2.1. Algebraic Structures
2.1.1. Affine and Vectorial Structure
When endowed with its usual vector space structure, the set
of the real
n-tuples will be noted by
, and when endowed with its affine structure, it will be denoted simply by
. The affine structure is a 1-transitive action of the additive group
for
:
When
, we will sometimes write
.
2.1.2. Euclidean Structures
The vector space
is equipped with a
Euclidean structure once a
scalar product on
has been selected. A scalar product is a definite positive bilinear symmetric form; in other words, it is an application:
It is linear in each variable and is symmetric and satisfies
, with the cancellation happening only when
.
This means that in some base of , the matrix representing the bilinear form , i.e., the matrix , where , is , which means that , which represents the Kronecker symbol.
If
and
, we then have
Such an
base is an
orthonormal base (with respect to the particular scalar product considered).
The application
is a
norm on
; in other words, it fulfills the following conditions:
- Positivity ();
- Separation ();
- Homogeneity ();
- Subadditivity ().
For a scalar product, this is also associated in such a way to measure angles:
The linear transformations
f of
respect the scalar product
; in other words, this is such that
are called
(vectorial) isometries. The set of vectorial isometries is a subgroup of the linear group
, called the
orthogonal group and is denoted by
(it is not relevant to note the particular scalar product that one considers because the orthogonal groups of two different scalar products on
are isomorphic).
When the vectorial space
is equipped with a Euclidean structure, the affine space
is endowed with a
distance defined by
In other words, we have
- Symmetry ();
- Separation ();
- Triangle inequality ().
Furthermore, this distance will be compatible with the affine structure:
- Invariance by translation:
;
- Homogeneity:
.
The affine transformations with a linear part in are the (affine) isometries.
2.1.3. Minkowski Structure
A structure of Minkowski space on
is the choice of some symmetric bilinear form,
on
with the signature
; this means that in some base
of
, the matrix associated with
is
. In other words, if
and
, we then have
It is common to refer to the vectors of a Minkowski space as “quadrivectors” and to
as a Minkowski product.
According to the sign of , a quadrivector is
time-like when ;
light-like when , with the isotropy cone of ;
space-like when .
The linear transformations,
f, of
respect the Minkowski product,
M; in other words, this is such that
are called
Lorentz transformations. The Lorentz transformation is a subgroup of
, called the
Lorentz group, and is denoted by
; it is straightforward to see that the Lorentz transformation
f respects quadrivector types.
When the space is equipped with a Minkowski structure, the points of the affine space are usually called events.
Two events,
and
, are
time-oriented when the unique quadrivector is
, such that
is time-like (
Figure 1).
2.2. Topological Structure of
Having a topology on a set, E, is a way to give meaning to expressions such as “x and y are close” without having a way to measure the distance between x and y.
A standard way to do so is to select (for each point x of E) a set of parts of E, , called the set of the neighborhoods of x. The sets, , fulfilling the conditions expect the following:
† The set E must contain whatever is “close” to x: ;
† The point x is among what is close to x: ;
† If two sets contain whatever is close to x, then their intersection, too, must be
;
† If V contains whatever is close to x, and W contains V, W contains whatever is close to x:
if then ;
† If V contains whatever is close to x, then there exists W, which also contains whatever is close to x, such that V contains whatever is close to whatever points are in W:
.
Once a topology on E has been chosen, an open set of E is a part, O, of E, such that .
Different topologies can be defined on ; the typical one is defined using the distance, d, defined in (5). The set is the open ball, with a center at and a radius of r. A neighborhood of x is any subset of containing an open ball centered at x. We will also use this topology on when equipped with its Minkowski affine structure, even though, in that case, there is no distance directly linked to the topologic structure.
The notion of topology allows for a correct definition of some very useful “local” notions; in particular, the notion of continuity at a point for a function between two topological spaces and ; f is continuous at x when The affine orthogonal transformations (and the affine Lorentz transformations) are continuous on (and on ).
