8.2. Dispersion of a Free Gaussian Wave Packet: Particle Trajectories and Velocities from Purely Classical Physics
In our thermodynamic approach to quantum behaviour, a particle of energy
is characterized by an oscillator of angular frequency
, which itself is a dissipative system maintained in a nonequilibrium steady-state by a permanent troughput of energy, or heat flow, respectively. We recall that the latter is a form of kinetic energy different from the “ordinary” kinetic energy of the particle, as it represents an additional, external contribution to it, like, e.g., from the presence of zero point fluctuations. The total energy of the whole system (
i.e., the particle as the “system of interest” in a narrower sense and the heat flow constituting the particle’s thermal embedding) is assumed as
where
is said additional, fluctuating momentum of the particle of mass
.
For the following, it will be helpful to let ourselves be guided by the picture provided by the “walking bouncers” introduced previously, which we shall further on simply call “walkers” (
i.e., in agreement with the use of the word by Couder’s group). So, with a walker one is confronted with a rapidly oscillating object, which itself is guided by an environment that also contributes some fluctuating momentum to the walker’s propagation. In fact, the walker is the cause of the waves surrounding the particle, and the detailed structure of the wave configurations influences the walker’s path, just as in our thermodynamic approach [
2,
3,
4] the particle both absorbs heat from and emits heat into its environment, both cases of which can be described in terms of momentum fluctuations.
Let us first consider the emergence of “well ordered” diffusion waves out of the “erratic”, Brownian-type diffusions of myriads of single sub-quantum particles through their thermal environments. Being swept along with a diffusion wave, with initial (
) location
and diffusion velocity
, a quantum particle’s distance to the heat accumulation’s center
at time
will be
such that one obtains the r.m.s. of (8.2.2) as
Now we introduce the
central argument of the present chapter: we assume, as an emerging result out of the statistics of a vast number of diffusion processes, the complete statistical independence of the velocities
and
, and thus also of
and the positions
:
This is justified considering the statistics of huge numbers, millions of millions of diffusive sub-quantum Brownian motions, which are supposed to bring forth the emergence of said larger-scale collective phenomenon,
i.e., the diffusion wave fields as solutions to the heat equation [
3]. (In our associative picture, these are nothing but the analogy to the walkers’ Faraday waves emitted with some fixed frequency.) In other words, Equation (8.2.2) represents the effect of collectively “smoothing out” the “erratic” processes of individual Brownian motions. Thereby, the mean convective and diffusion velocities must be unbiased (lest one introduces new physics), and thus linearly uncorrelated. (Note that it was exactly the corresponding average orthogonality of momentum and momentum changes which has led to a first new derivation of the Schrödinger equation [
33], as well as the subsequent one based on nonequilibrium thermodynamics [
2,
3].)
Therefore, with the thus introduced Ord-type projection,
i.e., the orthogonality of classical (convective) momentum on one hand, and its associated diffusive momentum on the other, one gets rid of the term linear in
in Equation(8.2.3), and thus of irreversibility, and one obtains
Equation (8.2.5) is the result obtained for the “pure” emergent diffusive motion as given by (8.2.2).
However, in a more realistic scenario, such a smooth diffusive motion will just represent an idealized case, or one, respectively, at very short time scales only,
i.e., before some “disturbances” of the surroundings will destroy the said smooth motion. That is, to invoke a more realistic scenario, the smooth behaviour in a completely isotropic and unconstrained environment will have to be substituted by behaviour in an
anisotropic, constrained environment. Thus, if we imagine the bouncing of a walker in its “fluid” environment, the latter will become “excited” or “heated up” wherever, in the said anisotropic manner, the momentum fluctuations direct the particle to. After some time span (which can be rather short, considering the very rapid oscillations of elementary particles), a whole area of the particle’s environment will be coherently heated up in this way. (Considering the electron, for example, the fact that it “bounces” roughly
times per second, with each bounce eventually providing a slight displacement from the original path’s momentum, one can thus understand the “area filling” capacity of any quantum path whose fractal dimension was shown to be equal to
. [
47])
Now, let us assume we have a source of identical particles, which are prepared in such a way that each one ideally has an initial (classical) velocity
. Even if we let them emerge one at a time only, say, from an aperture with unsharp edges (thus avoiding diffraction effects to good approximation), the probability density
will be a Gaussian one. This comes along with a heat distribution generated by the oscillating (“bouncing”) particle(s) within the constraints of that “Gaussian slit”,
i.e., with a maximum at the center of the aperture
. So, we have, in one dimension for simplicity, the corresponding solution of the heat equation,
with the usual variance
, where we shall choose
.
