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Fourier inversion theorems for integral transforms involving Bessel functions

This paper utilizes partial differential equations to derive and analyze the invertibility, spectral decomposition, and Plancherel-Parseval identities for generalized Weber-Orr transforms involving Bessel functions, specifically addressing their nontrivial kernels through a novel approach that includes both continuous and discrete spectra.

Original authors: Alexey Gorshkov

Published 2026-05-14
📖 5 min read🧠 Deep dive

Original authors: Alexey Gorshkov

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine you have a complex sound recording (a mathematical function) and you want to break it down into its individual notes to understand it better. In mathematics, this process is often done using something called a Fourier Transform. Usually, this works like a perfect puzzle: you take the picture apart into pieces, and you can always put it back together perfectly to get the original image back.

This paper is about a specific, slightly trickier version of that puzzle called the Weber-Orr Transform. The author, Alexey Gorshkov, uses the tools of heat equations (how heat spreads out over time) to prove how to take these specific mathematical puzzles apart and, crucially, how to put them back together correctly.

Here is the breakdown of what the paper does, using simple analogies:

1. The "Perfect" Puzzle vs. The "Broken" Puzzle

Most of the time, when you use a Fourier-style transform, the "machine" that breaks the signal down has no missing pieces. If you put a signal in, you get a unique set of notes out, and you can reconstruct the signal perfectly.

However, the Weber-Orr transforms studied in this paper sometimes have a "hidden piece" or a non-trivial kernel.

  • The Analogy: Imagine you are trying to sort a deck of cards. Usually, every card goes into a specific pile. But in this specific case, there is a "ghost card" (a specific mathematical shape, like 1/rk1/r^k) that the machine ignores completely. It doesn't show up in the output.
  • The Problem: If you just look at the output piles, you think you have all the cards, but you are missing that ghost card. If you try to rebuild the original image using only the piles, you get a blurry, incomplete picture.

2. The Heat Equation as a Time Machine

The author's clever trick is to treat the mathematical function not as a static picture, but as heat spreading out on a metal ring.

  • He imagines the function f(r)f(r) as the initial temperature of a ring.
  • He then asks: "How does this heat spread out over time?"
  • By solving the heat equation (a partial differential equation), he can watch the heat evolve.
  • The Magic: As time passes, the heat settles into a pattern. By analyzing how the heat behaves at the very beginning (time = 0) versus how it behaves later, he can mathematically prove exactly how to reverse the process.

3. Two Different Scenarios

The paper splits the problem into two main cases based on the "shape" of the ring (defined by a number kk):

Case A: The Standard Case (k1k \le 1)

  • What happens: The "ghost card" doesn't exist here. The machine works perfectly.
  • The Result: You can break the signal down and put it back together exactly as it was. The paper proves that the formula to rebuild the signal works almost everywhere (meaning it works for every point except maybe a few tiny, insignificant spots).
  • The "Energy" Rule: The paper also proves a "Plancherel-Parseval identity." Think of this as a conservation of energy law: The total "loudness" (energy) of the original signal is exactly equal to the total "loudness" of the broken-down notes. Nothing is lost.

Case B: The Tricky Case (k>1k > 1 or k<1k < -1)

  • What happens: Here, the "ghost card" (the kernel) is real. The machine does ignore a specific part of the signal.
  • The Result: If you try to rebuild the signal using the standard formula, you will fail. You will be missing that specific "ghost" part.
  • The Fix: The author derives a new formula. To rebuild the signal perfectly, you must:
    1. Use the standard "rebuild" formula.
    2. PLUS add a specific correction term (the "ghost card" back in).
  • The "Energy" Rule: In this case, the conservation of energy changes. The total energy of the original signal is equal to the energy of the notes PLUS the energy of that missing ghost card. You have to account for the missing piece to get the total right.

4. Why This Matters (According to the Paper)

The paper claims to provide a new approach to these problems.

  • Historically, these formulas were proven using strict assumptions about how "wiggly" the function was (bounded variation).
  • This paper uses partial differential equations (the heat equation) to prove the same things but for a broader, more standard class of functions (integrable functions, L1L2L^1 \cap L^2).
  • It provides a complete, step-by-step derivation of how to invert these transforms, ensuring that even when the "machine" has a blind spot (a non-trivial kernel), we know exactly how to fix the reconstruction.

Summary

Think of this paper as a manual for a specific type of signal decoder.

  • Old manuals said: "If the signal is smooth, you can decode it."
  • This new manual says: "We used the physics of heat flow to prove that you can decode these signals even if they are rough. Furthermore, if the decoder has a blind spot (a kernel), we give you the exact extra ingredient you need to add back in to get the perfect original signal back."

The paper does not discuss medical uses, engineering applications, or future technologies. It stays strictly within the realm of pure mathematics, proving that these specific formulas work and showing exactly how to fix them when they seem to fail.

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