Multispecies Neutron Transport Equation
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Abstract
The neutron transport equation (NTE) describes the flux of neutrons through inhomogeneous fissile medium. Whilst well treated in the nuclear physics literature, the NTE has had a somewhat scattered treatment in mathematical literature with a variety of different approaches. Within a probabilistic framework it has somewhat undeservingly received little attention in recent years; nonetheless, a few probabilistic treatments can be found. In this article our aim is threefold. First we want to introduce a slightly more general setting for the NTE, which gives a more complete picture of the different species of particle and radioactive fluxes that are involved in fission. Second we consolidate the classical \(c_0\)semigroup approach to solving the NTE with the method of stochastic representation which involves expectation semigroups. Third we provide the leading asymptotic of our multispecies NTE, which will turn out to be crucial for further stochastic analysis of the NTE in forthcoming work (Cox et al. 2019; Harris et al. 2018; Horton et al. 2018). The methodology used in this paper harmonises the culture of expectation semigroup analysis from the theory of stochastic processes against \(c_0\)semigroup theory from functional analysis. In this respect, our presentation is thus part review of existing theory and part presentation of new research results based on generalisation of existing results.
Keywords
Neutron transport equation Principal eigenvalue Semigroup theory Perron–Frobenius decompositionMathematics Subject Classification
Primary 82D75 60J80 60J75 Secondary 60J991 Introduction
The neutron transport equation (NTE) describes the flux of neutrons across a directional planar crosssection in an inhomogeneous fissile medium (typically measured is number of neutrons per cm\(^2\) per second). As such, flux is described as a function of time, t, Euclidian location, \(r\in \mathbb {R}^3\), direction of travel, \(\Omega \in \mathbb {S}_2\), speed \(c>0\) (and hence velocity \(\upsilon = c\Omega \)), and neutron energy, \(E\in \mathbb {R}\). It is not uncommon in the physics literature, as indeed we shall do here, to assume that energy is a function of velocity (\(E = m\upsilon ^2/2\)), thereby reducing the number of variables by one. This allows us to describe the dependency of flux more simply in terms of time and, what we call, the configuration variables\( (r, \upsilon ) \in D \times V\) where \(D\subseteq \mathbb {R}^3\) is a smooth, open, connected and bounded domain of concern such that \(\partial D\) has zero Lebesgue measure and V is the velocity space, which can now be taken to be \(V = \{v\in \mathbb {R}^3: \upsilon _{\texttt {min}}<v<\upsilon _{\texttt {max}}\}\), where \(0<\upsilon _{\texttt {min}}<\upsilon _{\texttt {max}}<\infty \).
Before stating the NTE, let us remind the reader of some elementary nuclear physics, which is required to describe the evolution of neutron flux. In the most basic of flux models, there are essentially only four processes at the level of the atomic nuclei which contribute to the evolution of neutron flux.
The first is spontaneous neutron emission from unstable nuclei. This comes from radioactive isotopes whose nuclei are excited. They cause what is known as nontransmutation emissions, in which a neutron is ejected with an escape velocity (neutron emission), or, conversely, what are called transmutation emissions in which the nucleus instantaneously fragments into two or more nuclei (spontaneous fission) with a range of possible masses, emitting one or more neutrons with escape velocities in the process.
The second process pertains to neutron scattering. This is where a neutron travelling with a given velocity passes in close proximity to an atomic nucleus, which, in our model, results in an instantaneous change of velocity.
The third process is neutroninduced fission. This is the classical setting in which a neutron travelling with a given velocity strikes an atomic nucleus sending it into an excited state, from which it instantaneously fragments into two or more nuclei, simultaneously releasing one or more neutrons.
The fourth and final process is neutron capture. In this setting, a neutron travelling with a given velocity strikes an atomic nucleus, but instead of causing nuclear fission, it is absorbed into the nucleus. It can also be the case that neutrons decay into other subatomic particles, and thus disappear from the system. To all intents and purposes, we can treat this as neutron capture.
When modelling the transmission of neutrons in a fissile material, those neutrons which have been released from nuclei are known as prompt neutrons.
With more advanced modelling, one can also take account of the fact that some of the processes described above can also involve other types of nuclear emissions, often in addition to neutrons. These include alpha and beta particles and gamma radiation. Whilst the former two are not sufficiently energetic to cause fission, sufficiently energetic gamma rays are able to induce fission.
Spontaneous fission and neutroninduced fission can also produce what are known as delayed neutrons. These are neutrons released from a fission product (isotope) some time after fission has occurred. In terms of modelling, they are spontaneous neutron emissions which occur at the site of neutroninduced fission but at a moment later in time. Delayed neutrons are only in a delayed state until they are released after which they are considered as prompt neutrons.
We refer to models which take account of the full range of flux profiles as multispecies models.
2 Neutron Transport Equation
Let us now write down the basic neutron transport equation (prompt neutrons only), which has been widely considered in a variety of physics and engineering literature (cf. [8, 28], to name but two classical references), and somewhat more sporadically studied in the mathematical literature. See [6, 17, 25] for the three most authoritative mathematical texts in more recent times, as well as e.g. [3, 4, 13, 22, 26] for some of the rarer examples of the probabilistic treatment of the NTE.
The notion of a solution of the form (2.1) turns out to be too strong to expect to make mathematical sense of it. This is predominantly due to the nondiffusive nature of the equation, in particular the nonlocal nature of the scattering and fission operators as well as regularity issues on the domain \(D\times V\) in relation to continuity properties of e.g. the operator \(\upsilon \cdot \nabla \). It is much more natural to look for solutions that belong to e.g. an appropriate \(L_2\) space. This is, moreover, helpful when looking to understand (2.1) as a backwards equation, rather than a forwards equation.
