The Near-Earth Object Surveyor Mission

Mainzer, A. K.; Masiero, J. R.; Abell, Paul A.; Bauer, J. M.; Bottke, William; Buratti, Bonnie J.; Carey, Sean J.; Cotto-Figueroa, D.; Cutri, R. M.; Dahlen, D.; Eisenhardt, Peter R. M.; Fernandez, Y. R.; Furfaro, Roberto; Grav, Tommy; Hoffman, T. L.; Kelley, Michael S.; Kim, Yoonyoung; Kirkpatrick, J. Davy; Lawler, Christopher R.; Lilly, Eva; Liu, X.; Marocco, Federico; Marsh, K. A.; Masci, Frank J.; McMurtry, Craig W.; Pourrahmani, Milad; Reinhart, Lennon; Ressler, Michael E.; Satpathy, Akash; Schambeau, C. A.; Sonnett, S.; Spahr, Timothy B.; Surace, Jason A.; Vaquero, Mar; Wright, E. L.; Zengilowski, Gregory R.; ,

doi:10.3847/psj/ad0468

Cited by 9 publications

(14 citation statements)

References 90 publications

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“…We use the NEO Surveyor Survey Simulator (NSS) software tools (Mainzer et al 2023) to propagate our synthetic orbits over the 5 yr nominal mission survey lifetime and calculate their expected fluxes with respect to the instrument sensitivity. The NSS then determines if the objects are detectable a sufficient number of times to report tracklets (sequences of position-time measurements) to the MPC that would all for the orbit to be constrained.…”

Section: Survey Simulation Resultsmentioning

confidence: 99%

“…State vector propagation is carried out using an N-body simulator; instrument sensitivity uses the current best estimate for the hardware that will be implemented for the mission; tracklet generation efficiency is based on ongoing tests of the NEO Surveyor Science Data System at IPAC; estimations for the capability of the MPC to link tracklets into measurable orbits are based on historic performance from the NEOWISE mission as well as ongoing testing between NEO Surveyor and the MPC. Mainzer et al (2023) provide the specific values used for these parameters in the NSS and the method of their derivation. The output of the NSS is a list of observations of each input object that passes detection and tracklet-building requirements; this is used to determine overall completeness as a function of both size and orbit, described in detail below.…”

Section: Survey Simulation Resultsmentioning

confidence: 99%

“…Our work makes use of the recently developed survey simulation tools for the NEO Surveyor mission (Mainzer et al 2023). These tools have been demonstrated by Masiero et al (2023) to successfully reproduce predicted observations of near-Earth asteroids through comparison with data from the NEOWISE mission (Mainzer et al 2011(Mainzer et al , 2014(Mainzer et al , 2019.…”

Section: Population Modelmentioning

confidence: 99%

“…The NEO Surveyor mission (Mainzer et al 2023) will conduct a census of asteroids and comets near the Earth's orbit, in order to determine the risk posed to our planet from any potential impactors. The mission makes use of a single instrument consisting of a two-channel thermal infrared camera, and will conduct a dedicated survey optimized for the detection of NEOs.…”

Section: Introductionmentioning

confidence: 99%

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The Sensitivity of NEO Surveyor to Low-perihelion Asteroids

Masiero,

Kwon,

Dahlen

et al. 2024

Planet. Sci. J.

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Asteroids with low orbital perihelion distances experience extreme heating from the Sun that can modify their surfaces and trigger nontypical activity mechanisms. These objects are generally difficult to observe from ground-based telescopes due to their frequent proximity to the Sun. The Near-Earth Object (NEO) Surveyor mission, however, will regularly survey down to solar elongations of 45° and is well suited for the detection and characterization of low-perihelion asteroids. Here, we use the survey simulation software tools developed for mission verification to explore the expected sensitivity of NEO Surveyor to these objects. We find that NEO Surveyor is expected to be >90% complete for near-Sun objects larger than D ∼ 300 m. Additionally, if the asteroid (3200) Phaethon underwent a disruption event in the past to form the Geminid meteor stream, Surveyor will be >90% complete to any fragments larger than D ∼ 200 m. For probable disruption models, NEO Surveyor would be expected to detect dozens of objects on Phaethon-like orbits, compared to a predicted background population of only a handful of asteroids, setting strong constraints on the likelihood of this scenario.

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Section: Survey Simulation Resultsmentioning

confidence: 99%

Section: Survey Simulation Resultsmentioning

confidence: 99%

Section: Population Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Sensitivity of NEO Surveyor to Low-perihelion Asteroids

Masiero,

Kwon,

Dahlen

et al. 2024

Planet. Sci. J.

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“…The incorporation of additional ML algorithms may lead to improved performance on a broader variety of classes and numbers of positive examples. Finally, upcoming sources of new data will support future time-domain studies: the Legacy Survey of Space and Time at Rubin Observatory (Ivezić 2019) will provide time-series data for nearly an order of magnitude more sources than ZTF, and the NEO Surveyor Mission (Mainzer et al 2023) will similarly succeed the Wide-field Infrared Survey Explorer in the mid-IR. The SCoPe code is readily adaptable to data with different cadences and bands, and we look forward to the continued contributions this project can make to time-domain astronomy.…”

Section: Discussionmentioning

confidence: 99%

The ZTF Source Classification Project. III. A Catalog of Variable Sources

Healy,

Coughlin,

Mahabal

et al. 2024

ApJS

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The classification of variable objects provides insight into a wide variety of astrophysics ranging from stellar interiors to galactic nuclei. The Zwicky Transient Facility (ZTF) provides time-series observations that record the variability of more than a billion sources. The scale of these data necessitates automated approaches to make a thorough analysis. Building on previous work, this paper reports the results of the ZTF Source Classification Project (SCoPe), which trains neural network and XGBoost (XGB) machine-learning (ML) algorithms to perform dichotomous classification of variable ZTF sources using a manually constructed training set containing 170,632 light curves. We find that several classifiers achieve high precision and recall scores, suggesting the reliability of their predictions for 209,991,147 light curves across 77 ZTF fields. We also identify the most important features for XGB classification and compare the performance of the two ML algorithms, finding a pattern of higher precision among XGB classifiers. The resulting classification catalog is available to the public, and the software developed for SCoPe is open source and adaptable to future time-domain surveys.

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