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Milagro MC simulation & analysis. Vlasios Vasileiou Milagro Collaboration Meeting Los Alamos, 05/10/2006. Latest improvements in the MC. New Corsika 6.500 (Fluka 2005, NeXus 3.97) Mie (forward) scattering in water Improved analysis (PMT corrections, noise). Corsika.

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Milagro mc simulation analysis

Milagro MC simulation & analysis

Vlasios Vasileiou

Milagro Collaboration Meeting

Los Alamos, 05/10/2006


Latest improvements in the mc
Latest improvements in the MC

  • New Corsika 6.500 (Fluka 2005, NeXus 3.97)

  • Mie (forward) scattering in water

  • Improved analysis (PMT corrections, noise)


Corsika
Corsika

  • Variety of hadronic physics models available

    • We use

      • Low energy hadronic model

        • Fluka 2005

      • High energy hadronic model

        • Nexus 3 (started using it in the latest Corsika v6.500 with g4sim v2.0). Previously used Venus

neXus (NEXt generation of Unied Scattering approach) is a common effort of the authors of VENUS and QGSJET with extensions enabling a safe extrapolation up to higher energies,using the universality hypothesis to treat the high energy interactions . It handlesnucleus-nucleus collisions with an up to date theoretical approach.


Water properties
Water properties

  • Water absorption and scattering lengths

    • g4sim v2.0

      • Has both Mie and Rayleigh scattering

      • Two configurations were mainly used:

        • Absorption Length 27.4m and Scattering Length 56.8m (Att. Length 19m). Source was an email from Don Coyne to the milagro mailing list dated 5/4/2004.

        • Absorption length 30m and Scattering length 50m (tried with a bit more scattering)


Water properties1
Water properties

  • Rayleigh Scattering – caused be scattering centres smaller than λ/20

    • Rayleigh scattering length increases with λ4 and scattering angular distribution goes like ~(1+cosθ2).

    • Forward and backward scattering probability equal.

    • Rayleigh scattering very long

    • Geant4 has code to simulate Rayleigh scattering. Calculates the lengths just for water using the Einstein-Smoluchowski formula


Water properties mie scattering
Water propertiesMie scattering

  • Mie Scattering

    • Geant4 doesn’t include code for Mie scattering, so I wrote some code to simulate this process

    • Mie’s angular distribution function is hard to solve

    • You can approximate the Mie complicated angular distribution functions with a simple function: the Henyey-Greenstein function

    • Needs only one parameter; the asymmetry factor (g=<cosθ>)


Differential (top) and integral (bottom) plots of the Mie scattering angle distributions for different asymmetry factors.


Mie scattering in g4sim v2 0 mc
Mie scattering in g4sim v2.0 MC scattering angle distributions for different asymmetry factors.

  • In the March 2004 Los Alamos meeting Don Coyne presented results of his water scattering measurements.

  • Showed a plot of relative intensity vs scattering angle

  • His results would agree with a Henyey-Greenstein function of <cosθ>=0.9999. Extremely forward scattering.

  • For the g4sim v2.0 MC, I used <cosθ>=0.99


Pmt tests
PMT tests scattering angle distributions for different asymmetry factors.

  • I tested some Milagro PMTs

  • Mainly, made pulse height distributions for different conditions and measured the relative detection efficiency on the surface of the PMT

  • PMT gain and detection efficiency decrease as we illuminate points far from the top of the photocathode.

  • I sent a memo out (5/1/2006) about these tests.


Pmt tests1
PMT tests scattering angle distributions for different asymmetry factors.

Pulse height distribution for illumination at different positions of the photocathode .

PMT #1024, HV 1800V, PMT vertical


Pmt tests2
PMT tests scattering angle distributions for different asymmetry factors.

Relative detection efficiency

Normal illumination at different points of the photocathode.

PMT #1024, 1800V, vertical position


Milinda s initial processing of the mc event noise addition
Milinda’s scattering angle distributions for different asymmetry factors. initial processing of the MC eventNoise addition

  • The MC event is read by DataRead_MCASCII5

  • AddNoise code superimposes noise to the events

    • Cosmic ray noise

      • Simulated 5 GeV – 100 TeV , 90deg zenith angle protons thrown uniformly on the pond with cores distributed to +-5km. Kept events with any pmts hit and at most 10AS PMTs hit. Saved in the config_milagro/noise.dat file

      • Milinda adds, randomly in time, events from this file until the hit rate of the AS tubes becomes 20KHz.

    • Dark noise

      • 2KHz for Pond PMTs and 20KHz for Outrigger PMTs.