The reason for this difference in grouping of terms lies with how one reads the operators in terms of infinitesimal generators as a probabilist. Although this will not make any difference in the analysis of this paper, we keep to this notation for the sake of consistency with further related articles which offer a probabilistic perspective on the backwards NTE; see [5, 12, 14].
The NTE has played a prominent role in realworld modelling and, for many years, has found a home in commercial software which is used in the nuclear safety industry. In particular, this is most prominent in the modelling and design of environments which are exposed to radioactive material, from nuclear reactor cores and hospital equipment, through to equipment used to irradiate produce that is sold in supermarkets, thereby prolonging its shelflife. More recently, with the notion of human interplanetary space exploration becoming less of a scifi fantasy and more of a fast approaching reality, an understanding of how longlasting and compact nuclear power sources, for e.g. Moon or Mars bases has become increasingly important.
The approximation (2.8) can be seen as a functional version of the Perron–Frobenius Theorem and has given rise to a number of different numerical methods for estimating the value of the eigenvalue \(\lambda \) as well as the eigenfunctions \(\varphi \) and \(\tilde{\varphi }\). One approach pertains to the discretisation of (2.1) followed by the use numerical analytic methods; see [31]. Another pertains to the previously alluded to identification of the solution to the NTE as the linear semigroup of a Markov branching process, which in turn implies Monte Carlo methods involving the simulation of the aforesaid branching process. Such methods are computationally expensive, as branching processes, being treelike structures, are complex to simulate, e.g. from the point of view of parallelisation. In related papers to this one, we will discuss a new Monte Carlo approach to the NTE based on some of the stochastic analysis we deal with in this article as well as in related work undertaken by the authors of this paper; see [5, 12, 14].
The aim of this paper is manifold. First and foremost, we aim to reposition the theory of the NTE into a contemporary probabilistic setting. We will do this by explaining a precise relationship between the NTE and a two different families of Markov processes via Feynman–Kac type formulae. Indeed, this article is one of a cluster of forthcoming pieces of work, which take a new and predominantly probabilistic point of view of the NTE; cf. [5, 12, 14]. Next we want to introduce the notion of the (multispecies) NTE into the literature, which generalises (2.1) by simultaneously modeling the flux of all species of particles and radiation involved in the process of nuclear fission. In doing so we will show that, just as in the classical setting, one may develop the notion of a lead eigenvalue and eigenfunction, which is an important part of describing fissile stability. As such, the current article is part review of existing theory and part presentation of new research results based on generalisation of existing results.
Together with the accompanying papers [5, 12, 14], we believe that the probabilistic perspective presented here, i.e. coupling the solutions to the NTE with averaging procedures of certain Markov processes, opens up the possibility of many questions that can be considered at depth in the arena of stochastic analysis and Monte Carlo algorithms, which are currently missing from the literature. Indeed, returning to the kind of environments seen in Fig. 1, there are many questions concerning how to analyse and numerically generate the leading eigenfunctions and eigenvalue to a reasonable degree of precision. Such questions might include: What is the connection of the eigendecomposition discussed in this paper and e.g. Rtheory or the theory of general Harris recurrence for stochastic processes (cf. [9, 23, 24])? How do different stochastic representations lead to different Monte Carlo simulations?Based on stochastic representation how does one measure convergence of Monte Carlo algorithms? How strong can they be predicted to be? What kind of variance reduction techniques does stochastic representation suggest?Does the inclusion of multispecies models make estimation of the leading eigenvalue more accurate?
3 Organisation of the Paper
In the next section, we give a brief overview of the key mathematical literature for the NTE. (Note we do not stray beyond mathematical literature, as the physics and engineering literature is significantly more expansive.) Thereafter in Sect. 5, we introduce the multispecies NTE (MNTE) and its rigorous formulation, existence, uniqueness and asymptotics in the setting of an abstract Cauchy problem. In particular, we show how the unique solution is identified as a \(c_0\)semigroup in the appropriate \(L_2\) space. In Sect. 6, we introduce a spatial branching process that is constructed using the cross sections that appear in the NTE to describe its stochastic evolution. Here we introduce its expectation semigroup. In Sect. 7, we provide a second stochastic representation to the expectation semigroup introduced in the previous section via a classical method of the manytoone formula.
Ideally, we would like to claim that the expectation semigroup discussed in Sects. 6 and 7 agree with the \(c_0\)semigroup introduced in Sect. 5 (its formal definition appearing just above Theorem 5.2). This is particularly desired as it forms the foundations of how Monte Carlo simulation of the physical process can be used to develop a numerical solution to the MNTE. In Sect. 8, we consolidate the two notions of semigroup and show that there is partial agreement in an appropriate sense. As far as we are aware, this is a point which is currently not clearly discussed in the literature. Finally we end the paper with a proof of one of the main theorems in Sect. 6 which provides the asymptotic behaviour of the solution to the MNTE in terms of the lead eigenfunction. This is a new result in the multispecies setting in the sense that we have allowed for multiple types of prompt emissions (both particles and radioactive emissions) rather than the case of only one type of prompt emission dealt with in [25]; we also allow for multiple types of delayed emissions (that is, emissions that are preemptively held in an unstable radioactive isotope product from an earlier fission event). Our proof nonetheless takes inspiration from the classical approach of [6, 25], and remains loyal to the techniques there.
