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Zichi Zhang

Publications and source records attributed to Zichi Zhang.

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What do position and time mean in the quantum wavefunction?

The notation $\psi(x,t)$ is among the first pieces of quantum mechanics that students learn. It is also among the easiest to over-interpret. Because $x$ and $t$ occur as arguments of the same function, students may ask whether they have the same mathematical status. They may also ask whether $\psi(t)$ should require a generalized bra $\bra{t}$ in the same way that $\psi(x)=\braket{x}{\psi}$ is often written. A related question is whether the absence of a universal time operator follows simply from Pauli's argument. These questions mix several structures that are usually introduced in different parts of the curriculum. We present a unified pedagogical treatment built around two maps hidden in $\psi(x,t)$. Time evolution selects a state along a trajectory in Hilbert space. A spectral representation then maps that state to amplitudes labelled by outcomes of a chosen observable. We formulate the position representation without generalized eigenkets. We recover Dirac's $\ket{x}$ notation as a controlled continuum shorthand and use a finite-grid limit to show where delta normalization enters. We distinguish background coordinates, translation parameters, observables, spectral labels, and physical records. We also clarify the Stone-theorem analogy, compare prescribed-time position measurements with arrival-time measurements, state what the strong form of Pauli's argument excludes, and exhibit an exactly solvable boundary case in which a canonical self-adjoint time observable exists. Spin, circuit-QED, and optical-clock examples provide experimentally grounded checks. The aim is not a new interpretation of time. It is a reusable teaching framework for separating mathematical role from notation.

quant-ph

Attention-Based Preprocessing Framework for Improving Rare Transient Classification

With large numbers of transients discovered by current and future imaging surveys, machine learning is increasingly applied to light curve and host galaxy properties to select events for follow-up. However, finding rare types of transients remains difficult due to extreme class imbalances in training sets, and extracting features from host images is complicated by the presence of bright foreground sources, particularly if the true host is faint or distant. Here we present a data augmentation pipeline for images and light curves that mitigates these issues, and apply this to improve classification of Superluminous Supernovae Type I (SLSNe-I) and Tidal Disruption Events (TDEs) with our existing NEEDLE code. The method uses a Similarity Index to remove image artefacts, and a masking procedure that removes unrelated sources while preserving the transient and its host. This focuses classifier attention on the relevant pixels, and enables arbitrary rotations for class upsampling. We also fit observed multi-band light curves with a two-dimensional Gaussian Process and generate data-driven synthetic samples by resampling and redshifting these models, cross-matching with galaxy images in the same class to produce unique but realistic new examples for training. Models trained with the augmented dataset achieve substantially higher purity: for classifications with a confidence of 0.8 or higher, we achieve 75% (43%) purity and 75% (66%) completeness for SLSNe-I (TDEs).

astro-ph.IM