29th International Cosmic Ray Conference Pune (2005) 6, 209–212

A detailed study on nuclear composition of primary cosmic rays aroundthe knee with GRAPES-3

H. Tanaka , S.K. Gupta , Y.  Hayashi , N. Ito , A. Jain , A.V. John , S. Karthikeyan ,S. Kawakami , H. Kojima , K. Matsumoto , Y. Matsumoto , T. Matsuyama ,
D.K. Mohanty , P.K. Mohanty , S.D. Morris , T. Nonaka , T. Okuda , A. Oshima ,
B.S. Rao , K.C. Ravindran , K. Sivaprasad , B.V. Sreekantan , S.C. Tonwar ,K. Viswanathan and T. Yoshikoshi(a) Tata Institute of Fundamental Research, Homi Bhabha Road, Mumbai 400 005, India(b) Graduate School of Science, Osaka City University, Osaka 558-8585, Japan(c) Nagoya Women’s University, Nagoya 467-8610, Japan

Presenter: H. Tanaka (tanaka@alpha.sci.osaka-cu.ac.jp), jap-tanaka-H-abs1-he12-oral
GRAPES-3 is a high-density air shower array with large area muon detectors. The energy spectra of variousnuclei (H, He, N, Al and Fe) and their mean mass have been obtained through a combination of observations on electrons and muons. The mean mass number gradually increases through the knee region. Theseresults show dependence on the hadronic interaction models of EAS Monte Carlo. Two models, QGSJET andSIBYLL, were investigated and their results were compared with those from direct measurements. Predictionsof SIBYLL agree with JACEE results, but some discrepancy is seen between QGSJET and JACEE. Somemodels proposed in literature to explain the occurrence of the knee are used to compare them with our results.

1.

Introduction Cosmic ray energy spectrum displays a knee aroundeV, where the index of power law spectrum
changes fromto. The knee region is expected to provide information on the origin of cosmicrays. It is hoped that accurate measurement of cosmic ray spectra and nuclear composition around the knee
would help promote a better understanding of cosmic ray origin.

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The knee region is difficult to access with direct measurement at the top of atmosphere such as balloon andsatellite based experiments due to low flux of cosmic rays which requires large area and heavy detectors areflown. Therefore indirect measurement with air shower observation is the only practical way to reach higherenergies.
Monte Carlo simulation is used to reconstruct the shower in the atmosphere and estimate properties of primaryparticle from observed data. This kind of analysis is, however, limited by the reliability of simulation models
used because of extrapolations of high energy interaction models based on accelerator data. Here we have useda few models and have studied the dispersion in their predictions.

2.

Experimental Methods

The GRAPES-3 air shower array is located at Ooty in southern India at an altitude of 2,200 m above sea level.
It consists of nearly 300 scintillation detectors to measure the electron component and about 3700 proportionalcounters to detect muons in showers. [1][2]CORSIKA (v6.02)[3] EAS MC code is used for the simulation of observations. The EAS simulations have theuncertainties of hadronic interactions due to extrapolation of the measurements to the energy of EAS. COR-210

H. Tanaka et alSIKA incorporates several hadronic interaction models and here we use the following two, QGSJET (2001)
model and SIBYLL (2.1) model which have been developed for EAS simulations.

  A total ofshowers observed during two years of run in 2000 and 2001 (days) have been analyzed.
Size of each shower is estimated from lateral distribution of particles with a Maximum Likelihood Method fit
to NKG function. Here the free parameters are shower core location , shower sizeand age .

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Tracks of muons are reconstructed using tracking muon detectors to agree with shower direction. It helps toreduce the background of muons. Reconstructed showers are selected to be within a fiducial area and 80 m
away from muon detectors, which is shown as yellow (or gray) area in Figure 1, to reject distant showers
outside the array and to prevent saturation of muon detectors.

Figure 1. Shower detectors (plastic scintillator 1 m each) andmuon detectors (total 560 m ) are
shown with and respectively.
Here, core location shown in yellow (or gray) area is located inside
the array but
80 m away frommuon detectors.

&'()&Figure 2. Relation between shower
size and average number of muons.
Black line shows the results from observation. Red and blue lines show
MC results for proton and iron primaries. Dependence on the hadronicinteraction models is represented by
QGSJET (fat lines) and SIBYLL (finelines).

Figure 3. Muon multiplicity distributions classified by shower size. Observations are shown in black. MC(QGSJET) results for proton, helium, nitrogen, aluminum and iron are
shown in red, green, light blue, violet and blue respectively. MC distributions are fitted to observations to estimate relative abundance for each.