4 Historical Remarks on the Mathematical Treatment of the NTE
Classical texts such as Davison and Sykes [8] were once hailed as a bible of mathematical knowledge during the 1950s post Manhattan Project era when rapid technological advances lead to the construction of the very first nuclear reactors driving commercial power stations. Around this time, there was an understanding of how to treat the NTE in special geometries and also by imposing an isotropic scattering and fission, see for example Lehner [18] and Lehner and Wing [19, 20]. It was also understood quite early on that the natural way to cite the NTE is via the linear differential transport equation associated to a suitably defined operator on a Banach space. Moreover, it was understood that in this formulation, a spectral decomposition should play a key role in representing solutions, see e.g. Jörgens [15], Pazy and Rabinowitz [29]. This notion was promoted by the work of R. Dautray and collaborators, who showed how \(c_0\)semigroups form a natural framework within which one may analyse the existence and uniqueness of solutions to the NTE; see [6, 7]. Moreover, a similar approach has also been pioneered by MokhtarKharroubi [25].
The probabilistic interpretation of the NTE was appreciated from the very first treatments of the NTE (see e.g. [8] and references therein, as well as Bell [2]). Indeed, the physical description of nuclear fission, when governed by basic principles, allowing for additional randomness, is nothing more than a branching Markov process. Numerous derivations of the NTE from this perspective can be found in the literature to various degrees of rigour; see e.g. Bell [2], Mori et al. [26], Pazy and Rabinowitz [30], Lewins [21] and Pázsit and Pál. [28].
A more modern treatment of the probabilistic representation through Feynman–Kac expectation semigroups and the connection to the theory of Markov diffusions is found in Dautray et al. [7]. A purely probabilistic can be found in Lapeyre et al. [17]. See also the accompanying papers to this one [5, 12, 14].
We finish this section by noting that there is a body of literature that pertains to the numerical analysis of the NTE. Recent work in this field, including the notion of uncertainty quantification, can be found in e.g. [22, 27, 31]. See also references therein.
5 Multispecies (Backwards) Neutron Transport Equation
In the following discussion, rather than talk about typed particles, we prefer to say typed ‘emissions’ as the different types correspond to particles, electromagnetic rays (e.g. gamma rays) and isotopes (which are considered to be carriers for delayed emissions).
Let us now introduce an advanced version of the NTE, which takes account of both nontransmutation emissions as well as transmutation emissions, in particular, allowing for the inclusion of all types of emissions, prompt neutrons, delayed neutrons, alpha, beta and gamma emissions etc. An important feature (and arguably a restriction) of our model is that only prompt neutrons can produce delayed emissions.
Assumption 5.1
All cross sections are nonnegative, measurable and uniformly bounded from above. Moreover, all prompt emissions scatter and hence, without loss of generality, we also assume that for for each \(i=1,\ldots , \ell \), the terms \(\sigma ^i_{\texttt {s}}\pi ^i_{\texttt {s}}\) are uniformly bounded away from the origin on \(D\times V\). We need not assume that the cross sections \(\sigma ^i_{\texttt {f}}\pi ^{i,j}_{\texttt {f}}\) are uniformly bounded away from the origin for \(1\le i,j\le \ell \), with the exception of \(i = 1\), for which it only makes sense that \(\sigma ^1_{\texttt {f}}m^j\) is uniformly bounded away from 0 for each \(j = \ell +1, \ldots , m.\) Without loss of generality, we can assume that \(0<\lambda _{\ell + 1}<\cdots <\lambda _m\).
Classical literature suggests that one can integrate delayed neutrons into the setting of the NTE by adding an inhomogeneity corresponding to the integral of incoming delayed neutrons from time \(\infty \) to the present; see e.g. [8]. A vectorial representation such as the one above can be found, however, in the work of [25]. There, only one category of prompt emissions are considered with multiple species of delayed neutrons.
It is not often that MNTE is stated as above in (5.1) and (5.2) in existing literature; see e.g. [25] for presentation of the NTE in a similar vectorial format, which allows for only one category of prompt neutrons.
The requirement that all cross sections are uniformly bounded is by far not the weakest assumption we can make (see e.g. Chapter XXI of [6]).
The precise mathematical sense in which we must understand solutions to the coupled system (5.1) and (5.2) needs some discussion before we can proceed. To this end, we shall first introduce some notational conventions.
 (i)
\(\texttt {V}_0 = \mathrm{Id}\),
 (ii)
\(\texttt {V}_{t+s}[g] = \texttt {V}_t[\texttt {V}_s[g]]\), for all \(s, t\ge 0\), \(g\in \prod _{j= 1}^m L_2({D}\times V)\) and
 (iii)
for all \(g\in \prod _{j= 1}^m L_2({D}\times V)\), \(\lim _{h\rightarrow 0}\texttt {V}_h[g]  g =0\).
Theorem 5.2
Let \(({\overset{_\leftarrow }{\texttt {A} }}, \mathrm{Dom}({\overset{_\leftarrow }{\texttt {A} }}))\) be the generator of a \(c_0\)semigroup \((\texttt {V} _t, t\ge 0)\). If \(g\in \mathrm{Dom}({\overset{_\leftarrow }{\texttt {A} }})\), then \(u_t: = \texttt {V} _t[g]\) is a representation of the unique classical solution of (5.5).
