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Jonathan H. Jiang

Publications and source records attributed to Jonathan H. Jiang.

29 records · Page 2Linked to original sources
arXiv2021

Angular momentum distributions for observed and modeled exoplanetary systems

The distribution of angular momentum of planets and their host stars provides important information on the formation and evolution of the planetary system. However, mysteries still remain, partly due to bias and uncertainty of the current observational datasets and partly due to the fact that theoretical models for the formation and evolution of planetary systems are still underdeveloped. In this study, we calculate the spin angular momenta of host stars and the orbital angular momenta of their planets using data from the NASA Exoplanet Archive, together with detailed analysis of observation dependent biases and uncertainty ranges. We also analyze the angular momenta of the planetary system as a function of star age to understand their variation in different evolutionary stages. In addition, we use a population of planets from theoretical model simulations to reexamine the observed patterns and compare the simulated population with the observed samples to assess variations and differences. We found the majority of exoplanets discovered thus far do not have the angular momentum distribution similar to the planets in our Solar System, though this could be due to the observation bias. When filtered by the observational biases, the model simulated angular momentum distributions are comparable to the observed pattern in general. However, the differences between the observation and model simulation in the parameter (angular momentum) space provide more rigorous constraints and insights on the issues that needed future improvement.

astro-ph.EP↗
arXiv2021

Avoiding the "Great Filter": A Projected Timeframe for Human Expansion Off-World

A foundational model has been developed based on trends built from empirical data of space exploration and computing power through the first six plus decades of the Space Age which projects earliest possible launch dates for human-crewed missions from cis-lunar space to selected Solar System and interstellar destinations. The model uses computational power, expressed as transistors per microprocessor, as a key broadly limiting factor for deep space missions' reach and complexity. The goal of this analysis is to provide a projected timeframe for humanity to become a multi-world species through off-world colonization, and in so doing all but guarantees the long-term survival of the human race from natural and human-caused calamities that could befall life on Earth. Be-ginning with the development and deployment of the first nuclear weapons near the end of World War II, humanity entered a 'Window of Peril' which will not be safely closed until robust off-world colonies become a reality. Our findings suggest the first human-crewed missions to land on Mars, selected Asteroid Belt objects, and selected moons of Jupiter and Saturn can occur before the end of the 21st century. Launches of human-crewed interstellar missions to exoplanet destinations within roughly 40 lightyears of the Solar System are seen as possible during the 23rd century and launch of intragalactic missions by the end of the 24th century. An aggressive and sustained space exploration program, which includes colonization, is thus seen as critical to the long-term survival of the human race.

physics.pop-ph↗
arXiv2021

Analyzing the Stability of Non-coplanar Circumbinary Planets using Machine Learning

Exoplanet detection in the past decade by efforts including NASA's Kepler and TESS missions has discovered many worlds that differ substantially from planets in our own Solar system, including more than 400 exoplanets orbiting binary or multi-star systems. This not only broadens our understanding of the diversity of exoplanets, but also promotes our study of exoplanets in the complex binary and multi-star systems and provides motivation to explore their habitability. In this study, we analyze orbital stability of exoplanets in non-coplanar circumbinary systems using a numerical simulation method, with which a large number of circumbinary planet samples are generated in order to quantify the effects of various orbital parameters on orbital stability. We also train a machine learning model that can quickly determine the stability of the circumbinary planetary systems. Our results indicate that larger inclinations of the planet tend to increase the stability of its orbit, but change in the planet's mass range between Earth and Jupiter has little effect on the stability of the system. In addition, we find that Deep Neural Networks (DNNs) have higher accuracy and precision than other machine learning algorithms.

astro-ph.EP↗
arXiv2021

Revisiting the Planet Mass and Stellar Metallicity Relation for Low-Mass Exoplanets Orbiting GKM Class Stars

