Sazuna/UAT_keywords huggingface dataset:
The Near Infrared Imager and Slitless Spectrograph for the James Webb Space Telescope. I. Instrument Overview and In-flight Performance. The Near-Infrared Imager and Slitless Spectrograph (NIRISS) is the science module of the Canadian-built Fine Guidance Sensor onboard the James Webb Space Telescope (JWST). NIRISS has four observing modes: (1) broadband imaging featuring seven of the eight NIRCam broadband filters, (2) wide-field slitless spectroscopy at a resolving power of ~150 between 0.8 and 2.2 μm, (3) single-object cross-dispersed slitless spectroscopy (SOSS) enabling simultaneous wavelength coverage between 0.6 and 2.8 μm at R ~ 700, a mode optimized for exoplanet spectroscopy of relatively bright (J &lt; 6.3) stars and (4) aperture masking interferometry (AMI) between 2.8 and 4.8 μm enabling high-contrast (~10-3 - 10-4) imaging at angular separations between 70 and 400 mas for relatively bright (M &lt; 8) sources. This paper presents an overview of the NIRISS instrument, its design, its scientific capabilities, and a summary of in-flight performance. NIRISS shows significantly better response shortward of ~2.5 μm resulting in 10%-40% sensitivity improvement for broadband and low-resolution spectroscopy compared to pre-flight predictions. Two time-series observations performed during instrument commissioning in the SOSS mode yield very stable spectro-photometry performance within ~10% of the expected noise. The first space-based companion detection of the tight binary star AB Dor AC through AMI was demonstrated.

Keywords: Space vehicle instruments, Astronomical instrumentation
Examining the Influence of the Regions on Star Formation Surface Density. We analyzed the star formation surface density (ΣSFR) between the global and H II regions in a sample of 69 low surface brightness galaxies (LSBGs) and 68 star-forming (SF) galaxies using data from the Hα images. The conventional global ΣSFR, which is defined as the star formation rate (SFR) divided by the area of the global galaxy, may not accurately describe the star formation activity in LSBGs due to the lower number of H II regions compared to SF galaxies. To address this, we divide the global galaxy into two regions, the H II region and the diffuse region, and then study the ΣSFR in each region. Our results show that both the SFR and area of the H II regions in LSBGs are lower than those in SF galaxies, resulting in the H II region's ΣSFR (SFR/area) being slightly lower in LSBGs than in SF galaxies by 0.28 dex, although the global ΣSFR is at least an order of magnitude lower in LSBGs than in SF galaxies. Furthermore, a significant difference exists between the global and H II regions in ΣSFR. In LSBGs, ΣSFR increased by 0.80 dex from the global region to the H II region, while SF galaxies demonstrate a 0.54 dex increase, highlighting the crucial aspect of carefully selecting an appropriate aperture for ΣSFR calculations.

Keywords: Galaxy formation
Fitting Optical Light Curves of Tidal Disruption Events with TiDE. A Tidal Disruption Event (TDE) occurs when a supermassive black hole tidally disrupts a nearby passing star. The fallback accretion rate of the disrupted star may exceed the Eddington limit, which induces a supersonic outflow and a burst of luminosity, similar to an explosive event. Thus, TDEs can be detected as very luminous transients, and the number of observations for such events is increasing rapidly. In this paper we fit 20 TDE light curves with TiDE, a new public, object-oriented code designed to model optical TDE light curves. We compare our results with those obtained by the popular MOSFiT and the recently developed TDEmass codes, and discuss the possible sources of differences.

