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NASA NTRS1979

Application of digital image processing techniques to astronomical imagery, 1979

Several areas of applications of image processing to astronomy were identified and discussed. These areas include: (1) deconvolution for atmospheric seeing compensation; a comparison between maximum entropy and conventional Wiener algorithms; (2) polarization in galaxies from photographic plates; (3) time changes in M87 and methods of displaying these changes; (4) comparing emission line images in planetary nebulae; and (5) log intensity, hue saturation intensity, and principal component color enhancements of M82. Examples are presented of these techniques applied to a variety of objects.

Lorre, J. J.↗

DOE OSTI2024

Search for Periodic Time Variations of the Solar 8 B Neutrino Flux between 1996 and 2018 in Super-Kamiokande

We report a search for time variations of the solar 8 B neutrino flux using 5804 live days of Super-Kamiokande data collected between May 31, 1996, and May 30, 2018. Super-Kamiokande measured the precise time of each solar neutrino interaction over 22 calendar years to search for solar neutrino flux modulations with unprecedented precision. Periodic modulations are searched for in a dataset comprising five-day interval solar neutrino flux measurements with a maximum likelihood method. We also applied the Lomb-Scargle method to this dataset to compare it with previous reports. The only significant modulation found is due to the elliptic orbit of the Earth around the Sun. The observed modulation is consistent with astronomical data: we measured an eccentricity of (1.53 ± 0.35)%, and a perihelion shift of (−1.5 ± 13.5) days.

astroparticle detectors↗

DOE OSTI2023

Quarknet Project: Simulating Double-Source Plane Lenses and Einstein Rings for Domain Adaptation

Double-Source Plane Lenses (DSPLs) are a form of strong gravitational lensing that can be used to uniquely constrain cosmological parameters like the dark energy equation of state. These objects are exceedingly rare: to date, only four have been discovered in astronomical surveys, like the Sloan Digital Sky Survey and the HyperSuprimeCam Survey. Additionally, the main features of these objects are typically co-located sets of arcs, which look like Einstein Rings of single-source galaxy-galaxy lenses. Larger astronomical surveys typically have low image resolution, which significantly exacerbates issues when trying to find DSPLs. Future surveys are likely to include many more of these objects and have higher image resolution. Traditional methods for finding strong lenses struggle to identify simple galaxy-galaxy lenses. Recently, neural networks have become the standard for lens identification, but these tools have not yet been used to search for DSPLs. In this work, we use the software deeplenstronomy to simulate varied images of DSPLs and Einstein Rings – with and without noise and artifacts of observational surveys, forming a comprehensive benchmark dataset of 40,000 images. We utilise this dataset to perform experiments with convolutional neural networks to discern between the two phenomena. Sofia Grimm and Jerry Zhou are co-first authors on this work.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2026

Opportunities in AI/ML for the Rubin LSST Dark Energy Science Collaboration

The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will produce unprecedented volumes of heterogeneous astronomical data (images, catalogs, and alerts) that challenge traditional analysis pipelines. The LSST Dark Energy Science Collaboration (DESC) aims to derive robust constraints on dark energy and dark matter from these data, requiring methods that are statistically powerful, scalable, and operationally reliable. Artificial intelligence and machine learning (AI/ML) are already embedded across DESC science workflows, from photometric redshifts and transient classification to weak lensing inference and cosmological simulations. Yet their utility for precision cosmology hinges on trustworthy uncertainty quantification, robustness to covariate shift and model misspecification, and reproducible integration within scientific pipelines. This white paper surveys the current landscape of AI/ML across DESC's primary cosmological probes and cross-cutting analyses, revealing that the same core methodologies and fundamental challenges recur across disparate science cases. Since progress on these cross-cutting challenges would benefit multiple probes simultaneously, we identify key methodological research priorities, including Bayesian inference at scale, physics-informed methods, validation frameworks, and active learning for discovery. With an eye on emerging techniques, we also explore the potential of the latest foundation model methodologies and LLM-driven agentic AI systems to reshape DESC workflows, provided their deployment is coupled with rigorous evaluation and governance. Finally, we discuss critical software, computing, data infrastructure, and human capital requirements for the successful deployment of these new methodologies, and consider associated risks and opportunities for broader coordination with external actors.

Aubourg, Eric [APC, Paris] (ORCID:000000025592023X↗

NASA NTRS1989

Catadioptric optics for laser Doppler velocimeter applications

This paper examines the adaptation of low-cost Schmidt-Cassegrain astronomical telescopes to perform the laser-beam-focusing and scattered-light collection tasks associated with dual-beam laser Doppler velocimetry. A generic telescope design is analyzed using ray-tracing methods and Gaussian beam-propagation theory. A straightforward modification procedure to convert from infinite to near unity conjugate-ratio operation with very low residual aberration is identified and tested with a 200-mm-aperture telescope modified for f/10 operation. Performance data for this modified telescope configuration are near the diffraction limit and agree well with predictions.

