Generating ultradense pair beams using 400 GeV / c protons
Not Available
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
SEARCH · Engineering Papers
Results for “astronomical optics”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Overview of the distributed image processing infrastructure to produce the Legacy Survey of Space and Time
The Vera C. Rubin Observatory is preparing to execute the most ambitious astronomical survey ever attempted, the Legacy Survey of Space and Time (LSST). Currently the final phase of construction is under way in the Chilean Andes, with the Observatory’s ten-year science mission scheduled to begin in 2025. Rubin’s 8.4-meter telescope will nightly scan the southern hemisphere collecting imagery in the wavelength range 320–1050 nm covering the entire observable sky every 4 nights using a 3.2 gigapixel camera, the largest imaging device ever built for astronomy. Automated detection and classification of celestial objects will be performed by sophisticated algorithms on high-resolution images to progressively produce an astronomical catalog eventually composed of 20 billion galaxies and 17 billion stars and their associated physical properties. In this article we present an overview of the system currently being constructed to perform data distribution as well as the annual campaigns which reprocess the entire image dataset collected since the beginning of the survey. These processing campaigns will utilize computing and storage resources provided by three Rubin data facilities (one in the US and two in Europe). Each year a Data Release will be produced and disseminated to science collaborations for use in studies comprising four main science pillars: probing dark matter and dark energy, taking inventory of solar system objects, exploring the transient optical sky and mapping the Milky Way. Also presented is the method by which we leverage some of the common tools and best practices used for management of large-scale distributed data processing projects in the high energy physics and astronomy communities. We also demonstrate how these tools and practices are utilized within the Rubin project in order to overcome the specific challenges faced by the Observatory.
79 ASTRONOMY AND ASTROPHYSICS↗
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↗
Detecting optical transients using artificial neural networks and reference images from different surveys
ABSTRACT We present a technique to detect optical transients based on an artificial neural networks method. We describe the architecture of two networks capable of comparing images of the same part of the sky taken by different telescopes. One image corresponds to the epoch in which a potential transient could exist; the other is a reference image of an earlier epoch. We use data obtained by the Dr. Cristina V. Torres Memorial Astronomical Observatory and archival reference images from the Sloan Digital Sky Survey. We trained a convolutional neural network and a dense layer network on simulated source samples and then tested the trained networks on samples created from real image data. Autonomous detection methods replace the standard process of detecting transients, which is normally achieved by source extraction of a difference image followed by human inspection of the detected candidates. Replacing the human inspection component with an entirely autonomous method would allow for a rapid and automatic follow-up of interesting targets of opportunity. The toy-model pipeline that we present here is not yet able to replace human inspection, but it might provide useful hints to identify potential candidates. The method will be further expanded and tested on telescopes participating in the Transient Optical Robotic Observatory of the South Collaboration.