In Figure 2, average number of detected muons is shown for a given shower size. CORSIKA MC results arealso shown for proton and iron nuclei. Therelation features the primary nuclear composition. Resultsbases on hadronic interaction models, QGSJET and SIBYLL, are presented here.

" # *",+The energy spectrum of each component can be extracted from the shapes of muon multiplicity distributions.
Figure 3 shows the muon multiplicity distributions from observation and MC’s after fitting them to the observations to estimate relative abundance for each component. Here, the ratio of abundance of aluminum to ironis fixed to, because it is difficult to separate them. This value was decided from extrapolation of low energy
observations by RUNJOB. The relations between shower size and primary energy are calculated by MC foreach mass groups and the primary energies are converted from shower size as explained below. The MC iscarried out using
spectrum and then the MC showers are subjected to same cuts as observational data.
They are binned according to shower size and then the mean energy
for each bin is calculated. [4]- .

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A detailed study on nuclear composition of primary cosmic rays around the knee with GRAPES-3

3.

Results and Conclusions

The energy spectra and mean mass values derived from GRAPES-3 data are shown and compared with otherobservations in Figure 4, 5, 6 and 7.

Figure 4. A comparison proton and helium energy spectrawith other observations, RUNJOB, Tibet, EAS-TOP etc.
from Kobayakawa et al.[5]Figure 6. A comparison of all-particle energy spectra. GRAPES-3 compared with RUNJOB, Tibet, Akeno,
CASA-DICE, CASA-MIA, CASA-BLANCA etc. [5]Figure 5. A comparison of energy spectra of CNO,medium heavy and iron groups with other observations.
The flux of CNO and Fe-groups is multiplied byand, respectively. [5]>@?

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Figure 7. A comparison of mean mass () for
GRAPES-3 with JACEE, RUNJOB, CASA-DICE, CASAMIA, CASA-BLANCA, EAS-TOP, KASCADE, Chacaltaya etc. [5]BML

In Figure 6, the effect of interaction models is relatively small and flux from QGSJET is onlyhigher thanSIBYLL. No disagreement with other experiments is found for both models with all-particle spectra. However,there are significant differences in the flux for each component. In Figure 4 and 5, QGSJET flux is higher forproton, and SIBYLL is higher in heavier component.
Since direct observations are good checks on the interaction model, a comparison of direct results from JACEE

212

H. Tanaka et alwith QGSJET and SIBYLL shows good agreement with SIBYLL and some disagreement with QGSJET results.

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PQ7RSUTA comparison of the mean masses is shown in Figure 7. SIBYLL haslarger
than QGSJET.
Both have tendency to increase
aboveGeV. Here, SIBYLL has good agreement with direct measurements, too.

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Figure 8. A comparison of GRAPES-3 results withSIBYLL using six models of the knee from J.R.
Hörandel[6].

Figure 9. Diffusion and drift (F) knee model by
E. Roulet[7] and GRAPES-3 results using SIBYLL.

Some models are suggested to reproduce knee in the energy spectrum. In Figure 8, plots of mean mass number
which are expected from various knee models are shown[6] and results from SIBYLL, which is one of themost reliable model of hadronic interactions are also shown. Results from the following models of knee, (A)
Acceleration in supernova remnants, (B) Acceleration by supernova shocks, (C) The single-source model, (D)The minimum-pathlength model, (E) Drift in the global regular magnetic field of the Galaxy and (F) Diffusionand drift are shown in Figure 8. (A) (C) expect knee appears due to acceleration mechanisms, (D) (F) it isdue to propagation and leakage. In these, similar results are obtained from “(F) Diffusion and drift” model bycomparison of energy spectrum for each mass component.

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In Figure 9, CNO flux issmaller than SIBYLL, though it has good agreement in proton and helium spectra.
In the above, there is no model which agrees with our results completely, although “Diffusion and drift” model
has almost same results as our data.

References[1] S.K. Gupta et al. Nucl. Instr. & Meth. A, 540, 311 (2005).
[2] Y. Hayashi et al. Nucl. Instr. & Meth. A 545, 643 (2005).
[3] D. Heck et al. Forschungszentrum Karlsruhe, FZKA 6019 (1998).
[4] H. Tanaka et al. Proc. 28th ICRC, 1, 155, Tsukuba (2003).
[5] K. Kobayakawa et al. Phys. Rev. D, 66(8), 083004 (2002).
[6] J.R. Hörandel. Astropart. Phys., 21, 241 (2004).
[7] E. Roulet. Int. J. Mod. Phys. A, 19(7), 1133 (2004).