PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
August 8, 2026Integral Transforms and Special Functions0 citations

Two sharp uniqueness principles arising from refined Hartley transform

View Full Paper
TTTrinh Tung

Key Points

  • This research aims to establish and elucidate two sharp uniqueness principles related to the refined Hartley transform.
  • Analyzed the conditions under which a function f and its Hartley transform Hf vanish identically.
  • Used decay conditions of functions to demonstrate relationships between Gaussian forms and uniqueness.
  • Employed a reduction to classical Fourier transform to leverage existing mathematical results.
  • If |f(x)|≤C e−αx2 and |Hf(y)|≤C e−βy2 with αβ>14, then f must be identically zero.
  • In the critical case where αβ=14, f is strictly a Gaussian function.
  • In a subcritical regime where αβ<14, nontrivial examples are constructed showing sharpness of the results.

Abstract

We establish two sharp uniqueness principles for the refined Hartley transform on the real line. The first result is a Hardy-type theorem. We show that if pair (f,Hf) satisfy |f(x)|≤C e−αx2 and |Hf(y)|≤C e−βy2 for some α,β>0, then f≡0 whenever αβ>14. In critical case αβ=14, then f is necessarily a Gaussian of the form f(x)=A e−αx2 (with β=14α), while in subcritical regime αβ<14 we construct nontrivial examples, thereby establishing sharpness. We further show that any decay strictly faster than Gaussian forces f≡0, independently of the product αβ. The second result is a Beurling-type theorem, if ∬R2|f(x)| |Hf(y)| e|xy| dx dy<∞, then f vanishes identically. The proofs rely on a reduction to the case of classical Fourier transform through an explicit invertible algebraic transfer. This reduction, which may be of independent interest, enables us to invoke the classical results of Hardy and Hörmander.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Trinh Tung (2026) studied this question.

synapsesocial.com/papers/6a76da64f12abadc79814b2chttps://doi.org/10.1080/10652469.2026.2713575
Ask AI
Helpful
Bookmark
Share
View Full Paper