79 ASTRONOMY AND ASTROPHYSICS↗
DeepZipper: A Novel Deep-learning Architecture for Lensed Supernovae Identification
Large-scale astronomical surveys have the potential to capture data on large numbers of strongly gravitationally lensed supernovae (LSNe). To facilitate timely analysis and spectroscopic follow-up before the supernova fades, an LSN needs to be identified soon after it begins. To quickly identify LSNe in optical survey data sets, we designed ZipperNet, a multibranch deep neural network that combines convolutional layers (traditionally used for images) with long short-term memory layers (traditionally used for time series). We tested ZipperNet on the task of classifying objects from four categories—no lens, galaxy-galaxy lens, lensed Type-Ia supernova, lensed core-collapse supernova—within high-fidelity simulations of three cosmic survey data sets: the Dark Energy Survey, Rubin Observatory’s Legacy Survey of Space and Time (LSST), and a Dark Energy Spectroscopic Instrument (DESI) imaging survey. Among our results, we find that for the LSST-like data set, ZipperNet classifies LSNe with a receiver operating characteristic area under the curve of 0.97, predicts the spectroscopic type of the lensed supernovae with 79% accuracy, and demonstrates similarly high performance for LSNe 1–2 epochs after first detection. We anticipate that a model like ZipperNet, which simultaneously incorporates spatial and temporal information, can play a significant role in the rapid identification of lensed transient systems in cosmic survey experiments.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Keck Infrared Transient Survey. I. Survey Description and Data Release 1
Abstract We present the Keck Infrared Transient Survey, a NASA Key Strategic Mission Support program to obtain near-infrared (NIR) spectra of astrophysical transients of all types, and its first data release, consisting of 105 NIR spectra of 50 transients. Such a data set is essential as we enter a new era of IR astronomy with the James Webb Space Telescope (JWST) and the upcoming Nancy Grace Roman Space Telescope (Roman). NIR spectral templates will be essential to search JWST images for stellar explosions of the first stars and to plan an effective Roman SN Ia cosmology survey, both key science objectives for mission success. Between 2022 February and 2023 July, we systematically obtained 274 NIR spectra of 146 astronomical transients, representing a significant increase in the number of available NIR spectra in the literature. Here, we describe the first release of data from the 2022A semester. We systematically observed three samples: a flux-limited sample that includes all transients <17 mag in a red optical band (usually ZTF r or ATLAS o bands); a volume-limited sample including all transients within redshift z < 0.01 ( D ≈ 50 Mpc); and an SN Ia sample targeting objects at phases and light-curve parameters that had scant existing NIR data in the literature. The flux-limited sample is 39% complete (60% excluding SNe Ia), while the volume-limited sample is 54% complete and is 79% complete to z = 0.005. Transient classes observed include common Type Ia and core-collapse supernovae, tidal disruption events, luminous red novae, and the newly categorized hydrogen-free/helium-poor interacting Type Icn supernovae. We describe our observing procedures and data reduction using PypeIt , which requires minimal human interaction to ensure reproducibility.
Tinyanont, S. (ORCID:0000000214814676)↗
Deep Generative Modeling of Periodic Variable Stars Using Physical Parameters
The ability to generate physically plausible ensembles of variable sources is critical to the optimization of time domain survey cadences and the training of classification models on data sets with few to no labels. Traditional data augmentation techniques expand training sets by reenvisioning observed exemplars, seeking to simulate observations of specific training sources under different (exogenous) conditions. Unlike fully theory-driven models, these approaches do not typically allow principled interpolation nor extrapolation. Moreover, the principal drawback of theory-driven models lies in the prohibitive computational cost of simulating source observables from ab initio parameters. In this work, we propose a computationally tractable machine learning approach to generate realistic light curves of periodic variables capable of integrating physical parameters and variability classes as inputs. Our deep generative model, inspired by the transparent latent space generative adversarial networks, uses a variational autoencoder (VAE) architecture with temporal convolutional network layers, trained using the OGLE-III optical light curves and physical characteristics (e.g., effective temperature and absolute magnitude) from Gaia DR2. A test using the temperature–shape relationship of RR Lyrae demonstrates the efficacy of our generative “physics-enhanced latent space VAE” (PELS-VAE) model. Such deep generative models, serving as nonlinear nonparametric emulators, present a novel tool for astronomers to create synthetic time series over arbitrary cadences.