Keywords: Accretion, Supermassive black holes, Tidal disruption
A Galaxy Morphology Classification Model Based on Momentum Contrastive Learning. The taxonomy of galaxy morphology plays an important role in astrophysics and provides great help for the study of galaxy evolution. To integrate the advantages of unsupervised learning without labels and supervised learning with high classification accuracy, this paper proposes a galaxy morphology classification model based on a momentum contrastive learning algorithm named Momentum Contrastive Learning Galaxy (MCL-Galaxy), which mainly includes two parts (i) pre-training of the model, where the ResNet_50 backbone network acts as an encoder to learn the galaxy morphology image features, which are stored in the queue and their consistency is ensured by using the momentum contrastive learning algorithm; and (ii) transfer learning, where Mahalanobis distance can assist in improving classification accuracy in downstream tasks where both encoder and queue are transferred. To evaluate the performance of MCL-Galaxy, we use the data set of the Galaxy Zoo challenge project on Kaggle for comparative testing. The experimental results show that the classification accuracy of MCL-Galaxy can reach 90.12%, which is 8.12% higher than the unsupervised state-of-the-art results. Although it is 3.1% lower than the advanced supervised method, it has the advantage of no label and can achieve a higher accuracy rate at the first epoch of classification iteration. This suggests that the gap between unsupervised and supervised representation learning in the field of Galaxy Morphologies classification tasks is well bridged.

Keywords: Astronomy data analysis, Galaxy classification systems, Neural networks
A Multimodal Transfer Learning Method for Classifying Images of Celestial Point Sources. A large fraction of celestial objects exhibit point shapes in CCD images, such as stars and QSOs, which contain less information due to their few pixels. Point source classification based solely on image data may lead to low accuracy. To address this challenge, this paper proposes a Multi-modal Transfer Learning-based classification method for celestial objects with point shape images. Considering that spectral data possess rich features and that there is a correlation between spectral data and image data, the proposed approach fully utilizes the knowledge gained from celestial spectral data and transfers it to the original image-based classification, enhancing the accuracy of classifying stars and QSOs. Initially, a one-dimensional residual network is employed to extract a 128-dimensional spectral feature vector from the original 3700-dimensional spectral data. This spectral feature vector captures important features of the celestial object. The Generative Adversarial Network is then utilized to generate a simulated spectral vector of 128 dimensions, which corresponds to the celestial object image. By generating simulated spectral vectors, data from two modals (spectral and image) for the same celestial object are available, enriching the input features of the model. In the upcoming multimodal classification model, we only require the images of celestial objects along with their corresponding simulated spectral data, and we no longer need real spectral data. With the assistance of spectral data, the proposed method alleviates the above disadvantages of the original image-based classification method. Remarkably, our method has improved the F1-score from 0.93 to 0.9777, while reducing the error rate in classification by 40%. These enhancements significantly increase the classification accuracy of stars and QSOs, providing strong support for the classification of celestial point sources.

Keywords: Dimensionality reduction, Astronomy data analysis, Classification
Fifteen Years of Millimeter Accuracy Lunar Laser Ranging with APOLLO: Data Reduction and Calibration. The Apache Point Lunar Laser-ranging Operation (APOLLO) has been collecting lunar range measurements for 15 yr at millimeter accuracy. The median nightly range uncertainty since 2006 is 1.7 mm. A recently added Absolute Calibration System (ACS), providing an independent assessment of APOLLO system accuracy and the capability to correct lunar range data, revealed a ~0.4% (10 ps) systematic error in the calibration of one piece of hardware that has been present for the entire history of APOLLO. The application of ACS-based timing corrections suggests systematic errors are reduced to &lt;1 mm, such that overall data accuracy and precision are both ~1 mm. This paper describes the processing of APOLLO/ACS data that converts photon-by-photon range measurements into the aggregated normal points that are used for science analyses. Additionally, we present methodologies to estimate timing corrections for range data lacking contemporaneous ACS photons, including range data collected prior to installation of the ACS. We also provide access to the full 15 yr archive of APOLLO normal points (2006 April 6-2020 December 27).

Keywords: Gravitation, General relativity, Lunar geodesy
Fifteen Years of Millimeter Accuracy Lunar Laser Ranging with APOLLO: Data Set Characterization. We present data from the Apache Point Observatory Lunar Laser-ranging Operation (APOLLO) covering the 15 yr span from 2006 April through the end of 2020. APOLLO measures the Earth-Moon separation by recording the round-trip travel time of photons from the Apache Point Observatory to five retro-reflector arrays on the Moon. The APOLLO data set, combined with the 50 yr archive of measurements from other lunar laser ranging (LLR) stations, can be used to probe fundamental physics such as gravity and Lorentz symmetry, as well as properties of the Moon itself. We show that range measurements performed by APOLLO since 2006 have a median nightly accuracy of 1.7 mm, which is significantly better than other LLR stations.

