Marco Circella

Isotope composition of secondary hydrogen and helium above the atmosphere measured by the instruments NINA and NINA‐2

[1]xa0In this paper we report on the energy spectra and abundance ratios of hydrogen and helium isotopes of albedo origin, measured by the instruments NINA and NINA-2 in near-equatorial regions. The instrument NINA flew on board the satellite Resurs-01-N4 between 1998 and 1999, at a 830 km average altitude. The NINA-2 apparatus, on board the satellite MITA, was put into orbit in July 2000 at an altitude of about 450 km. NINA and NINA-2 measurements revealed that 2H, 3H, 3He, and 4He are a significant portion of the secondary flux above the atmosphere. The energy spectra of hydrogen isotopes are practically flat across the energy range of 10–40 MeV/n, while the spectra of helium isotopes can be fitted by a power law of spectral indexes γ = 0.8 and γ = 1.5 for 3He and 4He, respectively.

Geomagnetically trapped light isotopes observed with the detector NINA

[1]xa0The detector New Instrument for Nuclear Analysis (NINA) aboard the satellite Resurs-01-N4 detected hydrogen and helium isotopes geomagnetically trapped, while crossing the South Atlantic Anomaly. Deuterium and tritium at L shell < 1.2 were unambiguously recognized. The 3He and 4He power law spectra, reconstructed at L shell = 1.2 and B < 0.22 G, have indices equal to 2.30 ± 0.08 in the energy range 12–50 MeV nucleon−1 and 3.4 ± 0.2 in 10–30 MeV nucleon−1, respectively. The measured 3He/4He ratio and the reconstructed deuterium profile as a function of L shell bring one to the conclusion that the main source of radiation belt light isotopes at Resurs altitudes (∼800 km) and for energy greater than 10 MeV nucleon−1 is the interaction of trapped protons with residual atmospheric helium.

Supervised algorithms for particle classification by a transition radiation detector

Support vector machines (SVMs) provide an interesting computational paradigm for the classification of data from high-energy physics and particle astrophysics experiments. In this study, the classification power of SVMs is compared with those from standard supervised algorithms, i.e. likelihood ratio and artificial neural networks (ANN), using test beam data from the transition radiation detector prototype of the PAMELA satellite-borne magnetic spectrometer. Concerning signal/background discrimination, SVM and ANN show the best performance. Moreover, our analysis shows that the use of SVM allows an accurate estimate of the discrimination efficiency of unseen data points: indeed, since almost the same efficiency is obtained with or without the cross-validation technique, the performance of SVM appears to be stable. On the other hand, the ANN shows a tendency to overfit the data, while this tendency is not observed using SVM. For these reasons, SVM could be used in particle astrophysics experiments where, due to the harsh experimental conditions, efficient and robust classification algorithms are needed.