The growing database of exoplanets have shown us the statistical characteristics of various exoplanet populations, providing insight towards their origins. Observational evidence suggests that the process by which gas giants are conceived in the stellar disk may be disparate from that of smaller planets. Using NASA's Exoplanet Archive, we analyzed a correlation between the planet mass and stellar metallicity of low-mass exoplanets (MP < 0.13 MJ) orbiting spectral class G, K, and M stars. The correlation suggests an exponential law relationship between the two that is not fully explained by observation biases alone.

astro-ph.EP↗
arXiv2020

Earth as a Proxy Exoplanet: Deconstructing and Reconstructing Spectrophotometric Light Curves

Point source spectrophotometric ("single-point") light curves of Earth-like planets contain a surprising amount of information about the spatial features of those worlds. Spatially resolving these light curves is important for assessing time-varying surface features and the existence of an atmosphere, which in turn is critical to life on Earth and significant for determining habitability on exoplanets. Given that Earth is the only celestial body confirmed to harbor life, treating it as a proxy exoplanet by analyzing time-resolved spectral images provides a benchmark in the search for habitable exoplanets. The Earth Polychromatic Imaging Camera (EPIC) on the Deep Space Climate Observatory (DSCOVR) provides such an opportunity, with observations of ~5000 full-disk sunlit Earth images each year at ten wavelengths with high temporal frequency. We disk-integrate these spectral images to create single-point light curves and decompose them into principal components (PCs). Using machine learning techniques to relate the PCs to six preselected spatial features, we find that the first and fourth PCs of the single-point light curves, contributing ~83.23% of the light curve variability, contain information about low and high clouds, respectively. Surface information relevant to the contrast between land and ocean reflectance is contained in the second PC, while individual land sub-types are not easily distinguishable (<0.1% total light curve variation). We build an Earth model by systematically altering the spatial features to derive causal relationships to the PCs. This model can serve as a baseline for analyzing Earth-like exoplanets and guide wavelength selection and sampling strategies for future observations.

astro-ph.EP↗
arXiv2020

A Statistical Estimation of the Occurrence of Extraterrestrial Intelligence in the Milky Way Galaxy

In the field of Astrobiology, the precise location, prevalence and age of potential extraterrestrial intelligence (ETI) have not been explicitly explored. Here, we address these inquiries using an empirical galactic simulation model to analyze the spatial-temporal variations and the prevalence of potential ETI within the Galaxy. This model estimates the occurrence of ETI, providing guidance on where to look for intelligent life in the Search for ETI (SETI) with a set of criteria, including well-established astrophysical properties of the Milky Way. Further, typically overlooked factors such as the process of abiogenesis, different evolutionary timescales and potential self-annihilation are incorporated to explore the growth propensity of ETI. We examine three major parameters: 1) the likelihood rate of abiogenesis (λA); 2) evolutionary timescales (Tevo); and 3) probability of self-annihilation of complex life (Pann). We found Pann to be the most influential parameter determining the quantity and age of galactic intelligent life. Our model simulation also identified a peak location for ETI at an annular region approximately 4 kpc from the Galactic center around 8 billion years (Gyrs), with complex life decreasing temporally and spatially from the peak point, asserting a high likelihood of intelligent life in the galactic inner disk. The simulated age distributions also suggest that most of the intelligent life in our galaxy are young, thus making observation or detection difficult.

astro-ph.GA↗
arXiv2019

Earth as an Exoplanet: A Two-dimensional Alien Map

Resolving spatially-varying exoplanet features from single-point light curves is essential for determining whether Earth-like worlds harbor geological features and/or climate systems that influence habitability. To evaluate the feasibility and requirements of this spatial feature resolving problem, we present an analysis of multi-wavelength single-point light curves of Earth, where it plays the role of a proxy exoplanet. Here, ~10,000 DSCOVR/EPIC frames collected over a two-year period were integrated over the Earth's disk to yield a spectrally-dependent point source and analyzed using singular value decomposition. We found that, between the two dominant principal components (PCs), the second PC contains surface-related features of the planet, while the first PC mainly includes cloud information. We present the first two-dimensional (2D) surface map of Earth reconstructed from light curve observations without any assumptions of its spectral properties. This study serves as a baseline for reconstructing the surface features of Earth-like exoplanets in the future.