The reader may well have wondered where the second boundary condition in (5.3) has gone in the above formulation. This is a matter of interpretation of \(({\overset{_\leftarrow }{\texttt {T}}}, \mathrm{Dom}({\overset{_\leftarrow }{\texttt {T}}}))\), and hence the generator \(({\overset{_\leftarrow }{\texttt {A}}}, \mathrm{Dom}({\overset{_\leftarrow }{\texttt {A}}}))\), as we now discuss.
One of our main results will be to establish the asymptotic (2.8) but now in the current setting. Recall that we have assumed that \(D\subseteq \mathbb {R}^3\) is a smooth open pathwise connected bounded domain of concern such that \(\partial D\) has zero Lebesgue measure.
Theorem 5.3
 (i)
the neutron transport operator \(\overset{_\leftarrow }{\texttt {A} }\) has a simple and isolated eigenvalue \(\lambda _c > \lambda _{\ell +1}\), which is leading in the sense that \(\lambda _c = \sup \{\mathrm{Re}(\lambda ): \lambda \text { is an eigenvalue of }\overset{_\leftarrow }{\texttt {A} }\}\) and which has corresponding nonnegative right and left eigenfunctions in \( \prod _{i=1}^mL_2(D\times V)\), \(\varphi \) and \({\tilde{\varphi }}\) respectively and
 (ii)there exists an \(\varepsilon >0\) such that, as \(t\rightarrow \infty \),for all \(f\in \prod _{i=1}^mL_2(D\times V)\), where \(({\texttt {V} }_t, t\ge 0)\) is defined in (5.6). To give a precise value for \(\varepsilon \), suppose we enumerate the eigenvalues of \(\overset{_\leftarrow }{\texttt {A} }\) in decreasing order by the set \(\{\lambda ^{(1)}, \ldots , \lambda ^{(n)}\}\) (noting from earlier that we have at least \(\lambda ^{(1)}= \lambda _c\)). Then \(\lambda ^{(n)}> \lambda _{\ell + 1}\) and we can take any \(\varepsilon \) such that \(\varepsilon <\lambda _c (\lambda ^{(2)}\vee (\lambda _{\ell +1} ))\) where we understand \(\lambda ^{(2)} = \infty \) if \(n = 1\).$$\begin{aligned}  \mathrm{e}^{\lambda _c t}{\texttt {V} }_t[f] \langle f, \tilde{\varphi } \rangle \varphi _2 = O(\mathrm{e}^{\varepsilon t}), \end{aligned}$$(5.13)
Remark 5.1
It could be argued that the assumptions in the above theorem rule out the possibility that we may, for example, include alpha or beta emissions emissions in the model for that particular conclusion. Whilst alpha and beta emissions may scatter, they are not energetic enough to cause fission. The irreducibility conditions (5.11) and (5.12) would thus fail. On the other hand, it is also known that when such particles are energetic enough, they can draw gamma radiation or positrons out of nuclei when passing in close proximity. If the latter are sufficiently energetic, then they can induce fission.
6 Multispecies Neutron Branching Process

The emission leaves the domain, at which point it is killed.
 Independently of all other emissions, a scattering event occurs when a emission comes in close proximity to an atomic nucleus and, accordingly, makes an instantaneous change of velocity. For an emission in the system of type \(i \in \{1, \dots , \ell \}\) with initial position and velocity \((r,\upsilon )\), if we write \(T^i_{\texttt {s}}\) for the random time until the next scattering occurs, then, independently of any other physical event that may affect the emission,$$\begin{aligned} \Pr (T^i_{\texttt {s}}>t) = \exp \left\{ \int _0^t \sigma ^i_{\texttt {s}}(r+\upsilon s, \upsilon )\mathrm{d}s \right\} . \end{aligned}$$(6.1)

When scattering of an emission of type \(i \in \{1, \dots , \ell \}\) occurs at spacevelocity \((r,\upsilon )\), the new velocity is selected independently with probability \(\pi ^i_{\texttt {s}}(r, \upsilon , \upsilon '){ d }\upsilon '\).
 Independently of all other emissions, a fission event occurs when an emission smashes into an atomic nucleus. For an emission in the system with initial position and velocity \((r,\upsilon )\), we will write \(T^i_{\texttt {f}}\) for the random time that the next fission occurs. Then independently of any other physical event that may affect the emission,$$\begin{aligned} \Pr (T^i_{\texttt {f}}>t) = \exp \left\{ \int _0^t \sigma ^i_{\texttt {f}}(r+\upsilon s, \upsilon )\mathrm{d}s \right\} . \end{aligned}$$(6.2)
 When fission occurs, the smashing of the atomic nucleus releases a random number of other prompt emissions of type \(i =1,\ldots , \ell \), say \(N^{i,j}\ge 0\), which are ejected from the point of impact with randomly distributed, and possibly corollated, velocities, say \(\{\upsilon ^{i,j}_k: k = 1, \ldots , N^{i,j}\}\). When fission occurs at location \(r\in D\) from a emission with incoming velocity \(\upsilon \in {V}\), the quantity \(\pi ^{i,j}_{\texttt {f}}(r, \upsilon , \upsilon '){ d }\upsilon '\) describes the average number of type j prompt emissions released from nuclear fission with outgoing velocity in the infinitesimal neighbourhood of \(\upsilon '\). In particular$$\begin{aligned} \int _A\pi ^{i,j}_{\texttt {f}}(r, \upsilon , \upsilon '){ d }\upsilon ' = \mathrm{E}\left[ \sum _{k =1}^{N^{i,j}}\mathbf {1}_{(\upsilon ^{i,j}_k\in A)} \right] , \quad A\in \mathcal {B}(V). \end{aligned}$$

Note, the possibility that \(\Pr (N^{i,j} = 0)>0\) is possible. If \(i = j = 1\) then this is tantamount to neutron capture or further decomposition into subatomic particles which are not counted.

