- Public Outreach
- NMDB Brochures
- Cosmic rays : high energy particles from the Universe
- Solar Wind, Heliosphere, and Cosmic Ray Propagation
- Cosmic rays and the Earth
- Impact: Technological and biological effects of cosmic rays
- Neutron monitor network : fundamental research and applications
- A few technical details
- Mathematical description: charged particle orbit in a magnetic field; magnetic rigidity
- What is an asymptotic arrival direction ?
- Detection of the secondary fluxes of cosmic rays
- 1. Modeling of the detector response.
- 2. Time series of secondary cosmic rays; smoothing and filtering techniques.
- 3. Calculating statistical significance of the detected peaks in time series.
- 4. Recovering of the primary particle intensities.
- 5. Calculation of the Barometric Coefficients for the Particle Detectors Belonging to the World-Wide Networks at the Start of the 24th Solar Activity Cycle
- Questions ?
1. Modeling of the detector response.
Particle detectors located at the Earth’s surface and on board of spacecraft are transforming the traverse of an elementary particle or nuclei through the volume of detector to an electrical signal and code thus providing connection between micro and macro worlds. Particle detectors use various physical phenomena to generate signals. Charged particles when interacting with matter in detector volume ionize atoms: ionization can be transformed to light or electrical current. Neutral particles in body of detector can generate charged particles by nuclear interactions and chemical reactions. From the magnitude of signals it is possible to determine the energy of particle and the number of particles traversing detector; by the combination of signals from composite detector it is possible to estimate the incident angles and type of the elementary particle.
However, there is no one-to-one relation between elementary particle properties and detector signals. Interaction mechanisms of particles with matter are of stochastic nature and only average characteristics can be estimated. To readout the particle properties from detector scale we need to do additional calibration/simulation work. Usually for the complicated detector setups it is rather difficult to provide accelerator beams of the definite particles with required energies to directly measure the response (detector calibration). A proxy of calibration is the so called, Monte Carlo method, a class of computational algorithms, exploiting fundamental knowledge of high energy physics as well as detailed design of detector to perform repeated random sampling in the domain of predefined input parameters. For the Neutron Monitors (see Figure 1) we need to the model of passage of the neutrons, protons, muons and pions through the absorber (lead) and moderator/reflector (polyethylene); calculate number of thermalized neutrons, follow their interactions in the proportional counter with boron isotope giving birth to alpha particles, and finally generating electrical pulse detected by Data Acquisition (DAQ) electronics.
Among several parameters addressed by the detector response simulation we will review only few ones, namely the efficiency, purity and cuts issues. The efficiency of detector to identify particles of definite types is calculated as the ratio of number of incident particles (or luminosity of the incident beam) to the registered ones. Purity of detection of the particle of definite type is the proportion of these particles among all particles selected by pre-chosen criteria applied upon the detector signals. Different cuts are applied to the detector signals to enlarge efficiency or purity dependent on the goals of experiment. However, it is not possible with one-and-the-same detector simultaneously enlarge both efficiency and purity; when you enlarge efficiency purity goes down, when you enlarge purity efficiency decreases. When the goal of research is count of the particles of definite type different kind of faults can occur. Detector can miss some of particles (loss in efficiency) and mistakenly register another kind of particles (loss in purity). Usually efficiency and purity are plotted as function of particle energy, or angle of incidence, or other parameter of interest.
Figure 2 displays the resulting detection efficiency of a NM-64 with 10BF3 counters for 6 different particle species in the vertical incident direction (Clem & Dorman, 2000). NM-64 is optimized to measure hadronic component of the secondary cosmic rays. The NM response (efficiency) to muons above 1 GeV is more than 3 orders of magnitude below the hadrons.
To obtain plot demonstrated in Figure 2 Monte Carlo code was implemented many times with different input parameters (10 energies for each of 6 particle types). For each of 60, so called simulation experiments, the code was run at least 1000 times to provide necessary statistical accuracy (note the error bars on the muon curve). For more detailed calculations and more complicated detectors a huge amount of computer time is needed. A famous GRID project is started at CERN as a tool for remote simulations and data analysis in all partner institutions.
The interplay of the efficiency and purity can be demonstrated by new particle detectors of world-wide network SEVAN (Chilingarian & Reymers, 2007).
