1 / 24

P ierre Colin Dmitry Naumov Patrick Nedelec

RECONSTRUCTION OF EXTENSIVE AIR SHOWERS FROM SPACE. Stand alone method using only EAS induced light . General algorithms for any space project. ( EUSO, OWL, TUS, KLYPVE… ). P ierre Colin Dmitry Naumov Patrick Nedelec. Physics hopes. Purpose : Reconstruct initial UHECR parameters.

konala
Télécharger la présentation

P ierre Colin Dmitry Naumov Patrick Nedelec

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. RECONSTRUCTION OF EXTENSIVE AIR SHOWERS FROM SPACE • Stand alone method using only EAS induced light. • General algorithms for any space project. • ( EUSO, OWL, TUS, KLYPVE… ) Pierre Colin Dmitry Naumov Patrick Nedelec

  2. Physics hopes Purpose: Reconstruct initial UHECR parameters Energy (spectrum) Direction (UHECR sources map) Particle type (proton, iron, neutrino, gamma, etc.) ?

  3. Shower parameters UHECR : Angles (Zenithal θ and Azimuthal φ) Altitude of shower maximum: Hmax Depth of shower maximum: Xmax Total energy released E E Xmax Hmax 

  4. Detection from space EUSO simulation Extensive air shower Air fluorescence (isotropic) Cerenkov light (directional) Ground scattering Space telescope SIGNAL = f(t) UHECR Cerenkov echo Fluorescence Cloud

  5. Data fit Available information: for every GTU (Time Unit ~2.5 µs) Number of detected photons: Ni fit: 2 Gaussians: Fluorescence + Cerenkov + constant: Background noise • Monte Carlo data • - Global fitFluorescence Cerenkov Background

  6. Key parameter Golden event Need Cerenkov echo Fluorescence event Only signal shape TWO METHODS Monte Carlo Data Signal analysis (Trigger conditions): 3 samples of events Fluorescence events Golden events (Fluo+Cer) Cerenkov events Reconstruction

  7. z EUSO R α Fluorescence ΔH = Hmax - Hcer ΔH y Cerenkov echo x ΔH Hmax = ΔH + Hcer • Disadvantage: • We need to know Hcer to reconstruct Hmax • : Relief, Cloud altitude (Lidar?) Hmax reconstruction : Cerenkov method (Classical method) For golden events : We use Cerenkov echo : Time between Cerenkov and fluorescence maximum

  8. Hmax reconstruction : Cerenkov method Test of the method: no cloud events (Hcer = 0 ) Reconstructed Hmax vs Simulated Hmax Relative Erreur Error<10% for <60° • Method not efficient for large  angle (horizontal EAS)

  9. In one GTU i: Li = LGTU Ni η·Y·Ne·LGTU = # detected ph/GTU Transmission η has also a smooth variation with altitude Niis quite independent of the altitude: Ni Ne Nmax (η·Y)max·Nemax·LGTU Hmax reconstruction : Shape method (Brand new method) For Fluorescence event: We use only Fluo signal = # emitted photon L= EAS track length Fluorescence Yield (ph/m) Ne = # charged particles in EAS Y = Fluorescence Light Yield Y: smooth variation with altitude

  10. Hmax reconstruction : Shape method For horizontal showers: Total shower lenght: L =  LGTU = xtot / (h) L20=100 km 5 km 20 km Xtot = L·(h) L5 = 15 km Ntot =  Ni  η·Y·< Ne>·L  η·Y·<Ne>· xtot / (h) Ntot varies dramatically with altitude:

  11. Hmax reconstruction : Shape method Generalization for all  angles : Thanks to η & Y smooth variation with altitude Approximation: <η·Y·Ne>= (η·Y)max·< Ne> < (h) > = (Hmax) Varies like ln(E) Nmax/Ntot  (Hmax) (Hmax) Hmax

  12. Hmax reconstruction : Shape method Test of the method: Reconstructed vs Simulated Hmax Relative Erreur Error<10% for >60° Good Method to reconstruct large  angle EAS !

  13. Direction reconstruction : Available information: for every GTU Photon incident angles: ix, iy There is relationship between (ix,iy) and (θ,φ) angle of EAS. Reconstruct Θ Reconstruct  Direction: σ ~ 2° Simulated  Simulated Assuming infinite pixel resolution

  14. Xmax reconstruction (reconstructed Xmax – simulated Xmax)(Θ)in g/cm2 Golden events fluorescence events Hmax by shape method Hmax by Cerenkov echo σ<5% for <50° σ ~ 10 %

  15. Energyreconstruction for 1020 eV proton σ = 22% E reconstructed by shape method (fluorescence)

  16. Shape method good for UHE neutrinos! neutrinos protons Neutrinos create mainly horizontal EAS without Cerenkov echo.

  17. Conclusion • We have developed two complementary methods to reconstruct EAS from space using UV light signal. • using Cerenkov echo • Efficient for “vertical” showers (<60°) • Need complementary information (echo altitude) • using only signal shape • Efficient for “horizontal” showers (>60°) • UHE Neutrino astronomy from space is possible We can reconstruct any  EAS: 0° to 90° or more ! This first trial is very promising.

  18. BONUS SLIDE

  19. Simulated data Available information: for every GTU (Time Unit ~2.5 µs) Photon incident angles: ix, iy Number of detected photons: Ni z Space telescope ix, iy EUSO simulation αy αx Extensive air shower  Hmax y  x

  20. If we add pixel resolution: EUSO simulation EUSO event on focal plan (M36)  Error : more from detector than from method

  21. Xmax reconstruction SLAST simulation of Xmax(g/cm2) Xmax change with RCUE type: Xmax = f(E/A) (E/A is energy by nucleon) Iron proton Test with 10 000 protons and 10 000 iron nuclei Xmaxfor fluorescence events Xmaxfor Golden events

  22. Energyreconstruction Y : Fluorescence yield (ph/m) Kakimoto Model η : Atmosphere transmission Lowtran Model ε : Detector efficiency ΔΩ : Detector solid angle

  23. Energyreconstruction

  24. Detection from space EUSO simulation SIGNAL = f(t) Extensive air shower Air fluorescence (isotropic) Cerenkov light (directional) Air scattering Ground scattering Space telescope UHECR Cloud

More Related