Related papers: Exploring Dimensionality Reduction of SDSS Spectra…
In preparation for future, large-scale, multi-object, high-resolution spectroscopic surveys of the Galaxy, we present a series of tests of the precision in radial velocity and chemical abundances that any such project can achieve at a 4m…
The spectral analysis and data products in Data Release 16 (DR16; December 2019) from the high-resolution near-infrared APOGEE-2/SDSS-IV survey are described. Compared to the previous APOGEE data release (DR14; July 2017), APOGEE DR16…
Single-cell RNA sequencing (scRNA-seq) has revolutionized our ability to analyze gene expression at the cellular level. By providing data on gene expression for each individual cell, scRNA-seq generates large datasets with thousands of…
We present a statistical study of the correlations and dimensionality of emission lines carried out on a sample of over 40,000 Sloan Digital Sky Survey (SDSS) galaxies. Using principal component analysis, we found that the equivalent widths…
In the past few years, several independent collaborations have presented cosmological constraints from tomographic cosmic shear analyses. These analyses differ in many aspects: the datasets, the shear and photometric redshift estimation…
This study explores the application of autoencoder-based machine learning techniques for anomaly detection to identify exoplanet atmospheres with unconventional chemical signatures using a low-dimensional data representation. We use the…
We present a simulation of the spatial, luminosity and spectral distributions of four types of stellar objects. We simulate: (1) Galactic stars, based on a Galactic structure model, a stellar population synthesis model, stellar isochrones,…
Stellar abundance measurements are subject to systematic errors that induce extra scatter and artificial correlations in elemental abundance patterns. We derive empirical calibration offsets to remove systematic trends with surface gravity…
Fitting model spectral energy distributions (SED) to galaxy photometric data is a widely used method to recover galaxy parameters from galaxy surveys. However, the parameter space used to describe galaxies is wide and interdependent, and…
We present the 4MOST-HR resolution Non-Local Thermal Equilibrium (NLTE) Payne artificial neural network (ANN), trained on $404\,793$ new FGK spectra with 16 elements computed in NLTE. This network will be part of the Stellar Abundances and…
The periodic table is a fundamental representation of chemical elements that plays essential theoretical and practical roles. The research article discusses the experiences of unsupervised training of neural networks to represent elements…
Dimensionality reduction can be applied to hyperspectral images so that the most useful data can be extracted and processed more quickly. This is critical in any situation in which data volume exceeds the capacity of the computational…
Spectra of late-type stars are usually analyzed with static model atmospheres in local thermodynamic equilibrium (LTE) and a homogeneous plane-parallel or spherically symmetric geometry. The energy balance requires particular attention, as…
Semantic segmentation takes pivotal roles in various applications such as autonomous driving and medical image analysis. When deploying segmentation models in practice, it is critical to test their behaviors in varied and complex scenes in…
Deep learning with artificial neural networks is increasingly gaining attention, because of its potential for data-driven astronomy. However, this methodology usually does not provide uncertainties and does not deal with incompleteness and…
Sequential or online dimensional reduction is of interests due to the explosion of streaming data based applications and the requirement of adaptive statistical modeling, in many emerging fields, such as the modeling of energy end-use…
Feature extraction and dimensionality reduction are important tasks in many fields of science dealing with signal processing and analysis. The relevance of these techniques is increasing as current sensory devices are developed with ever…
Dimensionality reduction is often used as an initial step in data exploration, either as preprocessing for classification or regression or for visualization. Most dimensionality reduction techniques to date are unsupervised; they do not…
Diverse classes of proteins function through large-scale conformational changes; sophisticated enhanced sampling methods have been proposed to generate these macromolecular transition paths. As such paths are curves in a high-dimensional…
While powerful for generation, Diffusion Probabilistic Models (DPMs) face slow sampling challenges, for which various distillation-based methods have been proposed. However, they typically require significant additional training costs and…