experience much more than the sir other
15:20
I Think that we have cured is the thermal noise. We know that All the components all the optical components components can cause thermal noise because the molecular that compose our Component can move due to the
15:40
Temperature and this can cause thermal noise. So we take care of this Building Mirrored very special mirror. These are an example of the advanced Virgo mirror These are made of a particular material. These are silly silly material
16:00
they are very heavy a very very big because they able to Beat and by the the lacer many times and With this we try we try to keep under control the thermal noise This I will show now how to looks like in reality Virgo
16:23
This is a video made by my colleague with a drone. This is a Virgo. This is in Italy This is a closer to cash, you know The this is what we call the male building all the optics all the injection lacer the injection lacer is here
16:41
These are the two arms over the terra meter the three three kilometers arms at the end of this tube you find the day the two and mirror and The lacer go through this tube go back and forward and then recombine in the central building. So
17:16
Let's come to the main topic of this talk. It is
17:21
Python in gravitational wave communities why I'm here talking of Python because as I said we use a Python in many many Field of our research as I say the Virgo LIGO are very complex instruments. This is a schematic view of the optical
17:40
Scheme that I showed before the two and the mirror the input mirror the injection lacer and we as I said there are To control a lot of noise that can Make worse our sensitivity at the end We came out with what we call a sensitivity card, so we know how much we are sensitive to gravitational waves
18:04
By looking to this card if we want to detect the event This should be in some way higher than our noise So we can do prediction estimation of our noise and see how much we are sensitive One of the tool that we use it. For example is the simulation
18:23
We have some optical simulation to know how the optics behaves This is what's written part on it. This is a pie cut The I put everywhere the link and you can go there and have a look This is a first Seijo Python, but this I say that we
18:43
Want to control as much as possible everything in our thermometer So the mirror we have to keep the interferometer lock it that is what that is a working point So we don't use only what we call passive Control our optics but also active control. So there are many things
19:03
That are you said in control room and are based on Python. This is This is shortly. So what we use in life in a vehicle. Sorry we started also to write a documentation of all this package and Many of these are used daily in our control room
19:22
So there are automations of the locking procedure that were done using the Python But now came to that on How we can extract the signal from our noise because this is the point we build an instrument
19:40
We we know that we can detect Gravitational wave using this instrument, but how we make can make this what is it? First of all a gravitational detection we have noise We have a surface of a signal and we know that this signal that are time serious We have even a signal and that is much more smaller that they know self. We have to extract this
20:07
The Astrophysical sources that I showed at the beginning produce a different signal The Rotating neutral star produces what we call continuous waves. These are these are periodic signal These are presently continuously in our data with a given frequency. So this is senior should be there
20:28
for all the The run that we made while acquiring data this signal if is there is there continuously While there are other senior that we call transient senior there are very short transient senior that are due to the supernova events
20:44
we have a lot of Release of energy in a very very short times the order of millisecond then there are the collection bias That are always transient because they start with this Rotation one around the other and then collide and so this can last from some millisecond to some second
21:04
It depends on the mass of the phenomena And then we have what is we call broadband senior that can be due to the stochastic background. This is simply a Noise in some way that should be
21:20
different of some some some way Below below our our senior. So it is very difficult our noises. Sorry. So it is very difficult to detect In the ideal world we have noise and signal That are summoned and in ideal world our noise is a it's a good
21:44
It's nice. It's a Gaussian and this is a stationary in this ideal world exists in Optimal filter to detect the senior so a bit of formula Obviously, I we go I want to go through this rush on this
22:02
Formula but the idea is that with this characteristic We know what what will be the best way to a strategy signal So if this is the data that came out from our instrument that we have The noise and the hypothetical signal we try to match our data with a template
22:23
We for example, I'm talking of a collection boundaries We know theoretically how it could be this senior What the way form of the senior itself? So we try to match a template with our data weighted by the power spectral density by the noise
22:43
This is a formula that is derived in a perfect way from mathematics What do we can do? So if we have a Seniorly template and we do this match we can find Say that we found a signal if this quantity is above a threshold
23:04
Maybe it's a clearer with this is a simulation. This is the senior that is hidden in the noise This is our template. This is a moving Along the data while it encountered the the real senior and it to match the exact way for more
23:21
We can see this speaker. Okay, we detect a senior so if the Match is perfect or almost perfect. We know that we have a trigger in some way in our data and also these Pipeline in some way
23:40
Was written in in Python. This is a pi CBC This is the documentation of our LIGO colleague and all this Code is in the tab so you can go there and have a look at the code itself So
24:02
So the idea of this Mechanism to detect the senior is to build a template bank So we don't know we know what is the waveform that we are looking for? But we don't know the parameters of this waveform. The parameters are linked to the mass of the
24:22
binaries to the position to the fact that the star maybe are spinning so we have a Very large parameter space That we have to span to find the exact template or our phenomena So we can in some way simulate the signal to produce this template and also this was done
24:43
Using Python in some way because we have a C library that were embedding in Python this called Pylal and we simulated the the waveform using this library We estimate this important quantity that is the senior to noise ratio for
25:04
Some of you that are doing a senior processing know what it is. It is the Estimate of our Your signal is higher than with respect to your noise. So, you know how intense is so your senior give some way this
25:21
quantity that is the amplitude of the signal self-awaited by the Spectral density Why I introduced Lisa because when we build our template banker we say the okay we build a template banker Taking account the fact that we don't want to lease to lose much more than the
25:41