Further, if the initial emission is a (type 1) neutron, a fission event (occurring at rate \(\sigma ^1_{\texttt {f}}\)) may result in the production of unstable isotopes (which later release delayed emissions). On this event, an average number, \(m^j(r, \upsilon )\), of type \(j \in \{\ell +1, \dots , m\}\) isotopes will be produced from a collision at position r from a neutron with incoming velocity \(\upsilon \). The isotopes will inherit the configuration of the incoming nucleus at the time of collision.
In all cases, it is a natural make the following physical assumption which will remain in force throughout.
Assumption 6.1
As we have assumed that all cross sections are uniformly bounded, ignoring spatial trajectories of neutrons (in particular those that are killed by leaving the domain D), it is straightforward to compare the growth of \((\psi _t[g], t\ge 0)\) against that of a continuoustime GaltonWatson process with growth rate \( \eta \{(m\times n_{\texttt {max}})1\} \), where \(\eta = \sup _{1\le i\le \ell , r\in D, \upsilon \in V}\sigma ^i_{\texttt {f}}(r,\upsilon ) + \max _{\ell +1\le i\le m}\lambda _i\).
The rate of growth \( \eta \{(m\times n_{\texttt {max}})1\} \) simply assumes that each emission of type i gives rise to at most \(n_{\texttt {max}}\) emissions of any other type and at a rate which is uniformly bounded by a uniform upper bound of all possible rates at which fission events occur. Note this rate takes account of the emission count introduced into the system at a fission event and the single emission removed from the system which caused the fission event.
It is also straightforward to stochastically upper bound the process \(\langle 1, X_t\rangle \), \(t\ge 0\), by the aforesaid continuoustime Galton Watson process on the same probability space. The latter process branches whenever X does, topping up the number of offspring always to \(n_{\texttt {max}}\), but also it has additional independent branching events at rate \((\eta \mathbf {1}_{(1\le i\le \ell )}\sigma ^i_{\texttt {f}}(r,\upsilon )  \mathbf {1}_{(\ell +1\le i\le m)}\lambda _i)\) always producing precisely \(n_{\texttt {max}}\) offspring of each of the m possible emissions.
Nonetheless, classical literature supports the view that it is the physical processes, i.e. in this setting the MNBP, that provides a stochastic representation of the solution to the backward MNTE. The authors are not aware of a formal proof of this fact. We will nonetheless try to address this point shortly in Sect. 8. In the mean time, let us present an alternative ‘mild’ form of the MNTE (also called a Duhamel solution in the PDE literature) which the semigroup \((\psi _t,t\ge 0)\) more comfortably solves.
Lemma 6.1
Before proceeding to the proof, let us remark that, in the statement of the theorem, we are not working with \((\texttt {U}_t, t\ge 0)\) as a \(c_0\)semigroup on \(\prod _{i = 1}^m L_\infty (D\times V)\), but a pointwise shift operator. The reader will recall from the discussion preceding (5.10) that \((\texttt {U}_t, t\ge 0)\) cannot be defined as such for \(\prod _{i = 1}^m L_\infty (D\times V)\).
Proof of Lemma 6.1
7 Multispecies Neutron Random Walk and the Manytoone Lemma
A second probabilistic perspective for analysing the MNTE is possible, seems rarely to have been discussed in existing literature, if at all. This consists of collapsing the sum of the operators \({\overset{_\leftarrow }{\texttt {T}}} + {\overset{_\leftarrow }{\texttt {S}}}+{\overset{_\leftarrow }{\texttt {F}}}\) to take the form \({\overset{_\leftarrow }{\texttt {L}}}+\texttt {diag}({\beta })\) for an appropriate choice of \(\beta \), where \(\overset{_\leftarrow }{\texttt {L}}\) is the operator which is similar in structure to \({\overset{_\leftarrow }{\texttt {T}}} + {\overset{_\leftarrow }{\texttt {S}}}\). In essence, this transformation, which we will describe more rigorously in a moment, heuristically postulates that the operator \({\overset{_\leftarrow }{\texttt {T}}} + {\overset{_\leftarrow }{\texttt {S}}}+{\overset{_\leftarrow }{\texttt {F}}}\) can be reinterpreted via a Feynman–Kac formula as the infinitesimal generator of a single emission which undergoes linear transport and scattering and which also accumulates potential \(\beta \).
To describe this more precisely, we need to introduce the notion of a multispecies neutron random walk (MNRW). In the current setting this means a continuoustime typed random walk by \( (J_t, R_t, \Upsilon _t)\), \(t\ge 0\), on \(\{1,\ldots ,m\}\times (D\times V)\) with additional cemetery state \(\{\dagger \}\) when it exits the physical domain D or an emission otherwise disappears from the system. The MNRW is described by two fundamental quantities (which are functions of the current particle type, spatial position and velocity). First, a scattering rate \(\alpha ^i(r,\upsilon )\), \(i\in \{1,\ldots ,m\}, r\in D, \upsilon ,\upsilon '\in V\), such that \(\alpha ^i(r,\upsilon ) = \lambda _i\), for \(i \in \{\ell +1,\ldots ,m\}\). Second, a scattering probability kernel \(\pi ^{i,j}(r,\upsilon , \upsilon ')\), \(i,j\in \{1,\ldots , m\}, r\in D, \upsilon ,\upsilon '\in V\). In the spirit of the description of the MNBP, the MNRW is described as follows.