Note that from Equation (8.2.1) one has for the
averages over particle positions and fluctuations (as represented via the probability density
)
with the mean values (generally defined in
dimensional configuration space)
As opposed to Equation (8.2.1), where
can take on an arbitrary value such that
is generally variable, equation (8.2.7) is a statement of total average energy conservation,
i.e., holding for all times
. This means that in Equation (8.2.7), a variation in
implies a varying “particle energy”
, and vice versa, such that each of the summands on the right hand side for itself is not conserved. In fact, as shall be detailed below, there will generally be an exchange of momentum between the two terms providing a net balance
where
describes a change in the ”convective“ velocity
paralleled by the “diffusive” momentum fluctuation
in the thermal environment.
As elaborated in references [
2,
3], once Equation (8.2.1) is assumed, considerations based on Boltzmann’s relation between action and angular frequency of an oscillator provide, without any further reference to quantum theory, that
Now we make use of one out of a whole series of practical identities, which Garbaczewski has collected in [
37]. (These identities hold true on general information theoretic grounds and are thus not bound to quantum mechanical issues.) Said identity, which can easily be checked by integration, is given by
In a further step, we now introduce a way to prepare for an Ord-type of projection as mentioned in the previous chapter,
i.e., to cut out a “slice of time” from an otherwise irreversible evolution as given by the assumed diffusion process. To do this, we shall first combine Equations (8.2.10) and (8.2.11), and shall then insert (8.2.6) for the initial time,
. As from (8.2.7) one has that
and thus also
and as only the kinetic energy varies, one obtains
. Then, with the Gaussian (8.2.6), this provides an expression for the averaged fluctuating kinetic energy, or heat, of a particle and its surroundings,
Equation (8.2.12) is an expression of the fact that at the time
the system is known to be in the prepared state whose fluctuating kinetic energy term is solely determined by the initial value
, whereas for later times
it decomposes into the term representing the particle’s changed kinetic energy and the term including
. As the kinetic energy term of the particle increases, the convective velocity becomes
, and, correspondingly,
. In other words, one can decompose said term into its initial
value and a subtracted fluctuating kinetic energy term, respectively,
i.e.,
where the last term on the right hand side is identical to
in order to fulfil Equation (8.2.12), and also in agreement with Equation (8.2.9).
From Equations (8.2.12) and (8.2.13) one derives
minimal uncertainty relations for all
,
i.e.,
Moreover, with the “diffusion constant”
Equation (8.2.12) provides an expression for the initial velocity fluctuation,
Now we take into account the small momentum fluctuations
from (8.2.13), providing an altered convective velocity
, and thus an additional displacement
i.e., as soon as
. Therefore, in Equation (8.2.2) one now must decompose
into its initial value
and a fluctuating contribution
, respectively. Unless some thermal equilibrium were reached, the latter is typically given off from the “heated” thermal bath to the particle of velocity
,
which is in accordance with (8.2.13)
As opposed to (8.2.2), Equation (8.2.17) now provides the particle’s total displacement
Squaring (8.2.18) provides
Since
one obtains in accordance with Equation (8.2.9) that the last terms on the l.h.s. and on the r.h.s. of (8.2.19), respectively, cancel each other out. Moreover, as the product terms in (8.2.19) are subject to the average orthogonality condition, one obtains through averaging over positions and fluctuations that
Inserting (8.2.16) into (8.2.20) for the particular case that
(
i.e.,
), provides for the time evolution of the wave packet’s variance
The quadratic time-dependence of the variance
is remarkable insofar as in ordinary diffusion processes the scenario is different. There, with the Gaussian distribution being a solution of the heat equation, for purely Brownian motion the variance grows only linearly with time,
i.e., as described by the familiar relation
However, as we have seen, the momentum exchange between the particle and its environment is characterized by both a changing velocity and by a changing thermal environment of the particle, i.e., also by a changing diffusivity. Therefore, Equation (8.2.22) must be modified to allow for a time-dependent diffusivity.
