Composition study of ultra-high-energy cosmic rays based on the Telescope Array surface detector data Yana Zhezher for the Telescope Array collaboration Institute for Nuclear Research of the Russian Academy of Sciences, Moscow, Russia July 17, 2017
Abstract ◮ Overview ◮ Method: Multivariate analysis based on Boosted decision trees ◮ Data set and results Y. Zhezher TA SD Composition
UHECR � 10 18 eV composition measurements Experiment detector Observable HiRes fluorescence stereo X MAX Pierre Auger fluorescence + SD X MAX (hybrid) Telescope Array stereo X MAX Telescope Array hybrid X MAX Yakutsk muon ρ µ X µ Pierre Auger SD MAX Pierre Auger SD risetime asymmetry SD – surface detector X MAX – depth of the shower maximum X µ MAX – muon production depth risetime – time from 10% to 50% for the total integrated signal Y. Zhezher TA SD Composition
Telescope Array Observatory ◮ Utah, 2 hrs drive from Salt Lake City, ◮ 507 surface detectors, S = 3m 2 , Largest UHECR statistics spacing 1.2 km in the Northern ◮ 3 fluorescence Hemisphere detectors ◮ 9 years of operation
Method outline 1. Reconstruct every event, get the values of composition-sensitive observables. 2. Multivariate analysis: ( a , AoP , . . . ) → ξ i . A set of observables is transformed into a single variable. The latter is used for composition analysis. 3. Compare distribution of ξ with Monte-Carlo modelling. 4. Result: average atomic mass < log A > as a function of energy.
List of relevant observables 1. Linsley front curvature parameter, a ; 2. Area-over-peak (AoP) of the signal at 1200 m; Pierre Auger Collaboration, Phys.Rev.Lett. 100 (2008) 211101 3. AoP slope parameter; 4. Number of detectors hit; 5. N. of detectors excluded from the fit of the shower front; 6. χ 2 / d . o . f . ; 7. S b = � S i × r b parameter for b = 3 and b = 4 . 5; Ros, Supanitsky, Medina-Tanco et al. Astropart.Phys. 47 (2013) 10 8. The sum of signals of all detectors of the event; 9. Asymmetry of signal at upper and lower layers of detectors; 10. Total n. of peaks within all FADC traces; 11. N. of peaks for the detector with the largest signal; 12. N. of peaks present in the upper layer and not in lower; 13. N. of peaks present in the lower layer and not in upper;
Linsley front curvature parameter Shower front is fit using the following function: t 0 ( r ) = t 0 + t plane + + a × 0 . 67 ( 1 + r / R L ) 1 . 5 LDF ( r ) − 0 . 5 LDF ( r ) = f ( r ) / f ( 800 m ) � r � − 0 . 6 � − 1 . 2 � � − ( η − 1 . 2 ) � 1 + r 2 r f ( r ) = 1 + R 2 R m R m 1 R m = 90 . 0 m , R 1 = 1000 m , R L = 30 m Deeper shower η = 3 . 97 − 1 . 79 ( sec ( θ ) − 1 ) maximum leads to more curved front. t plane – shower plane delay a – Linsley front curvature parameter LDF – lateral distribution function
Area-over-peak (AoP) and area-over-peak slope ◮ Consider a surface station time-resolved signal ◮ Both peak and area are well-measured and not much affected by fluctuations ◮ AoP ( r ) is fitted with a linear fit: ◮ AoP ( r ) = α − β ( r / r 0 − 1 . 0 ) ◮ r 0 = 1200 m, α - value at 1200 m, β - slope Y. Zhezher TA SD Composition
MVA BDT analysis ◮ The Boosted Decision Trees (BDT) technique is used to build p - Fe classifier based on multiple observables. Pierre Auger Collaboration, ApJ, 789, 160 (2014) ◮ BDT is trained with Monte-Carlo sets: Fe (Signal) and p (Background) ◮ BDT classifier is used to convert the set of observables for an event to a number ξ ∈ [ − 1 : 1 ] : 1 - pure signal ( Fe ), -1 - pure background ( p ). ◮ ξ is available for one-dimensional analysis. Y. Zhezher TA SD Composition
Boosted decision trees Y. Coadou, ESIPAP’16 ◮ For each variable, find splitting value with best separation between two branches: mostly signal in one branch, mostly background in another; ◮ Repeat algorithm recursively on each branch: take a new variable or reuse the former; ◮ Iterate until stopping criterion is reached (e.g. number of events in a branch). Terminal node = leaf; ◮ Boosting: create a good classifier using a number of weak ones (building a forest).