Each layer of the detector has rather different efficiencies and purities for detection of the particles of definite type. To estimate the efficiencies and purities to detect secondary cosmic rays we need to know the energy spectra of muons, neutrons, protons, electrons at the geographical coordinates where detector is located. Then we have to simulate cascades of the charged and neutral secondary particles created by the primary particles entering the Earth’s atmosphere. These spectra were obtained with the CORSIKA (version 6.204) code (Heck, et al., 1998). The threshold energies for the primary particles assumed as input for CORSIKA correspond to the vertical cutoff rigidity of the detector location (7 GeV for Aragats). All secondary particles were tracked until their energy drops below the predetermined value (50 MeV for hadrons, 10 MeV for muons and 6 MeV for electrons and photons) or reached all the way to the ground level. The spectra of primary protons and helium nuclei (99% of the flux at energies up to 100 GeV) are selected to follow the proton and helium spectra reported by CAPRICE98 balloon-borne experiment (Boezio, et al., 2003). Among different species of secondary particles, generated in nuclear-electromagnetic cascades in the atmosphere, muons, electrons, gammas, neutrons, protons, pions and kaons were followed with CORSIKA and stored. These particles were used as input for the GEANT3/Geant4 packages (GEANT, 1993), implemented for detector response simulation. Also, we take into account the light absorption in the scintillator.
As we can see in the Figures 4-6 the purity of the charged particle detection (electrons & muons) by top and upper layers of SEVAN detector is about 95%, however it is maximal at sea level, where flux of neutrons and gammas highly attenuate. The neutral particle (gammas and neutrons) detection purity in the middle “thick” scintillator reaches 55% at 3200 m, and vanished to 30% at sea level, again due to attenuation of the neutron flux. The high energy muons, are mostly selected by the third layer under 10 cm of lead; purity of muon selection at sea level is 95%, vanishing to 88% at altitude of 3200 m.
Figure 4. Fractions of elementary particles detected by the upper layer of SEVAN detector.
Figure 5. Fractions of elementary particles detected by the middle layer of SEVAN detector
Figure 6. Fractions of elementary particles detected by the lower layer of SEVAN detector.
We can improve the purity of selected particle “beams” with DAQ electronics counting all coincidences of signals in detector layers. When a neutral particle traverses the top thin (5cm) scintillator, usually no signal is produced. The absence of the signal in the upper scintillators, coinciding with the signal in the middle scintillator, points to neutral particle detection. The coincidence of signals from the top and bottom scintillators indicates the traversal of high energy muons. Lead absorbers improve the efficiency of the neutral flux detection and filtered low energy charged particles. If we denote by “1” the signal from a scintillator and by “0” the absence of a signal, then the following combinations of the 3-layered detector output are possible:
- 111 and 101 – traversal of high energy muon, energy greater than 200 MeV;
- 010 – traversal of a neutral particle;
- 100 – traversal of a low energy charged particle stopped in the scintillator or in the first or second lead
absorber (energy less than ~200 MeV).
- 110 – traversal of a higher energy charged particle stopped in the second lead absorber.
- 001 – registration of inclined charged particles
The cuts are used for selecting of events of definite type connected with particular physical problem of interest. To illustrate techniques of the implementation of cuts let consider another ASEC detector – Aragats Solar Neutron Telescope (ASNT, Chilingarian et al, 2007).
Figure 7. Aragats Solar Neutron Telescope; four 1 m2 , 5 cm. thick scintillators and four 1 m2 , 60 cm. thick scintillators overviewed by photomultipliers equipped with analog to digital convertors (ADC).
One of the interesting physical problem we can research with ASNT is the estimation of the near-horizontal muon flux and the muon flux attenuation in 30 km of mountain rock.
ASNT software trigger selects the events with energy releases above preselected threshold ensuring the horizontal transport of muons. The chosen cut is selected events intersecting 2 detectors in a row (i.e. scintillators 8&7, 6&5, 6&8, 5&7, but not 8&5 and 6&7, see Figure 7) and releasing energy corresponding to as minimum as 200 MeV (35 code of ADC, this value is equivalent for energy release of muon in 1 m of scintillator). Thus we can measure near horizontal muons coming from open horizon and from huge Aragats Mountain crossing at least 30 km of rock. Our results prove that muons coming from the Aragats direction have higher energies (as minimum 25-30GeV), comparing with muons coming from the “free” horizon (>5GeV).
