PROCEEDINGS OF THE 31st ICRC, ŁODZ 2009 1

Estimation of the Proton Energy Spectrum at the Knee Region byusing the Analog Read-out of ARGO-YBJ ExperimentSongZhan Chen∗, CunFeng Feng†, XiaoBo Qu†, Min Zha∗ and XueYao Zhang†

for the ARGO-YBJ Collaboration∗Key Laboratory of Particle Astrophysics, Institue of High Energy Physics, CAS, Beijing, 100049, P.R. China

†School of physics, Shandong University,Jinan 250100, China

Abstract. Based on a Monte Carlo simulation ofthe ARGO-YBJ experiment analog read-out, the dis-crimination between the different primaries inducedshowers by using the space-time information of thecharged particles in the Extensive Air Showers (EAS)is studied. Using an artificial neural network thepossibility of measuring the proton energy spectrumis investigated.

Keywords: knee region, ANN, simulation, protonenergy spectrum

I. INTRODUCTION

The all-particle energy spectrum of cosmic rays showsa distinctive feature around several PeV, known asthe ”knee”, where the spectral index of the power-law dependence changes from -2.7 to approximately-3.1[1]. The existence of this feature has been wellestablished experimentally, but controversial argumentson its origin still remain. To explain this feature, severalmechanisms have been proposed[2]. In some of thesetheoretical models, it is believed that the knee is anintrinsic property of the energy spectrum, related tothe acceleration and propagation of the cosmic rays.While in other models the knee is explained as a newtype of interaction at very high energy. All of thesetheoretical models are still under discussion due to lackof detailed knowledge about the chemical compositionaround the knee. Several ground based experiments havemeasured the energy spectrum of some components ofthe cosmic rays in the knee region, however due tothe limited ability of identifying the primary particlesand the limited statistics, the experimental results arestill inconsistent: the Tibet AS-γ experiment shows thatthe knee is caused by the steepening of heavy nucleispectrum in this region and proton spectrum shouldsteepen at about 100 TeV[3], whist according to theKASCADE experimental results, it should be caused bylight elements[4]. So a clear proton spectrum in the kneeregion is the key to a definite conclusion of this problemand it can also give a limitation for theoretical models.

To identify the individual components of the primarycosmic rays, the ground based experiments are requiredto give sufficient information on the Extensive AirShower (EAS) produced by these high energy particles.The ARGO-YBJ experiment utilizes a full coveragedetector array to detect the EAS. Its good time resolution(∼ 1.8 ns) and fine space granularity enable it to getenough information on air showers, which can be used to

discriminate between different primaries. In addition, atthe observation level of this experiment (4300 m a.s.l.),the EASs induced by cosmic ray particles with energiesin the knee region reach a maximum development,independently on the primary mass, so the shower sizehas minimum fluctuations and the energy determinationis more precise and less dependent from the unknowncomposition.

In this paper based on Monte Carlo simulation, westudy the feasibility of identifying the primary cosmicray particles with energies in the knee region. Study-ing the features of the lateral distribution of showersecondary particles produced by different primary par-ticles, 6 characteristic parameters have been identifiedto discriminate between different primaries. With theseparameters as input, multivariate analysis is performedby using the artificial neural network (ANN) method.The influence of hadronic interaction model is estimatedvia a detailed comparison between the results of twohadronic interaction models: QGSJET-II and SIBYLL.Through these analysis we find that, with weak modeldependence, the ANN method can efficiently pick outfrom the simulated data sample the proton inducedevents from the others.

In section 2, we describe the detector configuration.The detailed information about Monte Carlo simulation,cut criteria, the method to select candidate event samplesand to estimate the energy spectrum are described insection 3. The summary and discussion are shown insection 4.

II. THE ARGO-YBJ DETECTOR

The ARGO-YBJ experiment[5] is located in Ti-bet(P.R. China) at the Yangbajing Cosmic Ray Obser-vatory (30.11◦N, 90.53◦E) at an altitude of 4300 ma.s.l., corresponding to a vertical atmospheric depth of606 g cm−2. It consists of a full coverage array ofdimension 78 × 74 m2, composed of a single layerof RPCs (Resistive Plate Chambers) 285 × 122.5 cm2

each. The area surrounding this central carpet, up to∼ 110 × 100 m2, is partially instrumented with RPCs(the guard ring). The RPCs are grouped into clusters,each cluster consisting of 12 contiguous RPCs. EachRPC is divided into 10 basic detection units (calledPADs, with dimensions 55.6 × 61.8 cm2, and 8 digitalreadout strips each). In order to extend the dynamicrange, a charge read-out layer has been implementedby instrumenting each RPC with two large size pads,

2 CHEN et al. ESTIMATION OF PROTON ENERGY SPECTRUM

140 × 122.5 cm2 each (the so-called Big Pad). Inthe following analysis, the central carpet is artificiallydivided into two parts: the internal detector (the 6 × 9clusters in the center) and the external detector (the 76clusters around, see Fig. 1.)