Keywords: Gravitation, General relativity, Lunar geodesy
Improvement of Digest2 NEO Classification Code-utilizing the Astrometry Data Exchange Standard. We describe enhancements to the digest2 software, a short-arc orbit classifier for heliocentric orbits. Digest2 is primarily used by the Near-Earth Object (NEO) community to flag newly discovered objects for a immediate follow-up and has been a part of NEO discovery process for more than 15 yr. We have updated the solar system population model used to weight the digest2 score according to the 2023 catalog of known solar system orbits and extended the list of mean uncertainties for 140 observatory codes. Moreover, we have added Astrometry Data Exchange Standard (ADES) input format support to digest2, which provides additional information for the astrometry, such as positional uncertainties for each detection. The digest2 code was also extended to read the roving observer astrometric format as well as the ability to compute a new parameter from the provided astrometric uncertainties ( ${RMS}^{\prime} $ ) that can serve as an indicator of in-tracklet curvature when compared with tracklet's great-circle fit rms. Comparison with the previous version of digest2 confirmed the improvement in accuracy of NEO identification and found that using ADES XML input significantly reduces the computation time of the digest2.

Keywords: Near-Earth objects, Asteroids, Astronomy software, Surveys, Astronomy data analysis, Astrometry, Orbit determination
The Adaptive Optics System for the Gemini Infrared Multi-Object Spectrograph: Performance Modeling. The Gemini Infrared Multi-Object Spectrograph (GIRMOS) will be a near-infrared, multi-object, medium spectral resolution, integral field spectrograph (IFS) for Gemini North Telescope, designed to operate behind the future Gemini North Adaptive Optics system (GNAO). In addition to a first ground layer Adaptive Optics (AO) correction in closed loop carried out by GNAO, each of the four GIRMOS IFSs will independently perform additional multi-object AO correction in open loop, resulting in an improved image quality that is critical to achieve top level science requirements. We present the baseline parameters and simulated performance of GIRMOS obtained by modeling both the GNAO and GIRMOS AO systems. The image quality requirement for GIRMOS is that 57% of the energy of an unresolved point-spread function ensquared within a 0.1 × 0.1 arcsecond at 2.0 μ m. It was established that GIRMOS will be an order 16 × 16 adaptive optics (AO) system after examining the tradeoffs between performance, risks and costs. The ensquared energy requirement will be met in median atmospheric conditions at Maunakea at 30° from zenith.

Keywords: Astronomical instrumentation, Astronomical simulations, Telescopes
Rubin Observatory LSST Transients and Variable Stars Roadmap. The Vera C. Rubin Legacy Survey of Space and Time (LSST) holds the potential to revolutionize time domain astrophysics, reaching completely unexplored areas of the Universe and mapping variability time scales from minutes to a decade. To prepare to maximize the potential of the Rubin LSST data for the exploration of the transient and variable Universe, one of the four pillars of Rubin LSST science, the Transient and Variable Stars Science Collaboration, one of the eight Rubin LSST Science Collaborations, has identified research areas of interest and requirements, and paths to enable them. While our roadmap is ever-evolving, this document represents a snapshot of our plans and preparatory work in the final years and months leading up to the survey's first light.

