Characterization of Near-Earth Asteroids Using KMTNET-SAAO

Erasmus, N.; Mommert, Michael; Trilling, David E.; Sickafoose, Amanda A.; Gend, C. van; Hora, J. L.

doi:10.3847/1538-3881/aa88be

Cited by 22 publications

(17 citation statements)

References 24 publications

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“…Together, these two asteroid types represent ∼ 90% of our sample, with the rest likely to be Q-and X-type asteroids. This compositional fraction is in agreement with the results of our previous publications (Mommert et al 2016;Erasmus et al 2017) which are based on UKIRT and KMTNet-SAAO observations.…”

Section: Conclusion and Future Outlooksupporting

confidence: 93%

“…Perna et al (2018), for a sample of 146 objects, found an S fraction of ∼ 40%. Our team, using different telescopes and samples than the one presented in this paper, found ∼ 40% with 40 NEOs in Mommert et al (2016), while Erasmus et al (2017), with a sample of 45 objects, obtained an S fraction of ∼ 43%. Using a sample of 252 objects Stuart & Binzel Figure 8.…”

Section: Limitations and Comparison With Previous Studiesmentioning

confidence: 47%

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First Results from the Rapid-response Spectrophotometric Characterization of Near-Earth Objects Using RATIR

Navarro-Meza

Mommert

Trilling

et al. 2019

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As part of our multi-observatory, multi-filter campaign, we present r-i color observations of 82 Near-Earth Objects (NEOs) obtained with the RATIR instrument on the 1.5 m robotic telescope at the San Pedro Martir's National Observatory in Mexico. Our project is particularly focused on rapid response observations of small ( 850 m) NEOs.The rapid response and the use of spectrophotometry allows us to constrain the taxonomic classification of NEOs with high efficiency. Here we present the methodology of our observations and our result, suggesting that the ratio of C-type to S-type asteroids 2 in a size range of ∼30-850m is 1.1, which is in accordance with our previous results.We also find that 10% of all NEOs in our sample are neither C-nor S-type asteroids

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Section: Conclusion and Future Outlooksupporting

confidence: 93%

Section: Limitations and Comparison With Previous Studiesmentioning

confidence: 47%

First Results from the Rapid-response Spectrophotometric Characterization of Near-Earth Objects Using RATIR

Navarro-Meza

Mommert

Trilling

et al. 2019

Self Cite

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“…Krugly et al 2002;Chang et al 2015), spectrophotometry (e.g. Mommert et al 2016;Erasmus et al 2017;Navarro-Meza et al 2019), spectroscopy (e.g Binzel et al 2004Binzel et al , 2019, radar techniques (e.g. Ostro et al 2006), andpolarimetry (e.g.…”

Section: Introductionmentioning

confidence: 99%

Visible Spectroscopy from the Mission Accessible Near-Earth Object Survey (MANOS): Taxonomic Dependence on Asteroid Size

Devogèle

Moskovitz

Thirouin

et al. 2019

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The Mission Accessible Near-Earth Object Survey (MANOS) aims to observe and characterize small (mean absolute magnitude H ∼ 25 mag) Near-Earth Objects (NEOs) that are accessible by spacecraft (mean ∆v ∼ 5.7 km/s) and that make close approaches with the Earth (mean Minimum Orbital Intersection Distance MOID ∼ 0.03 AU). We present here the first results of the MANOS visible spectroscopic survey. The spectra were obtained from August 2013 to March 2018 at Lowell Observatory's Discovery Channel 4.3 meter telescope, and both Gemini North and South facilities. In total, 210 NEOs have been observed and taxonomically classified. Our taxonomic distribution shows significant variations with respect to surveys of larger objects. We suspect these to be due to a dependence of Main Belt source regions on object size. Compared to previous surveys of larger objects (Binzel et al. 2019Perna et al. 2018), we report a lower fraction of S+Q-complex asteroids of 43.8 ± 4.6%. We associate this decrease with a lack of Phocaea family members at very small size. We also report higher fractions of X-complex and A-type asteroids of 23.8 ± 3.3% and 3.8 ± 1.3% respectively due to an increase of Hungaria family objects at small size. We find a strong correlation between the Q/S ratio and perihelion distance. We suggest this correlation is due to planetary close encounters with Venus playing a major role in turning asteroids from S to Q-type. This hypothesis is supported by a similar correlation between the Q/S ratio and Venus MOID.

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“…Due to their relative proximity to the Earth, the motion of Solar System objects is key to their discovery. ML and AI have enabled removal, or reduction, of false detections from the moving object detection pipeline in the Subaru/Hyper‐Suprime‐Cam Strategic Survey Program (Lin et al, ), with applications to Trans‐Neptunian Objects (Chen et al, ); and detection and classification of asteroids (Erasmus et al, , ; Smirnov & Markov, ). Duev et al () trained a CNN to discover fast‐moving candidates from ZTF observations in order to more reliably identify potentially hazardous near‐Earth objects. Active galactic nuclei and quasars .…”

Section: Assessing the Maturity Of Adoptionmentioning

confidence: 99%

Surveying the reach and maturity of machine learning and artificial intelligence in astronomy

Fluke

Jacobs

2019

WIREs Data Min & Knowl

113

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Machine learning (automated processes that learn by example in order to classify, predict, discover, or generate new data) and artificial intelligence (methods by which a computer makes decisions or discoveries that would usually require human intelligence) are now firmly established in astronomy. Every week, new applications of machine learning and artificial intelligence are added to a growing corpus of work. Random forests, support vector machines, and neural networks are now having a genuine impact for applications as diverse as discovering extrasolar planets, transient objects, quasars, and gravitationally lensed systems, forecasting solar activity, and distinguishing between signals and instrumental effects in gravitational wave astronomy. This review surveys contemporary, published literature on machine learning and artificial intelligence in astronomy and astrophysics. Applications span seven main categories of activity: classification, regression, clustering, forecasting, generation, discovery, and the development of new scientific insights. These categories form the basis of a hierarchy of maturity, as the use of machine learning and artificial intelligence emerges, progresses, or becomes established. This article is categorized under: Application Areas > Science and Technology Fundamental Concepts of Data and Knowledge > Motivation and Emergence of Data Mining Technologies > Machine Learning

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Characterization of Near-Earth Asteroids Using KMTNET-SAAO

Cited by 22 publications

References 24 publications

First Results from the Rapid-response Spectrophotometric Characterization of Near-Earth Objects Using RATIR

First Results from the Rapid-response Spectrophotometric Characterization of Near-Earth Objects Using RATIR

Visible Spectroscopy from the Mission Accessible Near-Earth Object Survey (MANOS): Taxonomic Dependence on Asteroid Size

Surveying the reach and maturity of machine learning and artificial intelligence in astronomy

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