Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userExploring the Solar System with the NOIRLab Source Catalog I: Detecting Objects with CANFind
119
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
Despite extensive searches and the relative proximity of solar system objects (SSOS) to Earth, many remain undiscovered and there is still much to learn about their properties and interactions. This work is the first in a series dedicated to detecting and analyzing SSOs in the all-sky NOIRLab Source Catalog (NSC). We search the first data release of the NSC with CANFind, a Computationally Automated NSC tracklet Finder. NSC DR1 contains 34 billion measurements of 2.9 billion unique objects, which CANFind categorizes as belonging to stationary (distant stars, galaxies) or moving (SSOs) objects via an iterative clustering method. Detections of stationary bodies for proper motion (mu) less than 2.5/hr (0.017 degrees/day) are identified and analyzed separately. Remaining detections belonging to hi-mu objects are clustered together over single nights to form tracklets. Each tracklet contains detections of an individual moving object, and is validated based on spatial linearity and motion through time. Proper motions are then calculated and used to connect tracklets and other unassociated measurements over multiple nights by predicting their locations at common times forming tracks. This method extracted 527,055 tracklets from NSC DR1 in an area covering 29,971 square degrees of the sky. The data show distinct groups of objects with similar observed mu in ecliptic coordinates, namely Main Belt Asteroids, Jupiter Trojans, and Kuiper Belt Objects. Apparent magnitudes range from 10-25 mag in the ugrizY and VR bands. Color-color diagrams show a bimodality of tracklets between primarily carbonaceous and siliceous groups, supporting prior studies.
rate research
Read More
In the preparation for ESAs Euclid mission and the large amount of data it will produce, we train deep convolutional neural networks on Euclid simulations classify solar system objects from other astronomical sources. Using transfer learning we are able to achieve a good performance despite our tiny dataset with as few as 7512 images. Our best model correctly identifies objects with a top accuracy of 94% and improves to 96% when Euclids dither information is included. The neural network misses ~50% of the slowest moving asteroids (v < 10 arcsec/h) but is otherwise able to correctly classify asteroids even down to 26 mag. We show that the same model also performs well at classifying stars, galaxies and cosmic rays, and could potentially be applied to distinguish all types of objects in the Euclid data and other large optical surveys.
We present the discovery of a low-mass comoving system found by means of the NOIRLab Source Catalog (NSC) DR2. The system consists of the high proper-motion star LEHPM 5005 and an ultracool companion 2MASS J22410186-4500298 with an estimated spectral type of L2. The primary (LEHPM 5005) is likely a mid-M dwarf but over-luminous for its color, indicating a possible close equal mass binary. According to the Gaia EDR3 parallax of the primary, the system is located at a distance of $58pm2$ pc. We calculated an angular separation of 7.2 between both components, resulting in a projected physical separation of 418 AU.
The search for minor bodies in the solar system promises insights into its formation history. Wide imaging surveys offer the opportunity to serendipitously discover and identify these traces of planetary formation and evolution. We aim to present a method to acquire position, photometry, and proper motion measurements of solar system objects in surveys using dithered image sequences. The application of this method on the Kilo-Degree Survey is demonstrated. Optical images of 346 square degree fields of the sky are searched in up to four filters using the AstrOmatic software suite to reduce the pixel to catalog data. The solar system objects within the acquired sources are selected based on a set of criteria depending on their number of observation, motion, and size. The Virtual Observatory SkyBoT tool is used to identify known objects. We observed 20,221 SSO candidates, with an estimated false-positive content of less than 0.05%. Of these SSO candidates, 53.4% are identified by SkyBoT. KiDS can detect previously unknown SSOs because of its depth and coverage at high ecliptic latitude, including parts of the Southern Hemisphere. Thus we expect the large fraction of the 46.6% of unidentified objects to be truly new SSOs. Our method is applicable to a variety of dithered surveys such as DES, LSST, and Euclid. It offers a quick and easy-to-implement search for solar system objects. SkyBoT can then be used to estimate the completeness of the recovered sample.
We discuss the in-flight autonomous assembly as the means to build advanced planetary science payloads to explore the outer regions of the solar system. These payloads are robotically constructed from modular parts delivered by a group of smallsats (< 20 kg) which are placed on fast solar system transfer trajectories while being accelerated by solar sail propulsion to velocities of ~10 AU/yr. This concept provides the planetary science community with inexpensive, frequent access to distant regions of the solar system with flexible, reconfigurable instruments and systems that are assembled in flight. It permits faster revisit times, rapid replenishment and technology insertions, longer mission capability with lower costs. It also increases the science capabilities of smallsats via the use of modular, redundant architectures and allows for proliferation of sensing instrumentation throughout the solar system.
We announce the second data release (DR2) of the NOIRLab Source Catalog (NSC), using 412,116 public images from CTIO-4m+DECam, the KPNO-4m+Mosaic3 and the Bok-2.3m+90Prime. NSC DR2 contains over 3.9 billion unique objects, 68 billion individual source measurements, covers $approx$35,000 square degrees of the sky, has depths of $approx$23rd magnitude in most broadband filters with $approx$1-2% photometric precision, and astrometric accuracy of $approx$7 mas. Approximately 1.9 billion objects within $approx$30,000 square degrees of sky have photometry in three or more bands. There are several improvements over NSC DR1. DR2 includes 156,662 (61%) more exposures extending over 2 more years than in DR1. The southern photometric zeropoints in $griz$ are more accurate by using the Skymapper DR1 and ATLAS-Ref2 catalogs, and improved extinction corrections were used for high-extinction regions. In addition, the astrometric accuracy is improved by taking advantage of Gaia DR2 proper motions when calibrating the WCS of individual images. This improves the NSC proper motions to $sim$2.5 mas/yr (precision) and $sim$0.2 mas/yr (accuracy). The combination of sources into unique objects is performed using a DBSCAN algorithm and mean parameters per object (such as mean magnitudes, proper motion, etc.) are calculated more robustly with outlier rejection. Finally, eight multi-band photometric variability indices are calculated for each object and variable objects are flagged (23 million objects). NSC DR2 will be useful for exploring solar system objects, stellar streams, dwarf satellite galaxies, QSOs, variable stars, high-proper motion stars, and transients. Several examples of these science use cases are presented. The NSC DR2 catalog is publicly available via the NOIRLabs Astro Data Lab science platform.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
