Reconstructing hadron spectral functions through Euclidean correlation
functions are of the important missions in lattice QCD calculations. However,
in a K\”allen–Lehmann(KL) spectral representation, the reconstruction is
observed to be ill-posed in practice. It is usually ascribed to the fewer
observation points compared to the number of points in the spectral function.
In this paper, by solving the eigenvalue problem of continuous KL convolution,
we show analytically that the ill-posedness of the inversion is fundamental and
it exists even for continuous correlation functions. We discussed how to
introduce regulators to alleviate the predicament, in which include the
Artificial Neural Networks(ANNs) representations recently proposed by the
Authors in~[Phys. Rev. D 106 (2022) L051502]. The uniqueness of solutions using
ANNs representations is manifested analytically and validated numerically.
Reconstructed spectral functions using different regularization schemes are
also demonstrated, together with their eigen-mode decomposition. We observe
that components with large eigenvalues can be reliably reconstructed by all
methods, whereas those with low eigenvalues need to be constrained by

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