Keywords: Brown dwarfs, Binary stars, Exoplanets, Pulsating variable stars, Blazars, Tidal disruption, Young stellar objects, Supernovae, Microlensing event rate, Telescopes
Workshop Summary: Exoplanet Orbits and Dynamics. Exoplanetary systems show a wide variety of architectures, which can be explained by different formation and dynamical evolution processes. Precise orbital monitoring is mandatory to accurately constrain their orbital and dynamical parameters. Although major observational and theoretical advances have been made in understanding the architecture and dynamical properties of exoplanetary systems, many outstanding questions remain. This paper aims to give a brief review of a few current challenges in orbital and dynamical studies of exoplanetary systems and a few future prospects for improving our knowledge. Joint data analyses from several techniques are providing precise measurements of orbits and masses for a growing sample of exoplanetary systems, both with close-in orbits and with wide orbits, as well as different evolutionary stages. The sample of young planets detected around stars with circumstellar disks is also growing, allowing for simultaneous studies of planets and their birthplace environments. These analyses will expand with ongoing and future facilities from both ground and space, allowing for detailed tests of formation, evolution, and atmospheric models of exoplanets. Moreover, these detailed analyses may offer the possibility of finding missing components of exoplanetary systems, such as exomoons, or even finding new exotic configurations such as co-orbital planets. In addition to unveiling the architecture of planetary systems, precise measurements of orbital parameters and stellar properties-in combination with more realistic models for tidal interactions and the integration of such models in N-body codes-will improve the inference of the past history of mature exoplanetary systems in close-in orbits. These improvements will allow a better understanding of planetary formation and evolution, placing the solar system in context.

Keywords: Exoplanet dynamics, Orbit determination, Exoplanets
Nonlinear Wave Front Reconstruction from a Pyramid Sensor using Neural Networks. The pyramid wave front sensor (PyWFS) has become increasingly popular to use in adaptive optics (AO) systems due to its high sensitivity. The main drawback of the PyWFS is that it is inherently nonlinear, which means that classic linear wave front reconstruction techniques face a significant reduction in performance at high wave front errors, particularly when the pyramid is unmodulated. In this paper, we consider the potential use of neural networks (NNs) to replace the widely used matrix vector multiplication (MVM) control. We aim to test the hypothesis that the NN's ability to model nonlinearities will give it a distinct advantage over MVM control. We compare the performance of a MVM linear reconstructor against a dense NN, using daytime data acquired on the Subaru Coronagraphic Extreme Adaptive Optics system (SCExAO) instrument. In a first set of experiments, we produce wavefronts generated from 14 Zernike modes and the PyWFS responses at different modulation radii (25, 50, 75, and 100 mas). We find that the NN allows for a far more precise wave front reconstruction at all modulations, with differences in performance increasing in the regime where the PyWFS nonlinearity becomes significant. In a second set of experiments, we generate a data set of atmosphere-like wavefronts, and confirm that the NN outperforms the linear reconstructor. The SCExAO real-time computer software is used as baseline for the latter. These results suggest that NNs are well positioned to improve upon linear reconstructors and stand to bring about a leap forward in AO performance in the near future.

Keywords: Astronomical optics, Neural networks
Linking Sky-plane Observations of Moving Objects. The Asteroid Terrestrial-impact Last Alert System (ATLAS) observes the visible sky every night in search of dangerous asteroids. With four (soon five) sites ATLAS is facing new challenges for scheduling observations and linking detections to identify moving asteroids. Flexibility in coping with diverse observation sites and times of detections that can be linked is critical, as is optimization of observing time for coverage versus depth. We present new algorithms to fit orbits rapidly to sky-plane observations, and to test and link sets of detections to find the ones which belong to moving objects. The PUMA algorithm for fitting orbits to angular positions on the sky executes in about a millisecond, orders of magnitude faster than the methods currently in use by the community, without sacrifice in accuracy. The PUMA software should be generally useful to anyone who needs to test many sets of detections for consistency with a real orbit. The PUMALINK algorithm to find linkages among sets of detections has similarities to other approaches, notably HelioLinC, but it functions well at asteroid ranges of a small fraction of an astronomical unit. PUMALINK is fast enough to test 10 million possible tracklets against one another in a half hour of computer time. Candidate linkages are checked by the PUMA library to test that the detections correspond to a real orbit, even at close range, and the false alarm rate is manageable. Sky surveys that produce large numbers of detections from large numbers of exposures may find the PUMALINK software helpful. We present the results of tests of PUMALINK on three data sets which illustrate PUMALINK's effectiveness and economy: 2 weeks of all ATLAS detections over the sky, 2 weeks of special ATLAS opposition observations with long exposure time, and 2 weeks of simulated LSST asteroid observations. Detection probabilities of linkages must be traded against false alarm rate, but a representative choice for PUMALINK might be 90% detection probability for real objects while keeping the false alarm rate below 10% for a 100:1 population of false:real. Although optimization of the tradeoffs between detection probability, execution time, and false alarm rate is application specific and beyond the scope of this paper, we provide guidance on methods to distinguish false alarms from correct linkages of real objects.

