G Kornakov E A Smultivariate Analysis

457 views

Published on

Published in: Technology, Education
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
457
On SlideShare
0
From Embeds
0
Number of Embeds
3
Actions
Shares
0
Downloads
3
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

G Kornakov E A Smultivariate Analysis

  1. 1. Detecting EAS with TRASGOs -a simulation- G. Kornakov February, 2010, Santiago de Compostela
  2. 2. Extensive air shower (EAS) ● How does an EAS occur? -High energy primary cosmic rays interact at the high atmosphere with production of billions of secondaries and shower formation ● Why are they interesting? -Astroparticle Physics: – Where do they come from? – How are they accelerated? – How do they propagate? – and many other... -very high energy (up to 1020 eV), -understanding of formation process
  3. 3. The knee region 1 Partícle/m2-y 1 Partícle/km2-y 1 Partícle/km2-y Knee
  4. 4. EAS statistics in knee region Mass of the primary cosmic ray vs energy measured in different experiments [CCOU02] The scatter plot of the average logarithm of the nuclear mass number of the primary cosmic rays versus energy clearly shows the need for more input from accelerators.
  5. 5. EAS simulation Extensive showers detection on Earth surface Code: AIRES Simulations characteristics: -energy: 1015 eV (Knee region) -primary particles: P,C,Fe,Gammas -depth of first interaction:30g/cm2 -number of simulations: 100 for each case. -height of measurement plane: 1400 m
  6. 6. EAS simulation Variables simulated ● x,y, ● θ,φ ● time of arrival ● energy ● height of production of secondary part. ● id. of the secondary particle
  7. 7. EAS simulation Some results e μ
  8. 8. EAS simulation Some results e μ time time φ φ Difference between azimuthal angles of electrons and muons vs time
  9. 9. EAS simulation Some results e μ θ time r time θ r
  10. 10. Lateral distribution in a EAS induced by proton Lateral distribution of μ+e at different primary energies ~200m ~90m ~30m
  11. 11. EAS simulation We have started to analyse the answer of a single detector at different distances from the shower core: We assumed S=1m2 detectors 8 R=5particles/m2 9 7 R~30m for 101 5ev 3 proton R 10 4 1 2 6 5 11 13 12
  12. 12. RESULTS (time of arrival) 0m R/2~15 m R ~30m Iron Carbon
  13. 13. RESULTS (time of arrival) 0m R/2~15 m R ~30m Proton Gamma
  14. 14. RESULTS (zenithal angle) 0m R/2~15 m R ~30m Iron Carbon
  15. 15. RESULTS (zenithal angle) 0m R/2~15 m R ~30m Proton Gamma
  16. 16. RESULTS (Azimuthal angle in one detector) Iron Carbon Proton Gamma
  17. 17. NEXT STEPS
  18. 18. To define some secondary observables ● Number of particles <N>, <Ne>,<Nμ> ● Arrival Times: <T><Te><Tμ> ● Th <Th> and σ(Th) for e and μ at t=5ns, t=10ns, t=20ns. ● <Ph> and σ(Ph) as a function of position Analyse their behaviour, their correlations, their clusters...
  19. 19. One dream
  20. 20. Why multivariate analysis? ● A lot of information spread out in many observable variables (many dimensions problem) ● Some variables are strongly correlated and dependent on the primary cosmic ray characteristics (energy, mass, direction) ● Many multivariate techniques developed recently and not yet commonly used in astroparticle physics: clusters analysis, PAC analysis … Problems we expect: ● High fluctuations in different EAS from the same primary ● High statistical fluctuations inside a single shower Hope: ● To find some hidden relationship among all the observables informing us about the properties of the primitive cosmic ray
  21. 21. Still a lot of work
  22. 22. Acknowledgments especially want to thank R.Vázquez for his help with the simulations

×