Related papers: Machine Learning Classification of Kuiper Belt Pop…
We have analyzed the first 3.75 years of data from TAOS, the Taiwanese American Occultation Survey. TAOS monitors bright stars to search for occultations by Kuiper Belt Objects (KBOs). This dataset comprises 5e5 star-hours of…
In the first three years of operation the Kepler mission found 3,697 planet candidates from a set of 18,406 transit-like features detected on over 200,000 distinct stars. Vetting candidate signals manually by inspecting light curves and…
We used N-body simulations to model the 4.5 Gyr orbital evolution of the early Kuiper Belt, incorporating a massive protoplanetary disk, the four giant planets, and 1500 primordial Pluto-class bodies ("Plutos") that drove Neptune's grainy…
Visual observation of Cumulus Oocyte Complexes provides only limited information about its functional competence, whereas the molecular evaluations methods are cumbersome or costly. Image analysis of mammalian oocytes can provide attractive…
We introduce a new machine learning based technique to detect exoplanets using the transit method. Machine learning and deep learning techniques have proven to be broadly applicable in various scientific research areas. We aim to exploit…
The occultation of background stellar sources by foreground Kuiper Belt Objects (KBOs) can be used to survey physical properties of the KBO population. We discuss statistics related to a KBO occultation survey, such as the event duration…
We apply machine learning techniques in an attempt to predict and classify stellar properties from noisy and sparse time series data. We preprocessed over 94 GB of Kepler light curves from MAST to classify according to ten distinct physical…
We modeled the 3-D structure of the Kuiper Belt dust cloud at four different dust production rates, incorporating both planet-dust interactions and grain-grain collisions using the collisional grooming algorithm. Simulated images of a model…
High-fidelity measurements are important for the physical implementation of quantum information protocols. Current methods for classifying measurement trajectories in superconducting qubit systems produce fidelities that are systematically…
The low-inclination component of the Classical Kuiper Belt is host to a population of extremely widely-separated binaries. These systems are similar to other Trans-Neptunian binaries (TNBs) in that the primary and secondary components of…
Nearly one-third of objects occupying the most circular, coplanar Kuiper belt orbits (the cold classical belt) are binary, and several percent of them are "ultra-wide" binaries (UWBs): 100-km-sized companions spaced by tens of thousands of…
The orbits of trans-Neptunian objects (TNOs) can indicate the existence of an undiscovered planet in the outer solar system. Here, we used N-body computer simulations to investigate the effects of a hypothetical Kuiper Belt planet (KBP) on…
Machine learning is a powerful technique, becoming increasingly popular in astrophysics. In this paper, we apply machine learning to more than a thousand globular cluster (GC) models simulated as part of the 'MOCCA-Survey Database I'…
We analyze a detailed Nice model simulation of Kuiper Belt emplacement from Brasser & Morbidelli (2013), where Neptune undergoes a high eccentricity phase and migrates outward. In this work, which follows from Pike et al. (2017), we…
A number of authors have proposed that the statistically significant orbital alignment of the most distant Kuiper Belt Objects (KBOs) is evidence of an as-yet undetected planet in the outer solar system, now referred to colloquially a…
The KiDS Strongly lensed QUAsar Detection project (KiDS-SQuaD) aims at finding as many previously undiscovered gravitational lensed quasars as possible in the Kilo Degree Survey. This is the second paper of this series where we present a…
Nowadays, Machine Learning techniques offer fast and efficient solutions for classification problems that would require intensive computational resources via traditional methods. We examine the use of a supervised Random Forest to classify…
With the rapid increase in the number of Anthropogenic Space Objects (ASOs), Low Earth Orbit (LEO) is facing significant congestion, thereby posing challenges to space operators and risking the viability of the space environment for varied…
Within scientific and real life problems, classification is a typical case of extremely complex tasks in data-driven scenarios, especially if approached with traditional techniques. Machine Learning supervised and unsupervised paradigms,…
Long-term integrations of asteroid orbits with high-accuracy numerical integrators are essential for understanding dynamical evolution and ejection from the Solar System, but are computationally expensive. Here, we investigate the dynamical…