III. THE MONTE CARLO SIMULATION AND THEDATA SELECTION

A Monte Carlo simulation has been carried out tosimulate the air shower cascade development in theatmosphere and the response in the ARGO detector. TheCORSIKA code[6] has been used for the air shower sim-ulation. The detector response is simulated by means of aGEANT3-based program, named ARGOG, reproducingthe detector geometry and materials. In particular, theBig Pad is considered as a sensitive volume, generatinga hit for each crossing charged particle. The saturationis set to 104particles/m2.

In order to estimate the effect from different highenergy hadronic interaction models, QGSJET-II andSIBYLL have been used to generate the EASs. In bothcases the code GHEISHA has been adopted to treat thelow energy interaction processes. All shower secondaryparticles have been traced down to the energy thresholdof 300 MeV for hadrons and 3 MeV for electrons andpositrons.

In this study, we selected the Poly-gonato model[7]to consider the composition of incident cosmic rays. Inthis model, the primary chemical composition is dividedinto 5 groups: Proton (abbreviated as H and with massnumber=1), Helium (He, 4), light nuclei (C-O,7), mediannuclei (Mg-Si,13) and iron nuclei (Fe,56). Followingthis model, the component fractions in the simulatedenergy range from 10 TeV to 10 PeV are: H 37.0%,He 29.7%, C-O 13.4%, Mg-Si 7.7% and Fe 12.2%.The simulated energy spectrum of each primary typeis shown in Fig. 2; the incident zenith angles of primaryparticles are isotropically sampled within 15◦.

For each simulated EAS event which reaches thedetector observation level, the shower core location israndomly sampled within an area of 100 × 100 m2

around the detector center. A total of 3.78× 106 eventshas been generated for each high energy interactionmodel.

In order to select high energy events with enoughinformation around the shower core and reduce the errorcaused by the shower core reconstruction, only eventswith the core located inside the internal detector are usedfor the analysis. These internal events are selected byusing the following criteria:

(1) The number of fired pads, Npad, is larger than5000;

(2) The particle density in the internal detector is atleast 1.11 times higher than in the external one;

(3) The module (consisting of 3 RPCs) with the largestnumber of charged particles is in the internal detector;

(4) The reconstructed core is inside a fiducial area of50× 55 m2 around the center of the detector array.

The efficiency of internal event selection for protonand iron induced showers is 97.7% and 92.3% respec-tively. We carried out a test simulation for showers withcore located in a ring area between 100 × 100 m2 and200×200 m2. After the same selection, only 0.02% outof the second sample events were selected as internalevents, meaning that the sampling area of 100× 100m2

is big enough for the internal events selection.

IV. ANALYSIS AND RESULTS

A. Identification of proton induced showers

In order to discriminate the proton induced showersfrom the others, a multivariate analysis is performed byusing the artificial neuron network (ANN) method. Afeed forward ANN is created with 3 perception layers.The input layer consists of 6 neurons corresponding tothe 6 parameters introduced in the following text. Thehidden layer has 15 neurons and the output layer hasone neuron. The ANN is implemented and optimized byusing the TMVA toolkit[8]. The input parameters are:

1. The recorded shower size near the core region (r< 5 m): N core

hit ;2. The mean lateral spread radius: < R >=∑(NiRi)/

∑Ni, where Ni is the number of charged

particles recorded by the ith Big Pad, and Ri is thedistance from this detector to the shower core.

3. The radius of the minimum circle which contains80% of the total charged particles recorded by the centralcarpet: R80.

4. The slope of the conical shower time front: Sfront.5. The density ratio: Ratio80, i.e. the ratio of the core

region particle density with respect to the R80 region.6. The multi-core factor: Mcore =

√NinNoutr2,

where Nin is the signal recored near the core region,Nout is the highest signal out of the core region and ris their distance.

The distributions of < R >, R80, Ratio80 and Mcore

are shown in Fig. 3. This figure shows that protoninduced events have smaller < R > and R80, and largerMcore and Ratio80 compared to the events produced byheavier nuclei.