Dunagan, Stephen E.↗

DOE OSTI2023

Automatic detection of cataclysmic variables from SDSS images

Abstract Investigating rare and new objects have always been an important direction in astronomy. Cataclysmic variables (CVs) are ideal and natural celestial bodies for studying the accretion process of semi-detached binaries with accretion processes. However, the sample size of CVs must increase because a lager gap exists between the observational and the theoretical expanding CVs. Astronomy has entered the big data era and can provide massive images containing CV candidates. CVs as a type of faint celestial objects, are highly challenging to be identified directly from images using automatic manners. Deep learning has rapidly developed in intelligent image processing and has been widely applied in some astronomical fields with excellent detection results. YOLOX, as the latest YOLO framework, is advantageous in detecting small and dark targets. This work proposes an improved YOLOX-based framework according to the characteristics of CVs and Sloan Digital Sky Survey (SDSS) photometric images to train and verify the model to realise CV detection. We use the Convolutional Block Attention Module to increase the number of output features with the feature extraction network and adjust the feature fusion network to obtain fused features. Accordingly, the loss function is modified. Experimental results demonstrate that the improved model produces satisfactory results, with average accuracy (mean average Precision at 0.5) of 92.0%, Precision of 92.9%, Recall of 94.3%, and $F1-score$ of 93.6% on the test set. The proposed method can efficiently achieve the identification of CVs in test samples and search for CV candidates in unlabeled images. The image data vastly outnumber the spectra in the SDSS-released data. With supplementary follow-up observations or spectra, the proposed model can help astronomers in seeking and detecting CVs in a new manner to ensure that a more extensive CV catalog can be built. The proposed model may also be applied to the detection of other kinds of celestial objects.

Astronomy & Astrophysics↗

DOE OSTI2022

Realistic galaxy image simulation via score-based generative models

ABSTRACT We show that a denoising diffusion probabilistic model (DDPM), a class of score-based generative model, can be used to produce realistic mock images that mimic observations of galaxies. Our method is tested with Dark Energy Spectroscopic Instrument (DESI) grz imaging of galaxies from the Photometry and Rotation curve OBservations from Extragalactic Surveys (PROBES) sample and galaxies selected from the Sloan Digital Sky Survey. Subjectively, the generated galaxies are highly realistic when compared with samples from the real data set. We quantify the similarity by borrowing from the deep generative learning literature, using the ‘Fréchet inception distance’ to test for subjective and morphological similarity. We also introduce the ‘synthetic galaxy distance’ metric to compare the emergent physical properties (such as total magnitude, colour, and half-light radius) of a ground truth parent and synthesized child data set. We argue that the DDPM approach produces sharper and more realistic images than other generative methods such as adversarial networks (with the downside of more costly inference), and could be used to produce large samples of synthetic observations tailored to a specific imaging survey. We demonstrate two potential uses of the DDPM: (1) accurate inpainting of occluded data, such as satellite trails, and (2) domain transfer, where new input images can be processed to mimic the properties of the DDPM training set. Here we ‘DESI-fy’ cartoon images as a proof of concept for domain transfer. Finally, we suggest potential applications for score-based approaches that could motivate further research on this topic within the astronomical community.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1981

Studies in astronomical time series analysis. I - Modeling random processes in the time domain

Several random process models in the time domain are defined and discussed. Attention is given to the moving average model, the autoregressive model, and relationships between and combinations of these models. Consideration is then given to methods for investigating pulse structure, procedures of model construction, computational methods, and numerical experiments. A FORTRAN algorithm of time series analysis has been developed which is relatively stable numerically. Results of test cases are given to study the effect of adding noise and of different distributions for the pulse amplitudes. A preliminary analysis of the light curve of the quasar 3C 272 is considered as an example.