3% of its senior to noise ratio. So for example for the detection of the event in September we end up with the 250,000 waveforms so you can image how many times this symmetric filter was done to produce The real senior and this is the parameter space that we can span using this number of waveform
26:07
So till now we'll talk about the senior the instrument and the way in which we can extract the senior in the ideal world, but The Detector noise is not so ideal as we want it is a no stationary
26:24
That means that it is not the same while passing the time. So After some minutes it can change. It is not the Gaussian. So it is the distribution of this data are not a perfect Gaussian distribution and can be
26:45
Contaminated by the presence of many spurious events there are many things that can mimic in some way Gravitational waves because as I said the supernova could produce a gravitational wave We don't know exactly the waveform. So it's simply a glitch that we can see in our data
27:02
so we should take care of Clinics as much as possible our noise before They try to detect something inside it There are many packages that rely on some Python which we use it to do this procedure GW pi GW pi soft a lot the chart pile and pineapple there are their
27:24
Algorithms that we use to clean the data And this is important because what I show is the example which we know the way for more But what happened if the noise is not as ideal as we want if we don't know anything over there over there
27:42
Or the signal surface. So we should use what we call a trigger generator that are Generic so we look for a transient signal is our data that are simply find the excess of Signal in the data that can be due to different source of noise or of signal
28:06
by the way, the first pipeline that triggered the signal in September was one of these Generic tool these call it the current way bars and it is based on a wavelet decomposition of the data
28:20
And this is how it looks like that signal. So this Different pipeline are important for noise Characterization because we use this pipeline. So generic one just to find the different glitch that are in our data This is it what is called a glitch grammar So the number the glitch that are present in our data. So many of these
28:47
You can figure our signal but obviously they are noise and we should identify each of them to be sure that Our know our signal was not one of these We are a network. We are
29:03
LIGO instrument to detect the signal, but we are a network There are the two LIGO in the USA, Virgo in Italy, Jio in Germany There is a KAGRA that is almost Operating there is the was approved the LIGO India in the next year, but why we are a network
29:26
because the gravitational wave Detector are not as the standard telescope You cannot point your detector in some direction of the space To look for some signal if you want to know to the source
29:44
the position the source position of your event you need to do the triangulation of the the result so you can Take consider where the interferometer are the traveling time that the wave that the gravitational waves
30:04
Were detecting the different detector and in this way you can in some way have information on the position of the the source this this was In some way the error of the position in the sky for the event we detect and
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To do this kind of a localization on the sky. We use always Python some way there are some Link where you can find this Notebook and the tutorial to apply this
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estimation of the position why we are network because Much more detector are much more is will be the will be the precision of the estimation of the position of the source So when they also will go kagra will be operating at this
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Big area will can become this small area in the sky. That's why it is important To be a nectar. So come to the event The gravitational wave have been detected So it it for many years It seems almost impossible or also for people that are working in this field and
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I'm sure that many of us when Ever day alert No one believe that was a real event Because we were very surprised by the fact that it was so beautiful as I will show you it is
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almost perfect to the prediction So we have this guest star of 14 September but also in December during the first scientific run of LIGO There was another event that were detected always they call colliding This is the event in three minutes
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After the data acquisition, we have the alert. There was an email this go around and say, okay There is a strange event in our data. Please. Have a look at the data better and it's after the procedure that Conduce that let us give the announcement in February
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So these are the the waveform of the two Of the event to seen in the two different detector in and for the Livingston this is how to look sign time domain the Line continuous lie that is a super imposter is the
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Prediction the template that much at the best of this a senior so as you can see, it's almost perfect And I want to show you this is the famous Cheer for sound You can you can hear No, there is no sound
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We Call this kind of senior cheaper just for this sound that you can hear because there is a decent frequency that is Became higher and higher during the time it produces a beautiful sound
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for our The one that detected we detected in December was a bigger differ a bit different I say always to black holes, but with the smaller mass So it was detected directly by a pipeline. That is a bicep symmetric filtering
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The the waveform is always the same It is a frequency that the change in time with this a big peak at the end of the phenomena So this is our few numbers about the detection. So the first event
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As a very big signal-to-noise ratio that was why it was so evident in our data The second ever there is a little Smaller signal-to-noise ratio. These are the distance so 1.3 billion of light-years and the 1.4
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45 44 billion light-years and The solar mass so you can see are different for the one are very very big So these are very compact black coal 36 and 29 for the second event of 14 and the seven There was also another event that were considered a candidate event during the
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October but the statistics was not so good to to make us claim another detection So coming to my personal experience working in this field I am senior processing
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Researcher so I am a dot analyst and I work as mainly as For noise characterization. So I'm one of the one I'm one of the person to clean the data before the detection In Virgo, we developed a decent library. That is a noise analyst packages a c++ library