    • There maybe another source of single pe hits in our pond (noise, reflections?).

      • Relative rate of muons to single pe hits doesn’t seem to agree between MC and data for these noise rates.


Muon peak
Muon Peak scattering angle distributions for different asymmetry factors.

  • Number of pes a muon produces on the MU layer PMTs

Muon peak from data

Move the time window of the edge-finder off time, where the pond hits come from other unrelated showers.

Muon peak from the MC

Analyze non triggering data


Milinda s initial processing of the mc event pmt gain and efficiency corrections
Milinda’s initial processing of the MC event scattering angle distributions for different asymmetry factors.PMT gain and efficiency corrections

  • Milinda code was written to apply the PMT efficiency and gain corrections.

  • In g4sim v2.0 the position (distance from PMT axis) of each pe detection is saved in the output file.

  • Milinda’s code GetPEAmp.c

    • Discards some of the MC pes based on the position dependent PMT efficiency

    • Assigns a pulse height to the surviving pes using the pulse height distributions as a PDF. (Does the same for the dark noise hits, using a dark noise pulse height spectrum)


Milinda s initial processing of the mc event calibration
Milinda’s initial processing of the MC event scattering angle distributions for different asymmetry factors.Calibration

  • CalibrationMC2 then simulates the waveforms out of each PMT

    • For each PE it produces an exponential with amplitude the pe’s pulse height.

    • It superimposes these exponentials for each PMT

  • Then it performs edge generation and edge finding

    • Finds when the output waveform from each PMT passes the low and high thresholds

    • It assigns a TOT for the edge combinations that look like real hits

    • It calculates the number of pes that correspond to these TOTs

  • Adds a time jitter

    • Gaussian with pulse-height dependent width


Milinda s initial processing of the mc event pe rescaling in calibratemc2
Milinda’s initial processing of the MC event scattering angle distributions for different asymmetry factors.PE rescaling in CalibrateMC2

  • Optionally it rescales the pes

    • Old and new calibrations give different pe scales

    • The MC agrees with the new calibrations (603)

    • To compare the MC results with data analyzed with the old calibrations it is recommended to rescale the real number of pes to some number the old calibration would give.

    • In a sense, apply the systematic errors of the old calibrations to the MC results

    • Pesnew = Pesold,true*( 1 + rescale_factor*log10(Pesold,true)

    • Off by default

    • Milinda->Reco.Control.MCControl.RescalePEs=1;

    • Milinda->Reco.Control.MCControl.RescalePEFactor = factor ; (0.18 by default)

    • If pes are rescaled then X2 and A4 distributions for MC results match the ones produced using the v501 calibrations.


Results the muon peak
Results: the Muon Peak scattering angle distributions for different asymmetry factors.


Muon peak in data
Muon peak in data scattering angle distributions for different asymmetry factors.

Location of the muon peak using the 603 calibrations.

Location of the muon peak using the calibration corresponding to each era.


Muon peak results
Muon peak results scattering angle distributions for different asymmetry factors.

  • For G3 MC 232 pes

  • For G4 MC & no PMT corrections 180 pes

  • For G4 MC & PMT corrections 112 pes

  • Latest data with v603 calibrations ~109 pes


Results data vs mc
Results: Data vs MC scattering angle distributions for different asymmetry factors.

  • Some plots from my latest memo

  • Data run 6662, analyzed with v601 calibrations

  • g4sim v2.0 hadrons, 30m Abs Length, 50m Scattering length, latest milinda (v1.1)


Number of calibrated PMTs hit with different PE multiplicities

Red is data and black is g4sim v2.0 hadrons


Total pes per event multiplicities

Red is data and black is g4sim v2.0 hadrons


Number of pes for hottest PMT (mxpe) multiplicities

Red is data and black is g4sim v2.0 hadrons


Pe multiplicities
PE multiplicities multiplicities

Red is data and black is g4sim v2.0 hadrons




Trigger rates
Trigger rates multiplicities


Crab rate
Crab rate multiplicities

Used latest angle fitter

g4sim v2.0 with PMT corrections applied


Conclusion
Conclusion multiplicities

  • MC vs Data

    • Agree: Muon peak, total pes, mxpe, npes per hit, X2, A4 etc

    • Disagree: Crab rates (?), trigger rates

  • Next things to improve in the MC & analysis

    • Liner and cover reflectivity (pipes etc)

    • Water scattering (simulate other asymmetry factors, backward scattering)

    • Noise rates in milinda (increase dark noise rate?)

    • Trigger simulation (risetime calculation probably wrong)

  • Improve time jitter to match the tchi distributions


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