Related papers: Stellar Spectral Interpolation using Machine Learn…
A fundamental building block for supporting better utilization of radio spectrum involves predicting the impact that an emitter will have at different geographic locations. To this end, fixed sensors can be deployed to spatially sample the…
Stars play a decisive role in our Universe, from its beginning throughout its complete evolution. For a thorough understanding of their properties, evolution, and physics of their outer envelopes, stellar spectra need to be analyzed by…
Machine Learning algorithms are good tools for both classification and prediction purposes. These algorithms can further be used for scientific discoveries from the enormous data being collected in our era. We present ways of discovering…
We propose a self-supervised machine learning (SSML) framework for multi-fidelity learning and extended predictive soil spectroscopy based on latent space embeddings. A self-supervised representation was pretrained with the large MIR…
Context. The Spectroscopy Made Easy (SME) package has become a popular tool for analyzing stellar spectra, often in connection with large surveys or exoplanet research. SME has evolved significantly since it was first described in 1996, but…
We use the first release of the SDSS/MaStar stellar library comprising ~9000, high S/N spectra, to calculate integrated spectra of stellar population models. The models extend over the wavelength range 0.36-1.03 micron and share the same…
Airborne hyperspectral imaging is a promising method for identifying tropical species, but spectral variability between acquisitions hinders consistent results. This paper proposes using Self-Supervised Learning (SSL) to encode spectral…
A new stellar library developed for stellar population synthesis modeling is presented. The library consist of 985 stars spanning a large range in atmospheric parameters. The spectra were obtained at the 2.5m INT telescope and cover the…
The spectral predictions of stellar population models are not as accurate in the ultra-violet (UV) as in the optical wavelength domain. One of the reasons is the lack of high-quality stellar libraries. The New Generation Stellar Library…
We have obtained [Mg/Fe] measurements for 76.3% of the stars in the MILES spectral library used for understanding stellar atmospheres and stellar populations in galaxies and star clusters. These abundance ratios were obtained through (1) a…
Solar irradiance is fundamental data crucial for analyses related to weather and climate. High-precision estimation models are necessary to create areal data for solar irradiance. In this study, we developed a novel estimation model by…
Semi-Supervised Learning (SSL) is a framework that utilizes both labeled and unlabeled data to enhance model performance. Conventional SSL methods operate under the assumption that labeled and unlabeled data share the same label space.…
Spectral learning recently generated lots of excitement in machine learning, largely because it is the first known method to produce consistent estimates (under suitable conditions) for several latent variable models. In contrast, maximum…
Context: The full spectrum fitting of stellar spectra against a library of empirical spectra is a well-established approach to measure the atmospheric parameters of FGK stars with a high internal consistency. Extending it towards cooler…
Traditional spectral analysis methods are increasingly challenged by the exploding volumes of data produced by contemporary astronomical surveys. In response, we develop deep-Regularized Ensemble-based Multi-task Learning with Asymmetric…
Long-baseline interferometry at optical and near-infrared wavelengths is an emerging technology which is quickly becoming a useful tool to investigate stellar atmospheres and to compare observations with models. Stellar atmosphere models…
Machine learning models in astrophysics are often limited in scope and cannot adapt to data from new instruments or tasks. We introduce SpectraFM, a Transformer-based foundation model architecture that can be pre-trained on stellar spectra…
Empirical stellar libraries are extensively used to extract stellar kinematics in galaxies and to build stellar population models. An accurate knowledge of the spectral resolution of these libraries is critical to avoid propagation errors…
Self-supervised learning (SSL) methods targeting scene images have seen a rapid growth recently, and they mostly rely on either a dedicated dense matching mechanism or a costly unsupervised object discovery module. This paper shows that…
Speckles arise when coherent light interacts with biological tissues. Information retrieval from speckles is desired yet challenging, requiring understanding or mapping of the multiple scattering process, or reliable capability to reverse…