In other words, we shall have to deal with the field of
anomalous diffusion. This means that instead of the diffusion constant
in the usual heat equation, we now introduce a time-dependent diffusion coefficient
, where
is a constant factor and the exponent
has to be derived upon comparison with Equation (8.2.21). Thus, we write the heat equation in the more general form
and, inserting for
the Gaussian (8.2.6), one obtains after a short calculation that
Integration then provides (with integration constant
)
Upon comparison with (8.2.21) we obtain that
and
which can
only be fulfilled by
. Therefore,
, and the time-dependent diffusion coefficient becomes
Note that with the exponent of
being
, or the
-dependence of
in (8.2.21), respectively, one deals with the special case of anomalous diffusion usually named
ballistic diffusion. We shall review some general properties of ballistic diffusion in the last chapter. At this point, however, it is useful to recall that throughout the modelling of sub-quantum processes in the present chapter, we deal with various processes at different time scales. On the shortest scales, we have assumed Brownian-type motions (not detailed here), which, on the next higher level of (spatial and) temporal scales lead collectively to the emergence of a regular diffusion wave. The latter is characterized by a velocity
according to (8.2.2), and it is orthogonal on average to the particle’s velocity
, thus providing the r.m.s. displacement (8.2.5) depending on
. As a next step, we have introduced the noisy thermal bath of the particle’s environment,
i.e., essentially the effect of other diffusion wave configurations, which disturbs the relation (8.2.5) by introducing a fluctuating term
. The net effect of the latter, however, is the r.m.s. displacement (8.2.20) with a dependence solely on the initial diffusive velocity
. This manifests itself also in the expression for
of the ultimately emerging ballistic diffusion, which is also dependent only on
. However, even on the level of ballistic diffusion one can recover the signature of Brownian motion. In fact, if one considers the time-average of
for large enough times
,
i.e.,
one immediately obtains the linear-in-time Brownian relation
which is, however, also in accordance with the
dependence of Equation(8.2.21).
Note that the diffusivity’s rate of change is a constant,
such that it is determined only by the initial r.m.s. distribution
. In other words, the smaller the initial
, the faster
will change. With the square root of (8.2.21),
we note that
is a spreading ratio for the wave packet independent of
. This functional relationship is thus not only valid for the particular point
but for all
of the Gaussian. Therefore, one can generalize (8.2.31) for all
,
i.e.,
In other words, one derives also the time-invariant ratio
Now we remind ourselves that we deal with a particle of velocity
immersed in a wave-like thermal bath that permanently provides some momentum fluctuations
. The latter are reflected in Equation (8.2.31) via the r.m.s. deviation
from the usual classical path. In other words, one has to do with a wave packet with an overall uniform motion given by
, where the position
moves like a free classical particle. As the packet spreads according to Equation(8.2.31),
describes the motion of a point of this packet that was initially at
. Depending on whether initially
or
, then, respectively, said spreading happens faster or slower than that for
. In our picture, this is easy to understand. For a particle exactly at the center of the packet
, the momentum contributions from the “heated up” environment on average cancel each other for symmetry reasons. However, the further off a particle is from that center, the stronger this symmetry will be broken, i.e., leading to a position-dependent net acceleration or deceleration, respectively, or, in effect, to the “decay of the wave packet”. Moreover, also the appearance of the time-dependent diffusivity
is straightforward in our model. Essentially, the “decay of the wave packet” simply results from sub-quantum diffusion with a diffusivity varying in time due to the particle’s changing thermal environment: as the heat initially concentrated in a narrow spatial domain gets gradually dispersed, so must the diffusivity of the medium change accordingly.
In conclusion, then, one obtains with Equations (8.2.32) and (8.2.15) for the “smoothed out”
trajectories (
i.e., those averaged over a very large number of Brownian motions)
Moreover, one can now also calculate the average total velocity,
Thus, with (8.2.32), one obtains the
average total velocity field of a Gaussian wave packet as
Next to the fundamental relations (8.2.29), Equations (8.2.34) and (8.2.36) are the main results of this part of the chapter. They provide the trajectory distributions and the velocity field of a Gaussian wave packet as derived solely from classical physics. Note that the trajectories are not the “real” ones, but only represent the averaged behaviour of a statistical ensemble. The results are in full concordance with quantum theory, and in particular with Bohmian trajectories. (For a comparison with the latter, see, for example, [
14].) This is so despite the fact that no quantum mechanics has been used yet,
i.e., neither a quantum mechanical wave function, or the Schrödinger equation, respectively, nor a guiding wave equation, nor a quantum potential.
Implicitly, of course, one can easily find the connections to the rhetoric of (Bohmian or other) quantum mechanics. As for the Bohmian case, one just needs to consider the expression for the quantum potential,
Then one has, again with the help of the general relation (8.2.11),
and thus obtains from Equation (8.2.6) for
the time-independent expression for the
average quantum potential as
The expression (8.2.39) is identical to the one we obtained on the r.h.s. of (8.2.12), such that we find that the energy conservation law (8.2.7) can be rewritten as
i.e., where
Still, it is rather remarkable that the results presented above can be arrived at without even referring to (Bohmian or other) quantum mechanics. However, let us now see how a “translation” of the present formalism into that of ordinary quantum mechanics can be accomplished.