Keywords: Near-Earth objects, Asteroids, Astronomy software, Astronomy data analysis, Computational methods, Orbit determination
Comparative Analysis of Image-shift Measurement Algorithms for Solar Shack-Hartmann Wavefront Sensors. Observations of the Sun provide unique insights into its structure, evolution, and activity, with significant implications for space weather forecasting and solar energy technologies. Ground-based telescopes offer cost-effective and flexible solutions for high-resolution solar observations, but image quality can be affected by atmospheric turbulence. Adaptive optics (AO) systems equipped with Shack-Hartmann wave front sensors (SH-WFS) enable real-time image correction to mitigate these effects. The accuracy of SH-WFS relies on correlation algorithms that measure wave front shifts, but reaching consistent conclusions regarding their accuracy remains challenging. In this study, we conducted an evaluation and comparison of standard correlation algorithms (the Square Difference Function, Normalized Cross-Correlation, Absolute Difference Function, Absolute Difference Function-Squared, and the Covariance Function in the frequency domain (CFF)) using simulated and authentic solar images. We optimized the algorithms through pre-processing techniques and carefully selected the most suitable window function for the CFF algorithm. Additionally, we analyzed the influence of various factors, such as shift ranges, bias, and the size of live images on the accuracy of algorithms. The consistent findings revealed that the CFF algorithm demonstrates superior measurement accuracy and robustness compared to the others. Choosing the CFF algorithm for solar observations can significantly enhance measurement accuracy, AO system performance, and the overall quality of solar research findings, thereby providing crucial support for space weather forecasting and other related scientific fields.

Keywords: Atmospheric effects, Astronomical techniques, Solar observatories
Methods for Averaging Spectral Line Data. The ideal spectral averaging method depends on one's science goals and the available information about one's data. Including low-quality data in the average can decrease the signal-to-noise ratio (S/N), which may necessitate an optimization method or a consideration of different weighting schemes. Here, we explore a variety of spectral averaging methods. We investigate the use of three weighting schemes during averaging: weighting by the signal divided by the variance ("intensity-noise weighting"), weighting by the inverse of the variance ("noise weighting"), and uniform weighting. Whereas for intensity-noise weighting the S/N is maximized when all spectra are averaged, for noise and uniform weighting we find that averaging the 35%-45% of spectra with the highest S/N results in the highest S/N average spectrum. With this intensity cutoff, the average spectrum with noise or uniform weighting has ~95% of the intensity of the spectrum created from intensity-noise weighting. We apply our spectral averaging methods to GBT Diffuse Ionized Gas hydrogen radio recombination line data to determine the ionic abundance ratio, y +, and discuss future applications of the methodology.

Keywords: Warm ionized medium, Computational methods
Large-field Astronomical Image Restoration and Superresolution Reconstruction using Deep Learning. The existing astronomical image restoration and superresolution reconstruction methods have problems such as low efficiency and poor results when dealing with images possessing large fields of view. Furthermore, these methods typically only handle fixed-size images and require step-by-step processing, which is inconvenient. In this paper, a neural network called Res&amp;RecNet is proposed for the restoration and superresolution reconstruction of astronomical images with large fields of view for direct imaging instruments. This network performs feature extraction, feature correction, and progressive generation to achieve image restoration and superresolution reconstruction. The network is constructed using fully convolutional layers, allowing it to handle images of any size. The network can be trained using small samples and can perform image restoration and superresolution reconstruction in an end-to-end manner, resulting in high efficiency. Experimental results show that the network is highly effective in terms of processing astronomical images with complex scenes, generating image restoration results that improve the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) by 4.69 (dB)/0.073 and superresolution reconstruction results that improve the PSNR and SSIM by 1.97 (dB)/0.077 over those of the best existing algorithms, respectively.

Keywords: Optical observatories, Algorithms, Neural networks