In the Minkowsi space, the set of light-like quadrivectors has two path-connected components (a part, C, of a topological space, E, is said to be path-connected when, for any points (a and b of C), there is a continuous application such that and ; in other words, a path in C with source a and goal b). A Lorentz transformation is orthochrone when the path-connected components are respected and antichrone when the components are exchanged.
2.3. Differentiable Structure of
is equipped with a scalar product and the associated norm defined in (2) and , with the distance defined in (5).
2.3.1. Differential of a Function: Tangent Vectors at a Point
An application
is differentiable at
whenever
where
is continuous and linear from
to
(It is well-known that linear applications between two finite dimensional normed vector spaces are always continuous, so the condition of continuity of
can be omitted in the definition.) and
with
such that
and
.
The linear application
is the
differential at
x of
f.
The application
is the differential of
f.
At first glance, the definition of the differentiability of f at point x suggests that the vectors are picked in the “same” space , independently of the point x we are looking at; however, this point of view would be barren if we wanted to go further.
Each point is associated with a copy of , called the tangent space to at x, and this is denoted by . Its elements are called tangent vectors to at x.
With those definitions in mind, the definition of differentiability becomes
where
is linear, and
with
satisfies
and
.
We have to clarify the status of the differential application because its arrival set has become unclear.
Set
and
The set
is the tangent bundle of
; it identifies with
; then, the projection
p becomes the first projection of
on
. The tangent bundle
also identifies with the affine space
.
Now, let
be an application differentiable at each point of
. The application
is called the
tangent application or
differential application of
f.
Furthermore, the projection
is a (trivial) “fiber bundle”; in this very simple case, this just means that
p is differentiable on
.
The bi-tangent space to
at
,
identifies with the product vector space
; then,
and
.
The process of differentiation can be repeated indefinitely, and applications admitting differentials at all orders are said to be of class . The set of functions from to of class is denoted by .
2.3.2. Tangent Vector Fields on
A vector field
on
is a section of the tangent fiber bundle, i.e., an application:
As the first factor of a tangent vector field is always known, in the sequels, we will note the tangent vector field
by its second factor
.
Let
be the canonical base of
; the tangent vector field
will be denoted by
so that any tangent vector field on
has a unique expression,
with
having some functions from
to
.
The set of tangent vector fields of class
is
We now clarify the notation :
The set is endowed with the structure of real algebra according to the following:
- Addition, defined by
;
- Multiplication by a real scalar, defined by
;
- Inner multiplication, defined by
.
The set is endowed with the structure of -modulus according to the following:
- Vector field addition, defined by
;
- Multiplication by a real scalar, defined by
;
- Multiplication by a function, defined by
.
A derivation of the real algebra
is a linear application
satisfying
For , we have a corresponding derivation on where
can be seen as the directional derivative of f in the direction of at x.
The notations introduced previously for a tangent vector field are now clear:
If the expression of a tangent vector field is and
for
, then
The Lie bracket of two tangent vector fields,
and
, is defined by
where we have
When endowed with this bracket, the vector space
is Lie algebra.
2.3.3. Covariant Derivation
A
covariant derivation on
is an application:
satisfying
A covariant derivation, ∇, on
is entirely defined by the family of tangent vector fields
If
the functions
are called the
Christoffel symbols of ∇.
For
and
, we have
2.3.4. Flat Covariant Derivation
The flat covariant derivation ∇ is the covariant derivation for which all Christoffel symbols are null; in that case, we have
can be seen as the derivative of
in the direction of
.
2.3.5. Covariant Derivation along a Curve: Parallel Transport
Let
be a class
function.
A tangent vector field of along is an application:
The set of tangent vector fields of class along is denoted by ; it is a -modulus.
The
velocity is the tangent vector field along
, defined by
where
. Its value depends only on the differential structure of
and
.