Data and Monte-Carlo sets ◮ 9-year data collected by the TA surface detector: 2008-05-11 — 2017-05-11 Cuts: 1. Events with 7 or more triggered counters 2. Events with zenith angle θ < 45 ◦ . 3. Events with reconstructed core position of at least 1200 m away from the edge of the array. 4. Events with χ 2 G / d . o . f . < 4 and χ 2 LDF / d . o . f . < 4. 5. Events with geometry reconstructed with accuracy less than 5 ◦ . 6. Events with the fractional uncertainty of the S 800 less than 25 %. 7. Events with E > 10 18 eV. 18077 events after cuts Y. Zhezher TA SD Composition
Data and Monte-Carlo sets p and Fe Monte-Carlo sets with QGSJETII-03 Note: MC sets are split into 3 equal parts: (I) for training the classifier, (II) for MVA estimator calculation, (III) for determination of systematical uncertanties. Y. Zhezher TA SD Composition
Distribution of MVA estimator ξ 18.0<log(E)<18.2 18.2<log(E)<18.4 h_fm h_fm h_pm h_pm h_dt h_pm h_pm h_fm h_fm h_dt Entries Entries Entries Entries Entries 1980 3539 3539 1980 741 Entries Entries Entries Entries Entries 11463 16650 16650 11463 3482 2200 Mean Mean Mean Mean Mean 0.007598 0.007598 0.01306 0.01908 0.01908 Mean Mean Mean Mean Mean 0.004902 0.004902 0.02295 0.01412 0.02295 450 RMS RMS RMS RMS RMS 0.03416 0.03186 0.03186 0.03258 0.03258 2000 RMS RMS RMS RMS RMS 0.03235 0.03235 0.03443 0.03296 0.03296 Underflow Underflow Underflow Underflow Underflow 0 0 0 0 0 Underflow Underflow Underflow Underflow Underflow 0 0 0 0 0 400 1800 Overflow Overflow Overflow Overflow Overflow 0 0 0 0 0 Overflow Overflow Overflow Overflow Overflow 0 0 0 0 0 350 1600 300 1400 /PRELIMINARY/ 1200 250 1000 200 800 150 600 100 400 50 200 0 0 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 ξ ξ 18.4<log(E)<18.6 18.6<log(E)<18.8 h_pm h_pm h_fm h_fm h_dt h_fm h_fm h_pm h_pm h_dt Entries Entries Entries Entries Entries 21888 16362 16362 21888 4707 Entries Entries Entries Entries Entries 16544 16544 13720 13720 3834 2200 Mean Mean Mean Mean Mean 0.001475 0.001475 0.02461 0.02461 0.0115 Mean Mean Mean Mean Mean 0.005332 -0.006592 -0.006592 0.01769 0.01769 2500 RMS RMS RMS RMS RMS 0.03629 0.03629 0.03832 0.03762 0.03762 2000 RMS RMS RMS RMS RMS 0.03573 0.03573 0.03699 0.03825 0.03825 Underflow Underflow Underflow Underflow Underflow 0 0 0 0 0 Underflow Underflow Underflow Underflow Underflow 0 0 0 0 0 1800 Overflow Overflow Overflow Overflow Overflow 0 0 0 0 0 Overflow Overflow Overflow Overflow Overflow 0 0 0 0 0 2000 1600 1400 /PRELIMINARY/ 1500 1200 1000 1000 800 600 500 400 200 0 0 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 ξ ξ proton, iron, data Y. Zhezher TA SD Composition
Distribution of MVA estimator ξ 18.8<log(E)<19.0 19.0<log(E)<19.2 h_pm h_pm h_fm h_fm h_dt h_fm h_pm h_fm h_pm h_dt Entries Entries Entries Entries Entries 10757 10050 10050 10757 2762 800 Entries Entries Entries Entries Entries 4854 4854 4783 4783 1393 1600 Mean Mean Mean Mean Mean 0.000807 -0.01024 0.01339 -0.01024 0.01339 Mean Mean Mean Mean Mean 0.0005126 -0.01238 0.01036 0.01036 -0.01238 RMS RMS RMS RMS RMS 0.03306 0.03306 0.03515 0.03439 0.03515 RMS RMS RMS RMS RMS 0.03318 0.03318 0.03627 0.03627 0.03534 700 1400 Underflow Underflow Underflow Underflow Underflow 0 0 0 0 0 Underflow Underflow Underflow Underflow Underflow 0 0 0 0 0 Overflow Overflow Overflow Overflow Overflow 0 0 0 0 0 Overflow Overflow Overflow Overflow Overflow 0 0 0 0 0 600 1200 500 1000 /PRELIMINARY/ 400 800 300 600 200 400 200 100 0 0 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 ξ ξ 19.2<log(E)<19.4 19.4<log(E)<19.6 h_fm h_fm h_pm h_pm h_dt h_fm h_pm h_pm h_fm h_dt 350 Entries Entries Entries Entries Entries 2098 2068 2068 2098 691 Entries Entries Entries Entries Entries 998 998 949 949 280 Mean Mean Mean Mean Mean -0.002341 -0.01559 -0.01559 0.0121 0.0121 Mean Mean Mean Mean Mean 0.005376 0.01716 -0.01308 0.01716 -0.01308 RMS RMS RMS RMS RMS 0.04096 0.04096 0.04427 0.04264 0.04427 100 RMS RMS RMS RMS RMS 0.05333 0.05333 0.06157 0.05861 0.06157 300 Underflow Underflow Underflow Underflow Underflow 0 0 0 0 0 Underflow Underflow Underflow Underflow Underflow 0 0 0 0 0 Overflow Overflow Overflow Overflow Overflow 0 0 0 0 0 Overflow Overflow Overflow Overflow Overflow 0 0 0 0 0 250 80 /PRELIMINARY/ 200 60 150 40 100 20 50 0 0 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 ξ ξ proton, iron, data Y. Zhezher TA SD Composition
Results: TA SD (MVA) composition 6 /PRELIMINARY/ TA SD, QGSJET II-03 5 4 Fe Si 3 <ln A> N 2 He 1 0 p 18 18.5 19 19.5 20 log 10 E, eV
Results comparison: TA SD (MVA) vs TA hybrid 6 TA SD, QGSJET II-03 /PRELIMINARY/ TA hybrid, QGSJET II-03 5 4 Fe Si <ln A> 3 N 2 He 1 0 p 18 18.5 19 19.5 20 log 10 E, eV [TA] W.Hanlon, UHECR’16, also the talk [646][CRI123]
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