Scargle, J. D.↗

NASA NTRS2012

Comparative Modelling of the Spectra of Cool Giants

Our ability to extract information from the spectra of stars depends on reliable models of stellar atmospheres and appropriate techniques for spectral synthesis. Various model codes and strategies for the analysis of stellar spectra are available today. Aims. We aim to compare the results of deriving stellar parameters using different atmosphere models and different analysis strategies. The focus is set on high-resolution spectroscopy of cool giant stars. Methods. Spectra representing four cool giant stars were made available to various groups and individuals working in the area of spectral synthesis, asking them to derive stellar parameters from the data provided. The results were discussed at a workshop in Vienna in 2010. Most of the major codes currently used in the astronomical community for analyses of stellar spectra were included in this experiment. Results. We present the results from the different groups, as well as an additional experiment comparing the synthetic spectra produced by various codes for a given set of stellar parameters. Similarities and differences of the results are discussed. Conclusions. Several valid approaches to analyze a given spectrum of a star result in quite a wide range of solutions. The main causes for the differences in parameters derived by different groups seem to lie in the physical input data and in the details of the analysis method. This clearly shows how far from a definitive abundance analysis we still are.

stars↗

NASA NTRS1984

Problems and procedures of cometary astometry

Photography of comets or asteroids for the purpose of astrometry requires consideration of a few details not met with in other astronomical problems. In the general case the object has a substantial non-sidereal motion and the decision must be made whether to track on the fiducial stars or on the object. The best method for introducing the required image motion compensation to the detector will depend on the focal plane facilities. Measurement of elongated images and the problems involved are discussed.

Mccrosky, R. E.↗

NASA NTRS1987

The impact of infrared arrays on 1-5 microns spectroscopy

The use of two-dimensional infrared arrays for astronomical imaging and spectroscopy is now occurring and immensely changing the field. In view of this 'revolution' in infrared astronomy, some of the implications for future infrared spectroscopy are discussed in this paper. A method is presented for comparing the infrared background to future spectrometers at 1-5 microns. Some of the current developments in infrared spectroscopy at Mauna Kea are discussed.

Tokunaga, Alan↗

NASA NTRS1996

The Millimeter- and Submillimeter-Wave Spectrum of Gauche-Ethyl Alcohol

We report an investigation of the rotational-torsional spectrum of the gauche rotational isomers of ethyl alcohol in the 51-505 GHz frequency region. Over a thousand transitions between rotational levels in the gauche substates of the ground OH torsional state have been measured and assigned. These transitions involve rotational quantum numbers J and K(sub a) up to 30 and 15, respectively, and are of two types: alpha-type transitions between levels in either the gauche+ or the gauche-substate, and c-type transitions between rotational levels in the different substates. The majority of these transitions have been fit satisfactorily using a two-state Hamiltonian based on the Fixed Framework Axis Method (FFAM). The rotation, distortion, and interaction constants have been determined along with the energy difference between the two gauche substates. The derived constants can be used to predict many more transitions accurately for astronomical purposes. The J and K(sub a) region where the two-state analysis can be used has been determined. The basis for a three-state analysis including the trans substate is presented and the applicability of the FFAM approach is discussed.

Pearson, J. C.↗

NASA NTRS1995

The Millimeter- and Submillimeter-Wave Spectrum of Gauche-Ethyl Alcohol

We report an investigation of the rotational-torsional spectrum of the gauche rotational isomers of ethyl alcohol in the 51е05 GHz frequency region. Over a thousand transitions between rotational levels in the gauche substates of the ground OH torsional state have been measured and assigned. These transitions involve rotational quantum numbers J and Ka up to 30 and 15, respectively, and are of two types: a-type transitions between levels in either the gauche+ or the gaucheРsubstate, and c-type transitions between rotational levels in the different substates. The majority of these transitions have been fit satisfactorily using a two-state Hamiltonian based on the fixed framework axis method (FFAM). The rotation, distortion, and interaction constants have been determined along with the energy difference between the two gauche substates. The derived constants can be used to predict many more transitions accurately for astronomical purposes. The J and Ka region where the two-state analysis can be used has been determined. The basis for a three-state analysis including the trans substate is presented and the applicability of the FFAM approach is discussed.!

rotational-torsional↗

NASA NTRS2016

Alignment and Distortion-Free Integration of Lightweight Mirrors into Meta-Shells for High-Resolution Astronomical X-Ray Optics

High-resolution, high throughput optics for x-ray astronomy requires fabrication of well-formed mirror segments and their integration with arc-second level precision. Recently, advances of fabrication of silicon mirrors developed at NASA/Goddard prompted us to develop a new method of mirror integration. The new integration scheme takes advantage of the stiffer, more thermally conductive, and lower-CTE silicon, compared to glass, to build a telescope of much lighter weight. In this paper, we address issues of aligning and bonding mirrors with this method. In this preliminary work, we demonstrated the basic viability of such scheme. Using glass mirrors, we demonstrated that alignment error of 1" and bonding error 2" can be achieved for mirrors in a single shell. We will address the immediate plan to demonstrate the bonding reliability and to develop technology to build up a mirror stack and a whole "meta-shell".

X-ray optics↗

NASA NTRS1973

Application of the SIT vidicon to astronomical measurements.