When the MNRW position moves out of D or e.g. it decomposes into an emission type that is not counted, or is captured in a nucleus, it is instantaneously killed.
 A scattering event occurs and, accordingly, the MNRW keeps the same emission type but makes an instantaneous change of velocity. If we write \(T^i_{\texttt {s}}\) for the random time until the next scattering occurs, then,$$\begin{aligned} \Pr (T^i_{\texttt {s}}>t) = \exp \left\{ \int _0^t \alpha ^i(r+\upsilon s, \upsilon )\mathrm{d}s \right\} . \end{aligned}$$(7.1)

When scattering of an emission of type \(i \in \{1, \dots , \ell \}\) occurs at spacevelocity \((r,\upsilon )\), the new velocity is selected independently with probability \(\pi ^i(r, \upsilon , \upsilon '){ d }\upsilon '\).
Indeed, by conditioning the expectation in the definition of \(\phi _t[g]\) on the first scattering event, and then appealing to the Lemma 1.2, Chapter 4 in [10] in a similar manner to what was done in the proof of Lemma 6.1, one easily deduces the below result. In the the spatial branching process literature, this would be called a ‘manytoone’ lemma.
Lemma 7.1
For \(g\in \prod _{i = 1}^m L^+_\infty (D\times V)\), the two expectation semigroups \((\phi _t[g], t\ge 0)\) and \((\psi _{t}[g], t\ge 0)\) agree.
8 Consolidating the ACP with the Expectation Semigroup
We want to understand how the \(\prod _{i= 1}^m L_2({D}\times V)\) semigroup \((\texttt {V}_t, t\ge 0)\) that represents the unique solution to the Abstract Cauchy Problem (5.5) relates to the expectation semigroups \((\psi _t, t\ge 0)\) and \((\phi _t, t\ge 0)\) that offer two different stochastic representations to the mild Eq. (6.8).
We start by noting that if \(g\in \prod _{i = 1}^m L^+_\infty (D\times V)\), then, on account of the fact that \(\mathrm{Vol}(\prod _{i =1}^m (D\times V)) =( \int _{D\times V}{ d }r{ d }\upsilon )^m<\infty \), we also have \(g\in \prod _{i= 1}^m L_2({D}\times V)\). Since it is unclear whether \((\psi _t[g], t\ge 0)\) is well defined for all \(g\in \prod _{i= 1}^m L_2({D}\times V)\), it makes makes sense to consider the comparison with \((\texttt {V}_t[g], t\ge 0)\) (defined in (5.6)) for the more restrictive choice \(g\in \prod _{i = 1}^m L_\infty (D\times V)\). The natural setting in which to make the comparison is in the space \(\prod _{i= 1}^m L_2({D}\times V)\) as, by (6.4), \(\psi _t[g] _\infty <\infty \) and the latter implies \(\psi _t[g] _2<\infty \), again thanks to the fact that \(\mathrm{Vol}(\prod _{i =1}^m (D\times V))<\infty .\)
Theorem 8.1
If \(g\in \prod _{i = 1}^m L^+_\infty (D\times V)\) then, for \(t\ge 0\), \(\texttt {V} _t[g] = \psi _t[g]\) on \(\prod _{i= 1}^m L_2({D}\times V)\), i.e. \(\texttt {V} _t[g]  \psi _t[g] _2 = 0\).
Before moving to its proof, the reader should take care to note that this does not imply that \((\texttt {V}_t, t\ge 0)\) and \((\psi _t, t\ge 0)\) agree as \(c_0\)semigroups on \(\prod _{i= 1}^m L_2({D}\times V)\). In particular, the comparison between the two semigroup operators is only made on \(\prod _{i = 1}^m L_2(D\times V)\), and \((\psi _t, t\ge 0)\) was not (and in fact cannot be) shown to demonstrate the strong continuity property on \(\prod _{i= 1}^m L_2({D}\times V)\).
Remark 8.1
If we consider Theorem 8.1 in light of Theorem 5.3, noting that \((\psi _t[g],t\ge 0)\) is a uniformly bounded sequence, it is tempting to want to say that the leading eigenfunction \(\varphi \) belongs to \(\prod _{i = 1}^m L_\infty (D\times V)\). This is not the case necessarily and remains to be proved. In the setting of a single type of emission, this will be demonstrated in the forthcoming paper [14].
Proof of Theorem 8.1
The conclusion of this section is that it is not unreasonable to now understand the expectation semigroups \((\psi _t[g],t\ge 0)\) and \((\phi _t[g],t\ge 0)\) for nonnegative, bounded and measurable g on \(D\times V\) as the ‘solution’ to the MNTE in place of \((\texttt {V}_t[g], t\ge 0)\) for the same class of g. Indeed, the two agree both in \(\prod _{i= 1}^m L_2({D}\times V)\) and hence \(({ d }r\times { d }\upsilon )\)Lebesgue almost everywhere.
The reader will also note that from the perspective of Monte Carlo simulation, the expectation semigroup \( (\phi _t[g],t\ge 0)\) carries the potential to be exploited in a way that \( (\psi _t[g],t\ge 0)\) cannot. More precisely, where branching trees are difficult to simulate and are not convenient for Monte Carlo computational parallelisation, random walks are. This simple idea is explored in greater detail in the accompanying paper to this one [5].