The generated Monte Carlo air showers are randomlydivided into two samples: one half is used to train thenetwork and the other half to test it. The target valuesfor protons and other nuclei are set as one and zerorespectively. After 300 epochs of ANN training process,the network became very stable. Then this network hasbeen used to select the proton induced showers fromthe others. A cut value of C is set to pick up protonsfrom all ANN outputs. In this analysis, C = 0.75 meansthat showers with ANN output greater than 0.75 areconsidered proton-like showers. With this cut, the protonpicked-up ratio is 45%, the other nuclei rejection ratiois 91% and the purity of this cut is 85%.

In order to check the influence of the hadronic inter-action model, two ANN have been trained and testedusing QGSJET-II and SIBYLL simulated data, and thecross-analysis is done. Testing the QGSJET-II ANN

PROCEEDINGS OF THE 31st ICRC, ŁODZ 2009 3

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Fig. 1. The layout of the ARGO-YBJ experiment: The 6× 9 clusters in the center (marked) are the internal detector. See text for details.

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Fig. 2. The simulated energy spectrum for 5 elements (the details ofspectrum behaviour can be found in [7]).

with SIBYLL data, the proton picked-up ratio, the othernuclei rejection ratio and the purity are 42%, 90%and 83% respectively; testing the SIBYLL ANN withQGSJET-II data gives similar results. This confirms thatthe dependence on the hadronic interaction models isweak, and that the simulated primary proton events canbe effectively identified from the other events by usingthe ANN method.

B. Estimation of the primary energy

The number of charged particles recorded by theBig-Pad detector (Nhits) can be used to estimate theprimary energy of proton-like showers. Fig. 4 shows thescatter plots of the primary energy E0 versus Nhits. Therelationship between Nhits and E0 can be representedby the following equation:

log10(E0) = 1.28 + 0.88 log10(Nhits). (1)

Fig. 5 shows the effective collecting area, obtainedusing the cut criteria mentioned above. Taking into

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Fig. 3. Parameters distribution: the solid black line refers to protoninduced events, the red dash line for other nuclei induced events. Topleft: < R >; top right: R80; down left: Ratio80; down right: Mcore.

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Fig. 4. The relationship between primary energy and the total numberof charged particles. The solid line is the fit obtained using equation(1).

4 CHEN et al. ESTIMATION OF PROTON ENERGY SPECTRUM

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Fig. 6. Comparison between the assumed and two estimated spectraof proton. The solid line for assumed spectrum, the hollow squaresfor spectrum estimated by QGSJET-II ANN, the solid triangle forspectrum estimated by SIBYLL ANN.

account the ANN efficiency in selecting proton-likeshowers, the energy reconstruction and the effectivecollecting area, the spectrum of protons in the kneeregion is obtained and shown in Fig. 6, where the solidline is the proton spectrum assumed in the simulation,the hollow squares are the proton spectrum estimated bythe QGSJET-II trained ANN, and the solid triangles arethe proton spectrum from the SIBYLL trained ANN. Agood agreement between the assumed and the estimatedproton energy spectra is seen in the energy region from100 TeV to 10 PeV.

V. CONCLUSION

The Big Pad detector with analog read-out of ARGO-YBJ experiment can give detailed measurement of thelateral distribution of the charged particle in a shower.After careful study of the behavior of the lateral distri-bution of the shower secondary particles, six parametershave been used to characterize the differences betweenproton induced showers and the others. Via the ANNmethod, proton induced showers can be effectively se-lected and a good agreement between the assumed andestimated energy spectrum is obtained in the energy re-gion from 100 TeV to 10 PeV. Two high energy hadronicmodels, QGSJET-II and SIBYLL, have been used andtheir influence in the proton showers discrimination hasbeen studied. Using a similar method, the measurementof the spectrum of other nuclei in the knee region, such

as helium and iron, is under study.

VI. ACKNOWLEDGEMENTS

This work is supported in China byNSFC(10120130794), the Chinese Ministry of Scienceand Technology, the Chinese Academy of Sciences, theKey Laboratory of Particle Astrophysics, CAS, and inItaly by the Istituto Nazionale di Fisica Nucleare(INFN).

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02[6] D. Heck et al. Report FZKA 6019, Forschungszentrum Karl-

sruhe,1998.[7] Jorg.R. Horandel. Astroparticle Physics, 2003, 19:193-220[8] A. Hocker et al. E-print arXiv: physics/0703039