astro-ph.EP↗
arXiv2019

Empirical Predictions for the Period Distribution of Planets to be Discovered by the Transiting Exoplanet Survey Satellite

Launched on April 2018, NASA's Transiting Exoplanet Survey Satellite (TESS) has been performing a wide-field survey for exoplanets orbiting stars with a goal of producing a rich database for follow-on studies. Here we present estimates of the detected exoplanet orbital periods in the 2-minute cadence mode during the TESS mission. For a two-transit detection criterion, the expected mean value of the most frequently detected orbital period is 5.01 days with the most frequently detected range of 2.12 to 11.82 days in the region with observation of 27 days. Near the poles where the observational duration is 351 days, the expected mean orbital period is 10.93 days with the most frequently detected range being from 3.35 to 35.65 days. For one-transit, the most frequently detected orbital period is 8.17 days in the region with observation of 27 days and 11.25 days near the poles. For the entire TESS mission containing several sectors, we estimate that the mean value of orbital period is 8.47 days for two-transit and 10.09 days for one-transit, respectively. If TESS yields a planet population substantially different from what's predicted here, the underlying planet occurrence rates are likely different between the stellar sample probed by TESS and that by Kepler.

astro-ph.EP↗
arXiv2018

Using Deep Space Climate Observatory Measurements to Study the Earth as An Exoplanet

Even though it was not designed as an exoplanetary research mission, the Deep Space Climate Observatory (DSCOVR) has been opportunistically used for a novel experiment, in which Earth serves as a proxy exoplanet. More than two years of DSCOVR Earth images were employed to produce time series of multi-wavelength, single-point light sources, in order to extract information on planetary rotation, cloud patterns, surface type, and orbit around the Sun. In what follows, we assume that these properties of the Earth are unknown, and instead attempt to derive them from first principles. These conclusions are then compared with known data about our planet. We also used the DSCOVR data to simulate phase angle changes, as well as the minimum data collection rate needed to determine the rotation period of an exoplanet. This innovative method of using the time evolution of a multi-wavelength, reflected single-point light source, can be deployed for retrieving a range of intrinsic properties of an exoplanet around a distant star.

astro-ph.EP↗
arXiv2014

Dependency of U.S. Hurricane Economic Loss on Maximum Wind Speed and Storm Size

Many empirical hurricane economic loss models consider only wind speed and neglect storm size. These models may be inadequate in accurately predicting the losses of super-sized storms, such as Hurricane Sandy in 2012. In this study, we examined the dependencies of normalized U.S. hurricane loss on both wind speed and storm size for 73 tropical cyclones that made landfall in the U.S. from 1988 to 2012. A multi-variate least squares regression is used to construct a hurricane loss model using both wind speed and size as predictors. Using maximum wind speed and size together captures more variance of losses than using wind speed or size alone. It is found that normalized hurricane loss (L) approximately follows a power law relation with maximum wind speed (Vmax) and size (R). Assuming L=10^c Vmax^a R^b, c being a scaling factor, the coefficients, a and b, generally range between 4-12 and 2-4, respectively. Both a and b tend to increase with stronger wind speed. For large losses, a weighted regression model, with a being 4.28 and b being 2.52, produces a reasonable fitting to the actual losses. Hurricane Sandy's size was about 3.4 times of the average size of the 73 storms analyzed. Based on the weighted regression model, Hurricane Sandy's loss would be 21 times smaller if its size were of the average size with maximum wind speed unchanged. It is important to revise conventional empirical hurricane loss models to include both maximum wind speed and size as predictors.

physics.ao-ph↗