9 Asymptotic Behaviour of the MNTE: Proof of Theorem 5.3
In this section we return to the fundamental notion that the solution to the MNTE in the form (5.5) is described by its leading asymptotics for large times. That is to say, we give the proof of Theorem 5.3. Our proof follows closely ideas found in Chapters 4 and 5 of [25].
Recall that the quantities \(\alpha ^i\), \(\pi ^{i,j}\), \(\beta ^i\), \(i,j =1,\ldots ,m\) were defined in (7.3), (7.4) and (7.5) respectively. They were arranged into the operator \(\overset{_\leftarrow }{\texttt {A}}= \overset{_\leftarrow }{\texttt {T}}+\overset{_\leftarrow }{\texttt {S}}+\overset{_\leftarrow }{\texttt {F}}\), such that Dom\((\overset{_\leftarrow }{\texttt {A}})=\) Dom\(({\overset{_\leftarrow }{\texttt {T}}})\), described in (5.9).
Theorem 9.1
(Krein–Rutman theorem) Let X be a Banach space and suppose it contains a convex cone \(\mathcal {C}\) such that \(\mathcal {C}  \mathcal {C}: = \{h = fg: f, g\in \mathcal {C}\}\) is dense in X. Suppose \(\mathcal {L}\) is a positive compact linear operator on X such that \({r}(\mathcal {L}) :=\sup \{\lambda  : \lambda \in \Sigma (\mathcal {L})\} > 0\), where \(\Sigma (\mathcal {L})\) is the spectrum of the operator \(\mathcal {L}\). Then \({r}(\mathcal {L})\) is an eigenvalue of \(\mathcal {L}\) with a corresponding positive eigenfunction.
Our proof of Theorem 5.3 requires the following intermediary result below. Before stating it, the reader is reminded that the eigenvalues \(\lambda _{\ell +1},\ldots , \lambda _m\) are arranged so that \(\lambda _{\ell +1}\) is the smallest. Thus, the condition \(\lambda >\lambda _{\ell +1}\) ensures that \(\texttt {K}^\circ (\lambda )\) is well defined. In particular, \((\lambda \texttt {I}_{m\ell }+\Lambda )\) is invertible. We will use the obvious meaning for \({\texttt {I}}_\ell \).
Proposition 9.1
Under the assumptions of Theorem 5.3, for each \(\lambda > \lambda _{\ell +1}\), \({r}\big ((\lambda \texttt {I} _\ell  T )^{1}{} \texttt {K} ^\circ (\lambda )\big )\) is the leading eigenvalue of \((\lambda \texttt {I} _\ell  \texttt {T} )^{1}{} \texttt {K} ^\circ (\lambda )\) with a corresponding positive eigenfunction \(\varphi ^\circ _{\lambda }\).
Proof
In relation to the Krein–Rutman theorem stated above, our Banach space is \(X = \prod _{i = 1}^m L_2(D \times V)\) and the corresponding cone is \(\mathcal {C} = \prod _{i = 1}^m L^+_2(D \times V)\). It is clear that this cone is convex, and since every \(L_2\) function can be written as the difference of its positive and negative parts, \(\mathcal {C}\) satisfies the assumptions of the theorem. We now break the rest of the proof into a number of steps which are stated with a proof immediately afterwards.
Step 1 First we claim that \((\lambda \texttt {I}_\ell  \texttt {T})^{1}\texttt {K}^\circ (\lambda )\) is a compact operator.
Fix \(1\le i,j\le m\). By Fubini’s Theorem we have that \(r\mapsto \texttt {K}_{i,j}f(r,\upsilon )\) is measurable for \(g\in L_2(D\times V)\). The operators \( \texttt {K}_{i,j}\) are also integral operators and therefore are continuous on \(L_2(V)\) and compact. The assumed piecewise continuity of the cross sections \(\sigma _{\texttt {s}}^i\pi ^i_{\texttt {s}}\) and \(\sigma _{\texttt {f}}^i\pi ^{i,j}_{\texttt {f}}\) and the boundedness of the domain V is sufficient to ensure that \(r\mapsto \texttt {K}_{i,j}\cdot (r,\cdot )\) is continuous under the operator norm on \(L_2(V)\) and hence \(\{\texttt {K}_{i,j}\cdot (r,\cdot ): r\in D\}\) forms a relatively compact set in the space of linear operators on \(L_2(V)\). With these properties, the mapping \(r\mapsto \texttt {K}_{i,j}\cdot (r,\cdot )\), for \(r\in D\), is said to be regular. One similarly (but more easily) shows that \(r\mapsto \texttt {M}_{i,j}\cdot (r,\cdot )\) is regular for \(r\in D\) as operators on \(L_2(V)\). By linearity, this implies that, for \(1\le i,j\le \ell \), the mapping \(r\mapsto K^\circ (\lambda )_{i,j}\) is also regular. Hence, by [25, Theorem 4.1], \((\lambda \texttt {I}_\ell  \texttt {T})^{1}\texttt {K}^\circ (\lambda )\) is a compact operator.
Remark 9.1
It is precisely at the application of [25, Theorem 4.1] that we need the convexity of the domain D, as this is required within the aforesaid result.
Step 2 Next we show that \((\lambda \texttt {I}_\ell  \texttt {T})^{1}\texttt {K}^\circ (\lambda )\) is a positive irreducible operator.