79 ASTRONOMY AND ASTROPHYSICS↗
Siena Galaxy Atlas 2020
Abstract We present the 2020 version of the Siena Galaxy Atlas (SGA-2020), a multiwavelength optical and infrared imaging atlas of 383,620 nearby galaxies. The SGA-2020 uses optical grz imaging over ≈20,000 deg 2 from the Dark Energy Spectroscopic Instrument (DESI) Legacy Imaging Surveys Data Release 9 and infrared imaging in four bands (spanning 3.4–22 μ m) from the 6 year unWISE coadds; it is more than 95% complete for galaxies larger than R (26) ≈ 25″ and r < 18 measured at the 26 mag arcsec −2 isophote in the r band. The atlas delivers precise coordinates, multiwavelength mosaics, azimuthally averaged optical surface-brightness profiles, model images and photometry, and additional ancillary metadata for the full sample. Coupled with existing and forthcoming optical spectroscopy from the DESI, the SGA-2020 will facilitate new detailed studies of the star formation and mass assembly histories of nearby galaxies; enable precise measurements of the local velocity field via the Tully–Fisher and fundamental plane relations; serve as a reference sample of lasting legacy value for time-domain and multimessenger astronomical events; and more.
79 ASTRONOMY AND ASTROPHYSICS↗
Opportunities for isomer studies for astrophysics at FRIB
The role of nuclear isomers in astrophysical nucleosynthesis is gaining increased attention, as reactions on ground and isomeric states are both potentially important for determining the reaction rates and flow within the reaction network. A particular case is the odd-odd N=Z nuclides in the sd -shell, which play an important role in breakout from the CNO cycle in nova nucleosynthesis, affecting reaction flow, the nucleosynthesis end-point, and final abundances, impacting potential astronomical observables. Because many of these nuclides have low-lying isomers, it is important to constrain reactions on both ground and isomeric states. Developments in radioactive-beam facilities are opening such opportunities, via direct and indirect techniques. The first measurement using a new technique for manipulating ground/isomer content in reaccelerated beams without affecting ion optics, has been employed to study 38 K, which will be applicable to measurements on 34 Cl and others at the nascent Facility for Rare Isotope Beams.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Expediting DECam Multimessenger Counterpart Searches with Convolutional Neural Networks
Searches for counterparts to multimessenger events with optical imagers use difference imaging to detect new transient sources. However, even with existing artifact-detection algorithms, this process simultaneously returns several classes of false positives: false detections from poor-quality image subtractions, false detections from low signal-to-noise images, and detections of preexisting variable sources. Currently, human visual inspection to remove the false positives is a central part of multimessenger follow-up observations, but when next generation gravitational wave and neutrino detectors come online and increase the rate of multimessenger events, the visual inspection process will be prohibitively expensive. We approach this problem with two convolutional neural networks operating on the difference imaging outputs. The first network focuses on removing false detections and demonstrates an accuracy of 92% on our data set. The second network focuses on sorting all real detections by the probability of being a transient source within a host galaxy and distinguishes between various classes of images that previously required additional human inspection. We find the number of images requiring human inspection will decrease by a factor of 1.5 using our approach alone and a factor of 3.6 using our approach in combination with existing algorithms, facilitating rapid multimessenger counterpart identification by the astronomical community.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
deeplenstronomy: A dataset simulation package for strong gravitational lensing
Automated searches for strong gravitational lensing in optical imaging survey datasets often employ machine learning and deep learning approaches. These techniques require more example systems to train the algorithms than have presently been discovered, which creates a need for simulated images as training dataset supplements. This work introduces and summarizes deeplenstronomy, an open-source Python package that enables efficient, large-scale, and reproducible simulation of images of astronomical systems. A full suite of unit tests, documentation, and example notebooks are available at https://deepskies.github.io/deeplenstronomy/ .
