Fitting fractions of the $X_{rm max}$ distributions at ultra high energies

Discussion timeslot (ZOOM-Meeting): 16. July 2021 - 18:00
ZOOM-Meeting URL: https://icrc2021.desy.de/pf_access_abstracts
Corresponding Session: https://icrc2021-venue.desy.de/channel/Presenter-Forum-1-Evening-All-Categories/48
Abstract:
'The mass composition of ultra high-energy cosmic ray (UHECRs) can be inferred from measurements of $X_{rm max}$ distributions by fitting them with Monte Carlo (MC) predictions for different primary species of nuclei in each energy interval. On the basis of Monte Carlo (MC) simulations, we show that an appropriate approach is to fit the observed $X_{rm max}$ distributions with all possible combinations of elements from a large set of primaries (in our case p, He, C, N, O, Ne, Si and Fe), and to find the "best combination" of elements which best describe the observed $X_{rm max}$ distributions. 

We apply this method to the $X_{rm max}$ distributions recorded by the Pierre Auger (2014) and Telescope Array (TA) (2016) Observatories in the energy range $lg E (rm eV) =$ [17.8 - 19.3] and 

$lg E (rm eV) =$ [18.2 - 19.0], respectively, by employing MC predictions of the QGSJETII-04 hadronic interaction model. The results obtained from both data sets suggest that the mass composition of UHECRs is dominated by protons and He nuclei ($gtrsim 70%$) which present a modulation of their abundances as a function of primary energy, but keeping their sum roughly constant. We performed an indirect comparison between the two data sets measured by the two experiments and found a good degree of compatibility in some energy bins around and above the textit{ankle} ($lg E (rm eV) sim 18.7$), but worsening at lower energies.

We consider that the current approach, completed with predictions of different hadronic interaction models, can be used in further studies on mass composition to obtain a more accurate image of the evolution of the individual fractions of nuclei as a function of energy on the basis of experimental $X_{rm max}$ distributions.'

Authors: Nicusor Arsene
Indico-ID: 185
Proceeding URL: https://pos.sissa.it/395/193