Step 3 We claim that there exists a nonnegative eigenfunction \( 0\ne \varphi _\lambda \in \prod _{i = 1}^\ell L_2(D\times V) \) for the operator \((\lambda \texttt {I}_\ell  \texttt {T})^{1}\texttt {K}^\circ (\lambda )\) with eigenvalue that agrees with \({r}\big ((\lambda \texttt {I}_\ell  \texttt {T})^{1}\texttt {K}^\circ (\lambda )\big )\).
We use de Pagter’s Theorem, cf. [25, Theorem 5.7], which says that the spectral radius of an irreducible operator is strictly positive; that is to say \({r}\big ((\lambda \texttt {I}_\ell  \texttt {T})^{1}\texttt {K}^\circ (\lambda )\big )>0\). In turn the Krein–Rutman theorem 9.1 states that \({r}\big ((\lambda \texttt {I}_\ell  \texttt {T})^{1}\texttt {K}^\circ (\lambda )\big )\) is thus an eigenvalue for the operator \((\lambda \texttt {I}_\ell  \texttt {T})^{1}\texttt {K}^\circ (\lambda )\) with a corresponding nonnegative eigenfunction \(\varphi ^\circ _{\lambda }\). \(\square \)
Proof of Theorem 5.3
(i) In looking for a nonnegative eigenfunction of \(\overset{_\leftarrow }{\texttt {A}}\) with real eigenvalue, our earlier discussion tells us we must equivalently look for a solution to (9.5) and hence (9.7). This is equivalent to finding a real value \(\lambda _c\) such that \(r\big ((\lambda _c \texttt {I}_\ell  \texttt {T})^{1}\texttt {K}^\circ (\lambda _c)\big ) = 1\). We again achieve this goal in steps.
We first note that since we have shown that \(\lambda _c \in \sigma (\varvec{A})\), in particular that the spectrum is nonempty, it follows from [25, Theorem 5.2] that \(s(\varvec{A}) \in \sigma (\varvec{A})\). Now suppose that \(\lambda _c \ne s(\varvec{A})\) so that, in particular, \(\lambda _c < s(\varvec{A})\). Then, thanks again to [25, Lemma 8.1], \(r\big ((s(\varvec{A}) \texttt {I}_{\ell } \texttt {T})^{1}\texttt {K}^\circ (s(\varvec{A}))\big ) < 1\) and so 1 is not an eigenvalue of \((s(\varvec{A}) \texttt {I}_{\ell } \texttt {T})^{1}\texttt {K}(s(\varvec{A}))\). Said another way, this means that \(s(\varvec{A})\) is not an eigenvalue of \(\varvec{A}\) (and hence of \(\overset{_\leftarrow }{\texttt {A}}\)), leading to a contradiction. Algebraic and geometric simplicity of \(\lambda _c\) follows from [6, Remark 12] and [6, Theorem 7(iii)], respectively. \(\square \)
Before turning to the proof of Theorem 5.3 (ii), we must state another intermediary result which is translated from a general setting of Banach operators to our current situation; cf. [25, Theorem 4.1] and [1, p. 359, Theorem 22].
Proposition 9.2
Note the Theorem from which the above proposition is derived in [1, p. 359, Theorem 22] requires as a sufficient condition that \((\lambda {{\varvec{I}}}  \varvec{T})^{1}\varvec{K}\) is compact, where \(\varvec{I}\) is an \(m\times m\) identity matrix. This fact easily follows from the conclusion in Step 1 of the proof of Proposition 9.1.
Finally we can complete the proof of Theorem 5.3.
Proof of Theorem 5.3
(ii) It is also easy from the structure of \(\varvec{T}\) that \(\lambda _{\ell +1},\ldots , \lambda _{m}\), belong to its spectrum. Moreover, for all \(i = 1,\ldots , \ell \), \(s(\overset{_\leftarrow }{\texttt {T}}_i  \sigma ^i) = \infty \). Since \(\lambda _{\ell + 1}\) is the largest of these eigenvalues, and \(\lambda _c>\lambda _{\ell + 1}\) (from part (i) of Theorem 5.3), Proposition 9.2 tells us that \(\sigma (\varvec{A}) \cap \{\lambda : {\mathrm{Re}}(\lambda )>\lambda _{\ell +1}\}\) contains at least one isolated eigenvalue with finite (algebraic) multiplicity (i.e. the lead eigenvalue \(\lambda _c\)).
Footnotes
 1.
Here and everywhere else in the document, \(\nabla \) is the gradient operator with respect to the variable \(r\in \mathbb {R}^3\).
 2.
The authors are grateful to Prof. Paul Smith from Wood who has given us permission to use these images which were constructed with Wood nuclear software ANSWERS.
 3.
Strictly speaking the reality is that, nuclear reactors are kept in a slightly supercritical state. The reason for this is that at criticality, as proved in [12], neutron activity will eventually die out.
 4.
A function is piecewise continuous if its domain can be divided into an exhaustive finite partition (e.g. polytopes) such that there is continuity in each element of the partition. This is precisely how cross sections are stored in numerical libraries for modelling of nuclear reactor cores.
Notes
Acknowledgements
We are indebted to Paul Smith and Geoff Dobson from the ANSWERS modelling group at Wood for the extensive discussions as well as hosting at their offices in Dorchester. We would also like to thanks Minmin Wang, Ivan Graham, Matt Parkinson and Denis Villemonais for useful discussions. Finally we would like to thank an enthusiastic referee for their comments and support of this article which is part review, part new results.
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