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Measuring Local Turbulence Along the Optical Path: Multi-Beam Optical Seeing Sensor
Deflection of light along the optical path is a major source of image degradation for ground-based telescopes. Methods have been developed to measure upper atmospheric seeing based on models of the turbulence in the atmosphere, but due to boundary conditions, transmission within telescope enclosures is more complex. The Multi-beam Optical Seeing Sensor (MOSS) directly measures the component of the image quality degradation from inhomogeneity of the index of refraction within the telescope dome. MOSS outputs four near-parallel beams of light that travel along the optical path and are imaged by the telescope’s detector, landing like starlight on the telescope’s focal plane. By using a strobed light source, we can ‘freeze’ the instantaneous index variations transverse to the optical path. This system captures both ‘dome’ and ‘mirror’ seeing. Through plotting the standard deviation of differential motion between pairs of beams, MOSS enables characterization of the length scale of turbulence within the dome. The temporal coherence of temperature gradients can be probed with different pulse lengths, and the spatial coherence by comparing pairs at different separations across the aperture of the telescope. Optical path turbulence measurements, alongside other telemetry metrics, will guide thermal and airflow management to optimize image quality. A MOSS prototype was installed in the 1.2[Formula: see text]m Auxiliary Telescope (AuxTel) at the Vera C. Rubin Observatory in Chile, and preliminary data constrain the optical path turbulence with a lower bound of 1.4 arcsec. The optical path turbulence varied throughout the night of observing.
Astronomical seeing↗
Dr. Sahar Said Allam (1964-2022): A Memoriam
In this short talk, we memorialize the scientific life of AAS member Dr. Sahar Said Allam (1964-2022), an alumna of Cairo University and the National Research Institute of Astronomy \& Geophysics (NRIAG). Her scientific career took her to the Universitaet Potsdam (Germany), New Mexico State University (USA), the Space Telescope Science Institute (USA), and the Fermi National Accelerator Laboratory (Fermilab; USA), as well as to astronomical observatories in New Mexico, Arizona, Hawaii, Chile, and Australia. Among other projects, she worked on the Sloan Digital Sky Survey (SDSS) and the Dark Energy Survey (DES), achieving the coveted “Builders” status on both these projects. She was the discoverer of the (at the time) brightest known Lyman Break Galaxy, the strongly lensed “8 O’Clock Arc”, and played an important role in the discovery of the optical counterpart to the gravitational wave event GW170817. During her final illness, she began work as a Data Preview 0 (“DP0”) De legate for the Vera C. Rubin Legacy Survey of Space & Time (LSST) and was even the Principal Investigator on a successful observing proposal submitted posthumously. The asteroid “135979 Allam” is named after her.
Tucker, Douglas L.↗
Towards quantum telescopes: demonstration of a two-photon interferometer for precision astrometry
Classical optical interferometry requires maintaining live, phase-stable links between telescope stations. This requirement greatly adds to the cost of extending to long baseline separations and limits on baselines will in turn limit the achievable angular resolution. Here we describe a novel type of two-photon interferometer for astrometry, which uses photons from two separate sky sources and does not require an optical link between stations. Such techniques may make large increases in interferometric baselines practical, even by orders of magnitude, with a corresponding improvement in astrometric precision benefiting numerous fields in astrophysics. We tested a benchtop analogue version of the two-source interferometer and unambiguously observe correlated behavior in detections of photon pairs from two thermal light sources, in agreement with theoretical predictions. This work opens new possibilities in future astronomical measurements.