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That were embedding in Python or using swig and now we have this a fine-up genetic Noise analysis toolkit. I developed this event trigger generator that it was buzzed on the wave letter that can be used adjusted to detect the noise and
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In the last period also trying to using the machine learning tool to classify the noise senior These are the this is the environment in which we work Using a Python scikit-lin as I p-numpy and you know all of these Just to show what we did. This is these are the typical
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output our detector So these these are the data also came out came out from the detector These are the same data after the cleaning good the whitening the so-called whitening and you can see that there are Mr. Two peaks here that in time frequency appear in this way
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So the idea is to have a look at this kind of waveform and classify the senior Let the noise senior in some way and we did this using machine learning technique that Separated the senior in different class just
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Trying to fit in different way the waveform of the senior So I in the abstract I say that we can have a look at to the data there are LIGO produces this Like open Sun Center, you can go there. This is the link There is you can download the LIGO data. You can play with this LIGO data. There are beautiful tutorial
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now there are two beautiful tutorial because they do did the same with the second event where there are some Senior processing technique will describe there and you can play
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See how it works. Maybe we can try now. I prepared a short version of this So hoping it works. I downloaded the data on my PC So you can recognize there many of the Python packages that maybe all of you use
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and Plus some senior library of SAP That were used to prepare filter The data were preparing and the format
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HDF While in Virgo and LIGO we use a different format to save the data that are What we call frame data. I've been time and you can Also load the
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Simulation waveform the waveform that it was simulated we you can have a look at the data. This is How it looks like your data in this these are the some second of data around the event So in this data the two detector to refer a detector in somewhere
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Is it buried under the deck the event so you can see that it is important possible to to identify some events here but if you use some Senior processing technique for example the so-called the whitening
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What is the whitening? These are the data this power spectral density of the data That is similar to the sensitivity sensitivity Corvo that I showed at the beginning this we call this noise So our noise is not flat is a full of feature full of lines that are due to many
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Source of noise can be due to the 60 X power in Virgo in LIGO and the 50 X power in Virgo because in Italy there are many Lines that are due to the thermal noise the movement over the over the Wire that are suspended the mirror. So some of these lines are very identified
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but if we estimate this power spectral density and Apply the watching that is the first procedure of this power spectral density We divide in some way the data by this quantity
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just do and then Plot The white the whitening data. This is our event So just simply whitening your data without doing strange things you can Identify your event. These are the two strains at different detector that in the black. There is the
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simulated matched waveform the one that triggered So the same if you look at in the in the time of frequency domain Sorry, I don't know if how many of you know these terms
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These are out looks like the time frequency plot The senior was there but you cannot see without to do anything to your data But if you apply again the whitening and you produce the same plot Here it is you're seeing so many, I don't know if it is so evident also for you
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Yellow This is the so-called the chirp, you know in your data so I'm almost ended
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The data are now also on Kaggle I don't know if some of you will play on this platform So you there is a there is some portion of this data there are some script that you can use a directly there without downloading the data if you want to create your Python script or in
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The long discrete the language that you prefer you can play there and that's it So
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We might have time for just one question one short question Before lunch. We have one The question basically question is what are the gravitational wave on a physical level
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What is the relation gravitational wave at the physical level? Gravitational wave at the physical level, I think the beginning of the yeah the physical level Okay, I don't know if you miss the first part of the talk. I show
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What are gravitational wave it is a very tiny Oscillation over the space-time so your space-time in some ways moving While I also now I'm producing gravitational waves because I'm a master. I'm moving and while I'm moving I'm not
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Symmetric because you need also that your mass are asymmetric while moving and this can perturb your space-time It's any more flatter but produce this is more oscillation on your space-time self that Propagated through through all the space-time. They can reach yet or your
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yourself Okay, I have one question very quick. Is it true that the first event was Detected when they were basically still testing the this is true So it was not operation No, it was operation. It was during what we call engineering runs before the starting of a scientific around
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So usually there is a period over some days before that We use it to test that everything is working So we are quite data, but we officially are not in science mode Whatever since mode but it was during everything was running as it was in science
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Run because the pipeline were under test and we saw this You were expecting to see the first event in one year probably and you get it for you No, we expect to be honest. We expect that with the LIGO sensitivity to it was probably to be detecting heaven till this year, but not
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We did it was a real and expected for us, okay. Thank you very much again
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