Method for extracting discrete neutron components, from photo-nuclear reactions between photons and at least one chemical element to be detected, of a neutron spectrum from photo-nuclear reactions obtained by irradiation, of a material comprising said at least one chemical element to be detected, with a photon source, at least one energy of the photons of the source being greater than the photo-nuclear reaction threshold of said chemical element to be detected, using a multilayer and multichannel neural network having an architecture with a convolution stage and a deconvolution stage;
- wherein, if said at least one chemical element to be detected is chosen from carbon, nitrogen, oxygen or chlorine and if the photon source is a first multi-energy photon source, the method comprises:
- a preliminary step of training the neural network by supervised learning, which is performed by iteration using:
- first neutron spectra, from photo-nuclear reactions obtained by irradiating the material with bremsstrahlung photons;
- and
- second neutron spectra, each of the second neutron spectra being from photo-nuclear reactions induced by irradiating the material with photons of a same energy, which is greater than the photo-nuclear reaction threshold of the chemical element to be detected, and less than or equal to the maximum energy of the photons of said first multi-energy photon source,
- the first and second spectra being notional spectra established by a Monte-Carlo simulation, the first spectra being supplied at the input of the neural network, and the second spectra serving to compute a cost function intended to adjust the weights and biases of the neural network;
- and
- a prediction step, using the trained neural network, comprising:
- supplying, at the input of the trained neural network, the neutron spectrum from photo-nuclear reactions induced by irradiating the material with said first photon source;
- and
- if the irradiated material includes, in its composition, at least one chemical element chosen from carbon, nitrogen, oxygen, or chlorine, obtaining, at the output of the trained neural network and for at least one specific energy of interest of said first photon source, a predicted neutron spectrum, including discrete neutron components from photo-nuclear reactions of said chosen chemical element with said specific energy of interest;
- or
- wherein, if the material to be irradiated comprises said at least one chemical element to be detected chosen from carbon, nitrogen, oxygen or chlorine, and at least one other different chemical element, capable of emitting neutrons and which is not carbon, nitrogen, oxygen or chlorine, and if the photon source is a second, single-energy or multi-energy photon source, the method comprises:
- a preliminary step of training the neural network by supervised learning, which is performed by iteration using:
- first neutron spectra, from photo-nuclear reactions obtained by irradiating the material with bremsstrahlung, line or single-energy photons;
- and
- second neutron spectra, each of the second neutron spectra being from photo-nuclear reactions induced by irradiating the material with photons of an energy greater than the photo-nuclear reaction threshold of the chemical element to be detected and greater than the photo-nuclear reaction threshold of said other different chemical element;
- the first and second spectra being notional spectra established by a Monte-Carlo simulation, the first spectra being supplied at the input of the neural network, and the second spectra serving to compute a cost function intended to adjust the weights and biases of the neural network;
- and
- a prediction step, using the trained neural network, comprising:
- supplying, at the input of the trained neural network, the neutron spectrum from photo-nuclear reactions induced by irradiating the material with said second photon source;
- and
- if the irradiated material includes, in its composition, at least one chemical element chosen from carbon, nitrogen, oxygen or chlorine, obtaining, at the output of the trained neural network and for at least one specific energy of interest of said second photon source, a predicted neutron spectrum, including discrete neutron components from photo-nuclear reactions of said chemical element to be detected with said specific energy of interest.