47 OTHER INSTRUMENTATION↗
Probing gravity and growth of structure with gravitational waves and galaxies’ peculiar velocity
The low-redshift velocity field is a unique probe of the growth of cosmic structure and gravity. Here, we propose to use distances from gravitational wave (GW) detections, in conjunction with the redshifts of their host galaxies from wide field spectroscopic surveys (e.g., DESI, 4MOST, TAIPAN), to measure peculiar motions within the local Universe. Such measurement has the potential to constrain the growth rate f σ 8 and test gravity through determination of the gravitational growth index γ , complementing constraints from other peculiar velocity measurements. We find that binary neutron star mergers with associated counterpart at z ≲ 0.2 that will be detected by the Einstein Telescope (ET) will be able to constrain f σ 8 to ~ 3 % precision after 10 years of operations when combined with galaxy overdensities from DESI and TAIPAN. If a larger network of third generation GW detectors is available (e.g., including the Cosmic Explorer), the same constraints can be reached over a shorter timescale ( ~ 5 years for a 3 detectors network). The same events (plus information from their hosts’ redshifts) can constrain γ to σ γ ≲ 0.04 . This constraint is precise enough to discern general relativity from other popular gravity models at 3 σ . This constraint is improved to σ γ ~ 0.02 – 0.03 when combined with galaxy overdensities. The potential of combining galaxies’ peculiar velocities with gravitational wave detections for cosmology highlights the need for extensive optical to near-infrared follow-up of nearby gravitational wave events, or exquisite GW localization, in the next decade.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Generation of realistic input parameters for simulating atmospheric point-spread functions at astronomical observatories
High-fidelity simulated astronomical images are an important tool in developing and measuring the performance of image-processing algorithms, particularly for high precision measurements of cosmic shear – correlated distortions of images of distant galaxies due to weak gravitational lensing caused by the large-scale mass distribution in the Universe. For unbiased measurements of cosmic shear, all other sources of correlated image distortions must be modeled or removed. One such source is the correlated blurring of images due to optical turbulence in the atmosphere, which dominates the point-spread function (PSF) for ground-based instruments. In this work, we leverage data from weather forecasting models to produce wind speeds and directions, and turbulence parameters, that are realistically correlated with altitude. To study the resulting correlations in the size and shape of the PSF, we generate simulated images of the PSF across a ~10 square-degree field of view – the size of the camera focal plane for the Vera C. Rubin Observatory in Chile – using weather data and historical seeing for a geographic location near the Observatory. We make quantitative predictions for two-point correlation functions (2PCF) that are used in analyses of cosmic shear. We observe a strong anisotropy in the two-dimensional 2PCF, which is expected based on observations in real images, and study the dependence of the orientation of the anisotropy on dominant wind directions near the ground and at higher altitudes.
79 ASTRONOMY AND ASTROPHYSICS↗
The Spectroscopic Classification of Astronomical Transients (SCAT) Survey: Overview, Pipeline Description, Initial Results, and Future Plans
Here, we present the Spectroscopic Classification of Astronomical Transients (SCAT) survey, which is dedicated to spectrophotometric observations of transient objects such as supernovae and tidal disruption events. SCAT uses the SuperNova Integral-Field Spectrograph (SNIFS) on the University of Hawai’i 2.2 m (UH2.2m) telescope. SNIFS was designed specifically for accurate transient spectrophotometry, including absolute flux calibration and host-galaxy removal. We describe the data reduction and calibration pipeline including spectral extraction, telluric correction, atmospheric characterization, nightly photometricity, and spectrophotometric precision. We achieve ≲5% spectrophotometry across the full optical wavelength range (3500–9000 Å) under photometric conditions. The inclusion of photometry from the SNIFS multi-filter mosaic imager allows for decent spectrophotometric calibration (10%–20%) even under unfavorable weather/atmospheric conditions. SCAT obtained ≈640 spectra of transients over the first 3 yr of operations, including supernovae of all types, active galactic nuclei, cataclysmic variables, and rare transients such as superluminous supernovae and tidal disruption events. These observations will provide the community with benchmark spectrophotometry to constrain the next generation of hydrodynamic and radiative transfer models.
79 ASTRONOMY AND ASTROPHYSICS↗
Snowmass2021 Cosmic Frontier CF6 White Paper: Multi-Experiment Probes for Dark Energy - Transients
This invited Snowmass 2021 White Paper highlights the power of joint-analysis of astronomical transients in advancing HEP Science and presents research activities that can realize the opportunities that come with current and upcoming projects. Transients of interest include gravitational wave events, neutrino events, strongly-lensed quasars and supernovae, and Type~Ia supernovae specifically. These transients can serve as probes of cosmological distances in the Universe and as cosmic laboratories of extreme strong-gravity, high-energy physics. Joint analysis refers to work that requires significant coordination from multiple experiments or facilities so encompasses Multi-Messenger Astronomy and optical transient discovery and distributed follow-up programs.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