arXiv subjects

Sicong Lu

Publications and source records attributed to Sicong Lu.

3 recordsLinked to original sources

arXiv2026

AESTRA II: Generative Spectral Modeling of the Sun as a Star for Precise Radial Velocities

The detection of Earth analogs with extreme-precision radial velocities (EPRVs) is limited by spectral variability from stellar activity, telluric absorption, and instrumental systematics. We apply AESTRA, a generative spectrum modeling framework, to NEID Sun-as-a-star observations. AESTRA empirically decomposes the spectra into stellar line-shape variability, micro-telluric absorption, and continuum variability without external atmospheric or stellar templates. After removing the learned telluric and continuum components, we train a low-dimensional representation of the spectrum to infer activity-driven apparent RVs jointly with candidate Doppler signals. We evaluate the method with 500 single-planet injection-recovery tests spanning periods of 2.5 to 400 days and semi-amplitudes of K = 0.1 to 0.7 m s^-1, calibrating the detection criterion to yield zero spurious detections. At this matched confidence level, AESTRA recovers 238 injected planets, including 13 with K < 0.3 m s^-1, whereas traditional CCF-based activity-indicator detrending recovers 9 planets and none below K = 0.5 m s^-1.

astro-ph.EP↗

arXiv2023

Autoencoding Galaxy Spectra II: Redshift Invariance and Outlier Detection

We present an unsupervised outlier detection method for galaxy spectra based on the spectrum autoencoder architecture spender, which reliably captures spectral features and provides highly realistic reconstructions for SDSS galaxy spectra. We interpret the sample density in the autoencoder latent space as a probability distribution, and identify outliers as low-probability objects with a normalizing flow. However, we found that the latent-space position is not, as expected from the architecture, redshift invariant, which introduces stochasticity into the latent space and the outlier detection method. We solve this problem by adding two novel loss terms during training, which explicitly link latent-space distances to data-space distances, preserving locality in the autoencoding process. Minimizing the additional losses leads to a redshift-invariant, non-degenerate latent space distribution with clear separations between common and anomalous data. We inspect the spectra with the lowest probability and find them to include blends with foreground stars, extremely reddened galaxies, galaxy pairs and triples, and stars that are misclassified as galaxies. We release the newly trained spender model and the latent-space probability for the entire SDSS-I galaxy sample to aid further investigations.

astro-ph.IM↗

arXiv2018

Quantum Circuit Design for Training Perceptron Models

Perceptron model is a fundamental linear classifier in machine learning and also the building block of artificial neural networks. Recently, Wiebe et al. (arXiv:1602.04799) proposed that the training of a perceptron can be quadratically speeded using Grover search with a quantum computer, which has potentially important big-data applications. In this paper, we design a quantum circuit for implementing this algorithm. The Grover oracle, the central part of the circuit, is realized by Quantum-Fourier-Transform based arithmetics that specifies whether an input weight vector can correctly classify all training data samples. We also analyze the required number of qubits and universal gates for the algorithm, as well as the success probability using uniform sampling, showing that it has higher possibility than spherical Gaussian distribution $N(0,1)$. The feasibility of the circuit is demonstrated by a testing example using the IBM-Q cloud quantum computer, where 16 qubits are used to classify four data samples.

quant-ph↗