Review of the results of a detailed investigation of the properties and merits of silicon target (SIT) vidicon photometry, compared to previous photoelectric and photographic photometry, in astronomy applications. Following a description of the SIT-vidicon based photometry system and of its operating procedures, the data reduction methods are examined, and some of the results obtained are discussed.

Westphal, J. A.↗

NASA NTRS2019

The Refractive Index of Amorphous and Crystalline Water Ice in the UV-vis

Amorphous solid water (ASW) is found on icy dust grains in the interstellar medium (ISM), as well as on comets and other icy objects in the outer solar system. The optical properties of ASW are thus relevant for many astrophysical environments, but in the ultraviolet–visible (UV–vis), its refractive index is not well constrained. Here, we introduce a new method based on UV–vis broadband interferometry to measure the wavelength dependent refractive index n(λ) of amorphous water ice from 10 to 130 K, i.e., for different porosities, in the wavelength range of 210–757 nm. We also present n(λ) for crystalline water ice at 150 K, which allows us to compare our new method with literature data. Based on this, a method to calculate n(λ, ρ) as a function of wavelength and porosity is reported. This new approach carries much potential and is generally applicable to pure and mixed ice, both amorphous and crystalline. The astronomical and physical–chemical relevance and future potential of this work are discussed.

Kofman, Vincent↗

DOE OSTI2022

Automatic Detection and Classification of Radio Galaxy Images by Deep Learning

Abstract Surveys conducted by radio astronomy observatories, such as SKA, MeerKAT, Very Large Array, and ASKAP, have generated massive astronomical images containing radio galaxies (RGs). This generation of massive RG images has imposed strict requirements on the detection and classification of RGs and makes manual classification and detection increasingly difficult, even impossible. Rapid classification and detection of images of different types of RGs help astronomers make full use of the observed astronomical image data for further processing and analysis. The classification of FRI and FRII is relatively easy, and there are more studies and literature on them at present, but FR0 and FRI are similar, so it is difficult to distinguish them. It poses a greater challenge to image processing. At present, deep learning has made breakthrough progress in the field of image analysis and processing and has preliminary applications in astronomical data processing. Compared with classification algorithms that can only classify galaxies, object detection algorithms that can locate and classify RGs simultaneously are preferred. In target detection algorithms, YOLOv5 has outstanding advantages in the classification and positioning of small targets. Therefore, we propose a deep-learning method based on an improved YOLOv5 object detection model that makes full use of multisource data, combining FIRST radio with SDSS optical image data, and realizes the automatic detection of FR0, FRI, and FRII RGs. The innovation of our work is that on the basis of the original YOLOv5 object detection model, we introduce the SE Net attention mechanism, increase the number of preset anchors, adjust the network structure of the feature pyramid, and modify the network structure, thereby allowing our model to demonstrate galaxy classification and position detection effects. Our improved model produces satisfactory results, as evidenced by experiments. Overall, the mean average precision (mAP@0.5) of our improved model on the test set reaches 89.4%, which can determine the position (R.A. and decl.) and automatically detect and classify FR0s, FRIs, and FRIIs. Our work contributes to astronomy because it allows astronomers to locate FR0, FRI, and FRII galaxies in a relatively short time and can be further combined with other astronomically generated data to study the properties of these galaxies. The target detection model can also help astronomers find FR0s, FRIs, and FRIIs in future surveys and build a large-scale star RG catalog. Moreover, our work is also useful for the detection of other types of galaxies.

Astronomy & Astrophysics↗

DOE OSTI2025

Survey-wide asteroid discovery with a high-performance computing enabled non-linear digital tracking framework

Modern astronomical surveys detect asteroids by linking together their appearances across multiple images taken over time. This approach faces limitations in detecting faint asteroids and handling the computational complexity of trajectory linking. Here, we present a novel method that adapts “digital tracking” – traditionally used for short-term linear asteroid motion across images – to work with large-scale synoptic surveys such as the Vera Rubin Observatory Legacy Survey of Space and Time (Rubin/LSST). Our approach combines hundreds of sparse observations of individual asteroids across their non-linear orbital paths to enhance detection sensitivity by several magnitudes. To address the computational challenges of processing massive data sets and dense orbital phase spaces, we developed a specialized high-performance computing architecture. We demonstrate the effectiveness of our method through experiments that take advantage of the extensive computational resources at Lawrence Livermore National Laboratory. This work enables the detection of significantly fainter asteroids in existing and future survey data, potentially increasing the observable asteroid population by orders of magnitude across different orbital families, from near-Earth objects (NEOs) to Kuiper belt objects (KBOs).

Asteroid discovery↗