Hidden Variable - How to calculate the straight line

In summary: This was explained in Alain Aspect's talk at 2m28s. In summary, the hidden variable theory and quantum mechanics can be compared through the expectation value E(a,b) or E(theta). The hidden variable expectation value takes on the form of a line, as illustrated by the explanation given by Asher Peres in his book "Quantum theory: concepts and methods". This can also be seen in Alain Aspect's talk at 2m28s.
  • #1
Edgardo
706
17
There are plots that compare the expectation value E(a,b) of
(i) a hidden variable theory
(ii) quantum mechanics

For example here:
1. Talk given by Alain Aspect, video at 2m28s
2. PF Thread: The Unfair Sampling Assumption & Bell Tests

For the hidden variable theory the expectation value E(a,b) or E(theta) looks like a line. My question: How do you get this line?

--------

Let me further explain:

From what I understand the hidden variable is constructed as follows:
E(a,b) is given by:

[itex]E(\textbf{a},\textbf{b}) = \int \rho(\lambda) A(\lambda, \textbf{a}) B(\lambda,\textbf{b}) d\lambda[/itex]

(Let's call this expression HV-Expectation)

where the vectors a and b are the directions of the polarizers.
(See Aspect talk at 0m54s)


Aspect asks us at 2m28s to construct a hidden variable and comes up with:

[itex]A(\lambda, \textbf{a}) = sign( \{cos(2(\theta_a-\lambda )\}[/itex]
[itex]B(\lambda, \textbf{b}) = sign( \{cos(2(\theta_b-\lambda )\}[/itex]
(Let's call them HV-equations)

where [itex]\theta_a[/itex] and [itex]\theta_b[/itex] are the angles of the vectors a and b with respect to some axis.

Now, I suppose that [itex]\lambda[/itex] is the angle of a vector [itex]\vec{\lambda}[/itex]. This vector [itex]\vec{\lambda}[/itex] represents the hidden variable (sort of like a classical polarization). At least that's what I understood after reading Bell's original paper, see Eq (4).
(I also don't understand why Bell introduces the vector a').

Anyways, Aspect then explains that we get the line after plugging the HV-equations into HV-Expectation.

How is the calculation done?
 
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  • #2
I have meanwhile found a nice explanation in the book Quantum theory: concepts and methods by Asher Peres, see page 161-162.

The hidden variable expectation value is:

[itex]E(\theta) = -1 + 2\theta/\pi[/itex]

where [itex]\theta[/itex] is the angle between the vectors [itex]a[/itex] and [itex]b[/itex]. This is indeed a line.
 

Related to Hidden Variable - How to calculate the straight line

1. What is a hidden variable in scientific research?

A hidden variable is a theoretical concept used in scientific research to explain the relationship between two or more observed variables. It is a characteristic or factor that is not directly measured or observed, but is believed to have an impact on the relationship between the observed variables.

2. How do scientists determine the hidden variables in a study?

Determining hidden variables in a study often involves a combination of data analysis, statistical methods, and theoretical models. Scientists may also use prior knowledge and hypotheses to identify potential hidden variables and then test their impact on the observed variables.

3. Why are hidden variables important in scientific research?

Hidden variables are important because they allow scientists to better understand the underlying mechanisms and relationships between observed variables. They can also help explain any discrepancies or unexpected results in a study.

4. Can hidden variables be measured directly?

No, hidden variables cannot be measured directly as they are not observable. However, their effects can be indirectly measured through the impact they have on the observed variables.

5. How do scientists calculate the straight line using hidden variables?

The calculation of a straight line using hidden variables involves using mathematical equations and statistical methods to determine the relationship between the observed variables. This can help identify the hidden variables that have an impact on the straight line and allow for more accurate predictions and explanations.

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