Asteroids in the High Cadence Transient Survey

Peña, José Martínez; Fuentes, César; Förster, F.; Maureira, J. C.; Martı́n, Jaime San; Littin, Jorge; Huijse, P.; Cabrera-Vives, G.; Estévez, P. A.; Galbany, L.; González–Gaitán, S.; Martı́nez, Jorge; Jaeger, Thomas de; Hamuy, M.

doi:10.3847/1538-3881/aaaaed

Cited by 6 publications

(20 citation statements)

References 24 publications

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“…Since the detections with a probability of being real higher than 0.5 were more sparse than those in the 2014 campaign, linking detections for one night to another proved to be harder than in Peña et al (2018). To solve this, we first found tracklets (sets of at least three detections that assimilate a linear trajectory in one night).…”

Section: Discussionmentioning

confidence: 99%

“…We explored the latitudinal dependence by analyzing the SD of asteroids from the HiTS 2014 campaign (Peña et al 2018). Those asteroids were found mainly between ecliptic latitudes 0 • and 15 • .…”

Section: Summary and Discussionmentioning

confidence: 99%

“…For this, it combined high cadence with a high limiting magnitude and a wide field of view. These characteristics offer the opportunity to do science in various topics other than supernovae (Förster et al 2016 such as RR Lyrae (Medina et al 2017(Medina et al , 2018, SS minor bodies (Peña et al 2018 and this work) and automatic classification of variable sources (Martínez-Palomera et al 2018).…”

Section: Hits Observationsmentioning

confidence: 99%

“…Using the coordinates of known bodies obtained this way, we computed the distance between JPL and HiTS detections, yielding the distribution shown in Figure 2. Is remarkable that using data from JPL we concentrated the difference to a much smaller range, allowing us to easily reduce our criteria for recognizing detections to a 4 distance (in comparison to the 7 in Peña et al 2018).…”

Section: Survey Efficiencymentioning

confidence: 99%

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Asteroids’ Size Distribution and Colors from HITS

Peña

Fuentes

Förster

et al. 2020

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We report the observations of solar system objects during the 2015 campaign of the High cadence Transient Survey (HiTS). We found 5740 bodies (mostly Main Belt asteroids), 1203 of which were detected in different nights and in g and r . Objects were linked in the barycenter system and their orbital parameters were computed assuming Keplerian motion. We identified 6 near Earth objects, 1738 Main Belt asteroids and 4 Trans-Neptunian objects. We did not find a g −r color-size correlation for 14 < H g < 18 (1 < D < 10 km) asteroids. We show asteroids' colors are disturbed by HiTS' 1.6 hour cadence and estimate that observations should be separated by at most 14 minutes to avoid confusion in future wide-field surveys like LSST. The size distribution for the Main Belt objects can be characterized as a simple power law with slope ∼ 0.9 , steeper than in any other survey, while data from HiTS 2014's campaign is consistent with previous ones (slopes ∼ 0.68 at the bright end and ∼ 0.34 at the faint end). This difference is likely due to the ecliptic distribution of the Main Belt since 2015's campaign surveyed farther from the ecliptic than did 2014's and most previous surveys.

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Section: Discussionmentioning

confidence: 99%

Section: Summary and Discussionmentioning

confidence: 99%

Section: Hits Observationsmentioning

confidence: 99%

Section: Survey Efficiencymentioning

confidence: 99%

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Asteroids’ Size Distribution and Colors from HITS

Peña

Fuentes

Förster

et al. 2020

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“…With regards to the role of ML and AI in advancing knowledge in astronomy, there was clear evidence from the sample of recent publications that discovery tasks are being performed with all of the data types: images (Ciuca & Hernández, ; Gomez Gonzalez, Absil, & Van Droogenbroeck, ; Hartley, Flamary, Jackson, Tagore, & Metcalf, ; Jacobs et al, ; Lanusse et al, ; Morello, Morris, Van Dyk, Marston, & Mauerhan, ; Pourrahmani et al, ; Wan et al, ); spectroscopy (Bu, Lei, Zhao, Bu, & Pan, ; Li et al, ); photometry (Ostrovski et al, ; Timlin et al, ; Vida & Roettenbacher, ); light curves (Armstrong et al, ; Cohen et al, ; Giles & Walkowicz, ; Hedges, Hodgkin, & Kennedy, ; Heinze et al, ; Peña et al, ; van Roestel et al, ); time‐series (Connor & van Leeuwen, ; Farah et al, ; Michilli et al, ; Morello et al, ; Pang et al, ; Tan et al, ); catalogues (Lin et al, ; Marchetti et al, ; Nguyen, Pankratius, Eckman, & Seager, ; Yan et al, ); and simulation (Kuntzer & Courbin, ; Nadler et al, ; Xu & Offner, ).…”

Section: Machine Learning and Artificial Intelligence In Astronomymentioning

confidence: 99%

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

Fluke

Jacobs

2019

WIREs Data Min & Knowl

105

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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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Enabling discoveries of Solar System objects in large alert data streams

Le Montagner,

Peloton,

Carry

et al. 2023

A&A

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Context. With the advent of large-scale astronomical surveys, such as the Zwicky Transient Facility (ZTF) and the forthcoming Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST), the number of alerts generated by transient, variable, and moving astronomical objects is growing rapidly, reaching millions of alerts per night. Concerning the minor planets of the Solar System, their identification requires linking the alerts for many observations over a potentially lengthy period of time, leading to a very large combinatorial number. Aims. The goal is to demonstrate how a third-party module dedicated to the identification of new minor planets of the Solar System can be integrated with the Fink alert broker real-time operations, which deals with massive alert data streams produced by large-scale surveys. Methods. Our analysis takes advantage of the scientific surplus brought on by the Fink alert broker classification capabilities to first reduce the 111 275 131 processed alerts from ZTF between November 2019 and December 2022 (755 observation nights) to only 389 530 new Solar System alert candidates over the same period. We implemented a simple, yet pedagogical linking algorithm called Fink-FAT to create trajectory candidates in real time from alert data and extract orbital parameters. The analysis was validated on ZTF alert packets linked to confirmed Solar System objects from the Minor Planet Center (MPC) database. Finally, the candidates were confronted with follow-up observations. Results. Between November 2019 and December 2022, Fink-FAT extracted 327 new orbits from candidate Solar System objects at the time of the observations, of which 65 had still remained unreported in the MPC database as of March 2023. After two late follow-up observation campaigns of six orbit candidates, four were associated with known minor planets of the Solar System, and two still remain unknown. In terms of performance, Fink-FAT took under 3 h to link alerts into trajectory candidates and to extract the orbital elements over the three years of Fink data, using a modest hardware configuration. Conclusions. Despite a much lower efficiency than present linking algorithms, Fink-FAT reaches a high level of purity in reconstructing orbits and it runs fast, making it suitable for the real-time discovery of new minor planets. Fink-FAT is deployed in the Fink broker and analyzes, in real time, the alert data from the ZTF survey by regularly extracting new candidates for Solar System objects. Tests of scalability also show that Fink-FAT is capable of handling the even larger volume of alert data that will be sent by the Rubin Observatory’s real-time difference image analysis processing.

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Asteroids in the High Cadence Transient Survey

Cited by 6 publications

References 24 publications

Asteroids’ Size Distribution and Colors from HITS

Asteroids’ Size Distribution and Colors from HITS

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

Enabling discoveries of Solar System objects in large alert data streams

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