Nonparametric estimation of mark's distribution of an exponential Shot-noise process

Paul Ilhe (1, 2) , Eric Moulines (2) , François Roueff (2) , Antoine Souloumiac (1)

1 LOAD - Laboratoire Outils d'Analyse des Données
2 LTCI - Laboratoire Traitement et Communication de l'Information

In this paper, we consider a nonlinear inverse problem occurring in nuclear science. Gamma rays randomly hit a semiconductor detector which produces an impulse response of electric current. Because the sampling period of the measured current is larger than the mean inter arrival time of photons, the impulse responses associated to different gamma rays can overlap: this phenomenon is known as pileup. In this work, it is assumed that the impulse response is an exponentially decaying function. We propose a novel method to infer the distribution of gamma photon energies from the indirect measurements obtained from the detector. This technique is based on a formula linking the characteristic function of the photon density to a function involving the characteristic function and its derivative of the observations. We establish that our estimator converges to the mark density in uniform norm at a logarithmic rate. A limited Monte-Carlo experiment is provided to support our findings.

Keywords: Shot-noise; signal processing; pileup; Levy-driven Ornstein-Uhlenbeck process; nonparametric estimation; Markov process; empirical processes; β mixing

Domains: Computer Science [cs]; Signal and Image Processing; Mathematics [math]; Numerical Analysis [math.NA]; Physics [physics]; Nuclear Experiment [nucl-ex]

Paul Ilhe, Eric Moulines, François Roueff, Antoine Souloumiac. Nonparametric estimation of mark's distribution of an exponential Shot-noise process. Electronic Journal of Statistics , 2015, 9 (2), pp.3098-3123. ⟨10.1214/15-EJS1103⟩. ⟨hal-01164121v2⟩