Proving Parallelism of Orthogonal Vectors to Null Vectors

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In summary, any vector that is orthogonal to a null vector must be either parallel to a null vector or space-like.
  • #1
quantum123
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I read this in the notes:

Show that any vector that is orthogonal to a null vector must be either be:-
i) parallel to a null vector
ii) space-like

How??
 
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  • #2
Should this be in the howework section?
What's your starting point? What does it mean for two vectors to be orthogonal?
 
  • #3
Orthogonal means normal, I guess.
This is no homework. I am doing it as a hobby.
This comes out from GR notes describing the light cone, and a general description of the four linearly independent vectors that may or may not lie parallel to the light cone, which is a null surface, ie may or may not be null vectors, and the properties of the vectors that are not null vectors.
 
  • #4
quantum123 said:
I read this in the notes:

Show that any vector that is orthogonal to a null vector must be either be:-
i) parallel to a null vector
ii) space-like

How??

Let {e_0, e_1, e_2, e_3} be an arbitary orthonormal basis. Then, up to a constant multiple,

n = e_0 + e_1

is an arbitrary null vector. Let

v = v^0 e_0 + v^1 e_1 + v^2 e_2 + v^3 e_3

be an arbitrary 4-vector. If n and v are orthogonal, what does this give you?
 
  • #5
Thanks. I see.
<n,n>^2=-1^2+1^2=0
<n,v>=0 => =-v^0+v^1=0 => V^1=v^0
Therefore, <v,v>^2 = -v^0^2 + v^1^2 + V ^2^2 + v ^3^2 = -v^0^2 + v^0^2 + V ^2^2 + v ^3^2 = V ^2^2 + v ^3^2 >=0
If =0 , then null-like.
If >0 then space-like.
Correct?
But why can n = e_0 + e_1 for any arbitrary n such that <n,n>=0 where {e^v} is orthonormal basis?
 
  • #6
quantum123 said:
Thanks. I see.
<n,n>^2=-1^2+1^2=0
<n,v>=0 => =-v^0+v^1=0 => V^1=v^0
Therefore, <v,v>^2 = -v^0^2 + v^1^2 + V ^2^2 + v ^3^2 = -v^0^2 + v^0^2 + V ^2^2 + v ^3^2 = V ^2^2 + v ^3^2 >=0

Yes.

If =0 , then null-like.

For this case, there is one other, trivial, possibility. :smile:

But why can n = e_0 + e_1 for any arbitrary n such that <n,n>=0 where {e^v} is orthonormal basis?

Consider an arbitrary vector in a 2-dimensional spatial plane.

We are free to choose a basis that helps us. For example, we might choose: a basis such that the vector is in the direction of e_1; a basis such that the vector is in the direction of e_2; a basis such that the vector is halfway between e_1 and e_2, i.e., in the direction e_1 + e_2.

None of these choices restricts us.
 
  • #7
Thank you so much.
I guess you mean a vector subspace.
If we have 4 orthonormal vectors {e} that span a vector space, we can always find a 2 -D subspace which contains n, spanned by orthonormal basis f1, f2.
Hence if n is null, then <n,n>=0 => -n0^2 + n1^2 =0 => n1=n2.
And should be able to find f3 and f4 to complete the {f} for the total vector space T(M).
 

1. How do you define parallelism of orthogonal vectors?

Parallelism of orthogonal vectors refers to the property of two vectors being parallel to each other and also being perpendicular to the same vector, known as the normal vector. This means that the two vectors lie on the same plane and form a right angle with the normal vector.

2. What are null vectors and how are they related to parallelism of orthogonal vectors?

Null vectors, also known as zero vectors, have a magnitude of 0 and no direction. They are essentially the absence of a vector. In the context of parallelism of orthogonal vectors, null vectors are used to show that two vectors are parallel to each other and perpendicular to the same vector.

3. What is the mathematical way to prove parallelism of orthogonal vectors to null vectors?

The mathematical way to prove parallelism of orthogonal vectors to null vectors is by using the dot product. If the dot product of two vectors is equal to 0, then they are perpendicular to each other. Additionally, if the dot product of two vectors is equal to the dot product of one of those vectors and a null vector, then they are parallel to each other.

4. Can parallelism of orthogonal vectors to null vectors be proven using other methods?

Yes, there are other methods to prove parallelism of orthogonal vectors to null vectors, such as using the cross product or using the properties of vector addition and subtraction. However, the dot product method is the most commonly used and efficient method.

5. What are the real-world applications of proving parallelism of orthogonal vectors to null vectors?

Proving parallelism of orthogonal vectors to null vectors is commonly used in various fields of science and engineering, especially in geometry and physics. It is used to determine the orientation and direction of objects, such as in computer graphics and robotics. It is also used in calculating forces and moments in mechanical systems and analyzing electric and magnetic fields.

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