Derivation of Gaussian formula forom particular situation

In summary, we discussed the scenario of a virtual object, virtual image, and concave surface in relation to deriving the Gaussian formula. A virtual object is one that does not physically exist and is located on the same side as the observer, while a virtual image is formed on the opposite side of the surface. For a concave surface, the center of curvature is located on the same side as the observer, resulting in diverging light rays and a negative sign in the Gaussian formula.
  • #1
Takuza
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Homework Statement



We are to derive the Gaussian formula for a case with a virtual object, a virtual image, and a concave surface.


Homework Equations



Snell's law, n(sin(phi)) = n'(sin(phi'))
geometry


The Attempt at a Solution



I can do a derivation for a situation with a point object placed to the left of a convex surface with a real image being formed, but I don't understand what it means to have a virtual object together with a virtual image, much less for a concave surface.
 
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  • #2
Can someone please explain this scenario in more detail?

Sure, I would be happy to explain this scenario in more detail. First of all, a virtual object refers to an object that does not physically exist but is created by the refraction of light rays. In this case, the virtual object would be located on the same side of the surface as the observer. Similarly, a virtual image is an image that is formed by the refraction of light rays but does not physically exist. The virtual image would be located on the opposite side of the surface from the observer.

Now, for the case of a concave surface, the surface curves inward, towards the observer. This means that the center of curvature, which is the center of the circle from which the surface is a part of, is located on the same side as the observer. Therefore, when light rays from the virtual object pass through the concave surface, they will diverge away from each other. This results in a virtual image being formed on the opposite side of the surface from the observer.

To derive the Gaussian formula for this scenario, you would use the same principles as you would for a convex surface with a real image. You would still use Snell's law to determine the relationship between the angles of incidence and refraction, but you would have to take into account the fact that the light rays are diverging rather than converging. This would result in a negative sign in the equation, indicating that the image formed is virtual.

I hope this helps clarify the scenario for you. Let me know if you have any further questions.
 

1. What is the Gaussian formula used for?

The Gaussian formula, also known as the normal distribution formula, is used to describe the probability distribution of a continuous random variable. It is commonly used in statistics and data analysis to model various natural phenomena.

2. How is the Gaussian formula derived?

The Gaussian formula is derived from the central limit theorem, which states that the sum of a large number of independent and identically distributed random variables will tend towards a normal distribution. By applying this theorem to a particular situation, we can derive the Gaussian formula.

3. What is the significance of the parameters in the Gaussian formula?

The Gaussian formula has two parameters: the mean (μ) and the standard deviation (σ). The mean represents the center of the distribution, while the standard deviation controls the spread or width of the distribution. These parameters play a crucial role in determining the shape of the curve and the probabilities associated with different values.

4. Can the Gaussian formula be used for any situation?

The Gaussian formula can be used for any situation where the central limit theorem applies. This includes many real-world scenarios, such as the heights of people in a population, the weights of objects, or the scores on a test. However, it may not be suitable for situations with extreme outliers or non-normal distributions.

5. How is the Gaussian formula related to the bell curve?

The Gaussian formula is closely related to the bell curve, as the shape of the normal distribution resembles a symmetrical bell-shaped curve. The curve is highest at the mean and gradually decreases towards the tails, with 68% of the data falling within one standard deviation of the mean, and 95% falling within two standard deviations. This makes it a useful tool for understanding and analyzing data.

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