It is straightforward to simulate the diffusion process of Equation (8.2.20) in a simple computer model. Using coupled map lattices (CML), one approximates the heat equation as usual by
and for our anomalous (“ballistic”) diffusion one simply inserts (8.2.27) into (8.2.42).
The result is depicted in
Figure 1, where the (macroscopic, classical) velocity is chosen as
. (For examples with
and different
, see [
48].) Moreover, nine exemplary averaged Bohmian trajectories are shown in
Figure 1, and it must be stressed that
the Figure shows the emerging behaviour of the Gaussian packet following solely from the CML simulation of Eq. (8.2.42). In addition, the emerging trajectories from the simulation are shown together with the calculated ones from (8.2.34), providing
exactly the same trajectories (
i.e., up to resolution limits due to discretization).
Note that the trajectories are not the “real” ones, but only represent the averaged behaviour of a statistical ensemble. The results are in full concordance with quantum theory, and in particular with Bohmian trajectories. This is so despite the fact that no quantum mechanics has been used yet,
i.e., neither a quantum mechanical wave function, or the Schrödinger equation, respectively, nor a guiding wave equation, nor a quantum potential. Moreover, we want to stress that our model offers possible insights into the sub-quantum domain which must escape (Bohmian or orthodox) quantum theory because the latter simply does not employ the “language” necessary to express them. Note, for example, that the existence of the hyperbolic trajectories depicted in
Figure 1, which are given by the formula for the scale invariant wave packet spread (8.2.21), has a simple physical explanation in terms of sub-quantum processes. As the inflection points of the hyperbolas are, according to (8.2.21), characterized by the relation
,
i.e., by the length scales
, the trajectories’ evolution is easily understood: as long as the main bulk of the heat “stored” in the initial Gaussian spreads well “inside” the distribution,
, the average particle velocity
is not affected much. However, if said main bulk approximately reaches the distance
, or spreads to regions
, respectively, the particles will “feel” the full heat and get propagated into new directions. For
, then,
becomes the spreading rate of the whole Gaussian packet:
In other words, the “spreading” already begins at
, but becomes “visible” in terms of deflected trajectories only when
.
Figure 1.
Dispersion of a free Gaussian wave packet.
Figure 1.
Dispersion of a free Gaussian wave packet.
Figure 1: Considering the particles of a source as oscillating “bouncers”, they can be shown to “heat up” their environment in such a way that the particles leaving the source (and thus becoming “walkers”) are guided through the thus created thermal “landscape”. In the Figures, the classically simulated evolution of exemplary
averaged trajectories is shown (
i.e., averaged over many single trajectories of Brownian-type motions). The results are in full agreement with quantum theory, and in particular with Bohmian trajectories. This is so despite the fact that no quantum mechanics is used in the calculations (
i.e., neither a quantum mechanical wave function, nor a guiding wave equation, nor a quantum potential), but purely classical physics. The Figure displays a simulation with coupled map lattices of classical ballistic diffusion, with a time-dependent diffusivity as given by Equation (8.2.27). In the (1+1)-dimensional space-time diagram, both the emerging intensity field and nine exemplary emerging trajectories are shown (dark lines). They exactly match with the superimposed (bright) calculated trajectories from Equation (8.2.34). Note that the emerging hyperbolas’ inflection points occur at the scale
, a fact which has a direct physical meaning: It is there where the main bulk of the heat concentrated within the Gaussian reaches the latter’s average “borders”. Whereas at earlier times the heat was essentially spreading “inside” the original distribution, it now begins to affect the distribution itself by broadening it via heat dissipation.
Figure 2.
Dispersion of a Gaussian wave packet in a gravitational field.
Figure 2.
Dispersion of a Gaussian wave packet in a gravitational field.
Figure 2: Same as
Figure 1, but with the addition of a linear (e.g., gravitational) field. The results are again in full agreement with quantum theory, and in particular with Bohmian trajectories, despite the use of a classical CML simulation of ballistic diffusion, now modified according to the substitution of the classical velocity by
. Again, both the emerging intensity field and nine exemplary emerging trajectories are shown (dark lines), thereby more or less exactly superimposing the (bright) calculated trajectories due to Equation (8.3.3). Note that some trajectories of the dispersing Gaussian even overcome gravity for a well-defined period of time. In fact, our sub-quantum model provides a detailed explanation of why, and within which time limits, this “anti-gravity” effect becomes possible: Some of the upper curves’ extrema occur at the scale
, which describes the maximum of the “anti-gravity” effect, because it is there where the heat of the main bulk of the packet is consumed, which has via the kinetic energy counter-acted the effect of gravity for initial times. For larger times, then, the remaining heat gets gradually less, and therefore gravitational acceleration begins to dominate the trajectories’ curvature.