Let ∇ be a covariant derivation on
. There is exactly one operator
(The usual notation can sometimes be tricky because the dependence on the curve
is not noted.) on the
-modulus of the tangent vector fields along
, such that
and if
is the restriction to
of a tangent vector field
on
, then
For
, we have
When
is equipped with the flat covariant derivative (The application
can be seen as a section of the trivial vector bundle over
with fibers
; the covariant derivation along
is then the flat covariant derivative of
relative to the tangent field
over
.), the covariant derivative of
with
along
is the tangent vector field along
, defined by
A tangent vector field,
, along
is said to be
parallel with respect to the covariant derivation ∇ whenever
. The general results for differential equations ensure that a parallel tangent vector field along
is determined by its value at one point of the trajectory of
. For
, the application is
where
is parallel along
, which is called the parallel transport along
between time
and
.
When ∇ is the flat covariant derivation for any class curve , the parallel vector fields along are simply the constant vector fields.
2.3.6. Acceleration
Let be a class function.
The covariant derivative of the vector field along is the covariant acceleration of denoted by ; note that, unlike the velocity, depends on the choice of a covariant derivation on .
When a curve, , satisfies , it is called the geodesic curve of the covariant derivation ∇. For the flat covariant derivation, the geodesics are the parametrizations with the constant velocity of straight lines.
2.4. Riemannian Structures on
A
Riemannian metric on
is an application,
G, that associates with each point
of a scalar product
on
, with the condition that
The general expression of a Riemannian metric on
is
where the coefficients
are such that
the matrix
is symmetric definite positive and
is defined on
by
Let
G be a Riemannian metric
G and
be a class
application defined on some interval of
, where the real
is the
speed of
at instant
t with respect to the Riemannian metric
G, also known as
G-speed; the application
is a scalar field along
.
The
G-length of
is
The
G-kinetic energy of
is
The
G-length does not depend on the parametrization
, but the
G-kinetic energy does.
We can obtain a distance on
by setting it for
Although
is, in general, not associated with any norm on
, the topology of
induced by
is always the usual topology of
.
For
, the
G-angle,
, of
and
is defined by
2.4.1. Levi-Civita Connection
Let
G be a Riemannian metric on
; a covariant derivation, ∇, on
is compatible with
G when
For any Riemannian metric, G, on , there is exactly one torsion-free () covariant derivation compatible with G, which is called the Levi-Civita connection of G.
The Christoffel symbols of the Levi-Civita connection have the following expressions:
where
is the inverse matrix of
.
2.4.2. Some Riemannian Metrics
(1) The usual affine Euclidean structure of
can be seen as a Riemannian structure on
:
When
is equipped with the metric
, the Levi-Civita connection is the flat covariant derivation ∇ defined previously. The distance
is the usual Euclidean distance, and the angle measurement is the usual angle measurement. The geodesics are the parametrizations of straight lines with constant velocities.
(2) For
, we use
The Levi-Civita connection of
is also the flat covariant derivation.
The associated distance is
The associated angles measurement satisfies
The application
from
to
is for
scaling.
The geodesics are straight lines parametrized with constant velocities.
For and , the parallel transport is trivial, and the parallel tangent vector fields are of the form , where are constants.
(3) For
such that
, we use
The Levi-Civita connection is not the flat covariant derivation anymore; a straightforward computation gives the expressions of the Christoffel symbols:
The geodesics are not straight lines anymore. The application
from
to
is conformal.
2.5. Pseudo-Riemannian Structure on
A
pseudo-Riemannian metric on
is an application,
G, where each point
is associated with a Minkowski product
on
, with the condition that
The general expression of a pseudo-Riemannian metric on
is
where the coefficients
are such that
the matrix
is symmetric with signature
.
Let
be the coefficient of the matrix
. The usual affine Minkowski structure of
is a pseudo-Riemannian structure:
It will be practical to limit the summation to
. With this convention, we have
As in the Riemannian case, for any pseudo-Riemannian structure on
, the Levi-Civita connection is the unique, torsion-free covariant derivation satisfying
The Levi-Civita connection of
is, once again, the flat covariant derivation.
For
, later, we will consider the pseudo-Riemannian metric defined by
For this pseudo-Riemannian metric, the Levi-Civita connection is still the flat covariant derivation.