Another physical problem solved by applying cuts is atmospheric electricity discharge connected with thunderstorms. A powerful particle accelerator is triggered by the large atmospheric electrical fields and cosmic ray showers. A new model, involving positive feedback from positrons and high energy photons allows the runaway discharge to become self-sustaining and increase exponentially the number of avalanche electrons in very short time scales (Dwyer, 2003). This mechanism, which shall be referred to as Relativistic Feedback Breakdown, (RFB) dramatically increasing the flux of runaway electrons, the accompanying high-energy radiation, and resulting ionization, thus enabling lightning breakdown. The theory puts the threshold of the high energy electrons on ~30 MeV. Therefore, it is important to check the energies of electrons comprising the pre-lightning
enhancement measured by ASEC detectors (Chilingarian et al., 2009).
Figure 8. Count Rate enhancements registered by the ASNT channels: 6-10 MeV (pink), 10-16 MeV (black), >30 MeV - blue
In Figure 8, obtained by applying of cuts on the ADC outputs from the ASNT channels we can see that indeed the vast majority of the enhancement is due to lower energy electrons and time series of electrons & muons with energies above 30 MeV did not demonstrate any enhancement.
In developing code for calculation of the detector response the necessary procedure is the code validation. Any model performs a reduction of a sophisticated physical process. Due to many simplifications, we cannot expect that results of simulations will exactly coincide with measurements. Nonetheless, the basic features of simulated phenomenon should coincide within definite limits with measurements. For instance, to validate CORSIKA simulation, we choose count rates of ASEC particle detectors and muon spectra measured at mountain altitude (Chilinagrian & Zazyan, 2009).
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- Agostinelli, S., Allison, J., Amako, K. GEANT4 — a simulation toolkit. Nucl. Instr. Meth. A 506, 250-303, 2003.
- Boezio, M., Bonvicini, V. , Schiavon, P., Vacchi, A., and Zampa, N. The cosmic-ray proton and helium spectra measured with the CAPRICE98 balloon experiment. Astropart. Phys. 19, 583-604, 2003.
- Chilingarian A.A., Arakelyan K., Avagyan K., et al. Correlated measurements of secondary cosmic ray fluxes by the Aragats Space Environmental Center monitors. NIM, A543, pp. 483-496, 2005.
- Chilingarian A, Melkumyan L., Hovsepyan G., Reymers A., The response function of the Aragats Solar Neutron Telescope, Nuclear Instruments and Methods in Physics Research, A 574, (255-263), 2007.
- Chilingarian A.A. and Reymers A.E. Particle detectors in Solar Physics and Space Weather Research, Astropart. Phys., 27, pp. 465-472, 2007.
- Chilingarian A.A. and Reymers A. Investigations of the response of hybrid particle detectors for the Space Environmental Viewing and Analysis Network (SEVAN), Ann. Geophys., 26, pp. 249-257, 2008.
- Chilingarian A., Hovsepyan G., Arakelyan K., et al., Space environmental viewing and analysis network (SEVAN). Earth, Moon, and Planets, v.104, p. 195, 2009.
- Chilingarian A., Daryan A., Arakelyan K. et al., Thunderstorm correlated enhancements of Cosmic Ray fluxes, detected at mt. Aragats, Proceedings of the 31th ICRC, Lodz, Poland, 2009.
- Clem, J.M. and Dorman, L.I., 2000, “Neutron monitor response functions” Space Sci. Rev., 93, 335.
- Dwyer, J. R., A fundamental limit on electric fields in air, Geophys. Res. Lett., 30(20), 2055, 2003.
- Fasso, A., Ferrari, A., Ranft, J., and Sala, P.R., 2005, FLUKA: a multi-particle transport code. CERN Yellow Report, INFN/TC_05/11.
- Heck, D., and Knapp, J., A Monte Carlo Code to Simulate Extensive Air Showers, 1998, Forschungszentrum Karlsryhe, FZKA Report 6019.
- Zazyan, M.Z., Chilingarian, A.A. Calculations of the Sensitivity of the Particle Detectors of ASECand SEVAN Networks to Galactic and Solar Cosmic Rays, Astroparticle physics, in press.