Related papers: Exploring Dimensionality Reduction of SDSS Spectra…

Principal Component Analysis (PCA) minimizes the reconstruction error given a class of linear models of fixed component dimensionality. Probabilistic PCA adds a probabilistic structure by learning the probability distribution of the PCA…

In this paper, we study the diffusability (learnability) of variational autoencoders (VAE) in latent diffusion. First, we show that pixel-space diffusion trained with an MSE objective is inherently biased toward learning low and mid spatial…

We present the results of a search for extremely metal-poor (EMP), carbon-enhanced metal-poor (CEMP), and cataclysmic variable (CV) stars using a new exploration tool based on linked scatter plots (LSPs). Our approach is especially designed…

Aerosol-cloud--radiation interactions remain among the most uncertain components of the Earth's climate system, in partdue to the high dimensionality of aerosol state representations and the difficulty of obtaining complete \textit{in situ}…

Real-time data collection and analysis in large experimental facilities present a great challenge across multiple domains, including high energy physics, nuclear physics, and cosmology. To address this, machine learning (ML)-based methods…

Broad Absorption Line Quasars (BALQSOs) displaying distinct blue-shifted broad absorption lines. These serve as invaluable probes for unraveling the intricate structure and evolution of quasars, shedding light on the profound influence…

For turbulent reacting flows, identification of low-dimensional representations of the thermo-chemical state space is vitally important, primarily to significantly reduce the computational cost of device-scale simulations. Principal…

We present observations and initial analysis from an HST/STIS program to obtain the first co-spatial, UV-optical spectra of ten Galactic planetary nebulae (PNe). Our primary objective was to measure the critical emission lines of carbon and…

Dimension reduction is an important tool for analyzing high-dimensional data. The predictor envelope is a method of dimension reduction for regression that assumes certain linear combinations of the predictors are immaterial to the…

Planetary Nebulae (PNe) abundance patterns have long been used to note signatures of nuclear processing and to trace the distribution of metals throughout galaxies. We present abundance gradients and heavy element ratios based upon newly…

A new research project on spectral analysis that aims to characterize the vertical stratification of element abundances in stellar atmospheres of chemically peculiar (CP) stars is discussed in detail. Some results on detection of vertical…

We have used photometry from the Kepler satellite to characterize the variability of four radio-loud active galactic nuclei (AGN) on timescales from years to minutes. The Kepler satellite produced nearly continuous high precision data sets…

Sparse autoencoders (SAEs) have been applied to large language models and protein language models, but not systematically to electronic health record (EHR) foundation models. We train TopK SAEs on FlatASCEND, a 14.5-million-parameter…

Shear peak statistics has gained a lot of attention recently as a practical alternative to the two point statistics for constraining cosmological parameters. We perform a shear peak statistics analysis of the Dark Energy Survey (DES)…

Traditional spectral energy distribution (SED)-fitting methods for stellar mass estimation face persistent challenges including systematic biases and computational constraints. We present a controlled comparison of machine learning (ML) and…

Sparse Principal Component Analysis (sPCA) is a cardinal technique for obtaining combinations of features, or principal components (PCs), that explain the variance of high-dimensional datasets in an interpretable manner. This involves…

The Chinese Space Station Survey Telescope (CSST) aims to map the universe across an unprecedented dynamic range of stellar densities, spanning from extragalactic voids to the crowded Galactic center (e.g. a few stars and galaxies in the…

We present a new analysis of the LAMOST DR1 survey spectral database performed with the code SP_Ace, which provides the derived stellar parameters T$_{\rm eff}$, log (g), [Fe/H], and [$\alpha$/Fe] for 1,097,231 stellar objects. We tested…

We have compared the oxygen and nitrogen abundances derived from global emission-line SDSS spectra of galaxies using (1) the Te method and (2) two recent strong line calibrations: the ON and NS calibrations. Using the Te method, anomously…

The adaptation of foundation models has significantly advanced environmental audio deepfake detection (EADD), a rapidly growing area of research. These models are typically fine-tuned or utilized in their frozen states for downstream tasks.…