Modeling protoplanetary disk SEDs with artificial neural networks

We model the spectral energy distributions (SEDs) of 23 protoplanetary disks in the Taurus-Auriga star-forming region using detailed disk models and a Bayesian approach. This is made possible by combining these models with artificial neural networks to drastically speed up their performance. Such a setup allows us to confront α-disk models with observations while accounting for several uncertainties and degeneracies. Our results yield high viscosities and accretion rates for many sources, which is not consistent with recent measurements of low turbulence levels in disks. This inconsistency could imply that viscosity is not the main mechanism for angular momentum transport in disks, and that alternatives such as disk winds play an important role in this process. We also find that our SED-derived disk masses are systematically higher than those obtained solely from (sub)mm fluxes, suggesting that part of the disk emission could still be optically thick at (sub)mm wavelengths. This effect is particularly relevant for disk population studies and alleviates previous observational tensions between the masses of protoplanetary disks and exoplanetary systems.

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Why do protoplanetary disks appear not massive enough to form the known exoplanet population?

Astronomy and Astrophysics ◽

10.1051/0004-6361/201834076 ◽

2018 ◽

Vol 618 ◽

pp. L3 ◽

Cited By ~ 62

Author(s):

C. F. Manara ◽

A. Morbidelli ◽

T. Guillot

Keyword(s):

Protoplanetary Disks ◽

Protoplanetary Disk ◽

Giant Planets ◽

Dust Particles ◽

Low Mass Stars ◽

Star Forming ◽

Star Forming Regions ◽

Exoplanetary Systems ◽

Low Mass ◽

The Masses

When and how planets form in protoplanetary disks is still a topic of discussion. Exoplanet detection surveys and protoplanetary disk surveys are now providing results that are leading to new insights. We collect the masses of confirmed exoplanets and compare their dependence on stellar mass with the same dependence for protoplanetary disk masses measured in ∼1–3 Myr old star-forming regions. We recalculated the disk masses using the new estimates of their distances derived from Gaia DR2 parallaxes. We note that single and multiple exoplanetary systems form two different populations, probably pointing to a different formation mechanism for massive giant planets around very low-mass stars. While expecting that the mass in exoplanetary systems is much lower than the measured disk masses, we instead find that exoplanetary systems masses are comparable or higher than the most massive disks. This same result is found by converting the measured planet masses into heavy element content (core masses for the giant planets and full masses for the super-Earth systems) and by comparing this value with the disk dust masses. Unless disk dust masses are heavily underestimated, this is a big conundrum. An extremely efficient recycling of dust particles in the disk cannot solve this conundrum. This implies that either the cores of planets have formed very rapidly (<0.1–1 Myr) and a large amount of gas is expelled on the same timescales from the disk, or that disks are continuously replenished by fresh planet-forming material from the environment. These hypotheses can be tested by measuring disk masses in even younger targets and by better understanding if and how the disks are replenished by their surroundings.

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Identification of efficient COVID-19 diagnostic test through artificial neural networks approach − substantiated by modeling and simulation

Journal of Intelligent Systems ◽

10.1515/jisys-2021-0041 ◽

2021 ◽

Vol 30 (1) ◽

pp. 836-854

Author(s):

Mustafa Kamal Pasha ◽

Syed Fasih Ali Gardazi ◽

Fariha Imtiaz ◽

Asma Talib Qureshi ◽

Rabia Afrasiab

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Cost Effective ◽

Limiting Factor ◽

Probabilistic Algorithms ◽

Global Pandemic ◽

Sample Pooling ◽

Test Specificity ◽

Artificial Neural ◽

The Masses

Abstract Soon after the first COVID-19 positive case was detected in Wuhan, China, the virus spread around the globe, and in no time, it was declared as a global pandemic by the WHO. Testing, which is the first step in identifying and diagnosing COVID-19, became the first need of the masses. Therefore, testing kits for COVID-19 were manufactured for efficiently detecting COVID-19. However, due to limited resources in the densely populated countries, testing capacity even after a year is still a limiting factor for COVID-19 diagnosis on a larger scale and contributes to a lag in disease tracking and containment. Due to this reason, we started this study to provide a better cost-effective solution for enhancing the testing capacity so that the maximum number of people could get tested for COVID-19. For this purpose, we utilized the approach of artificial neural networks (ANN) to acquire the relevant data on COVID-19 and its testing. The data were analyzed by using Machine Learning, and probabilistic algorithms were applied to obtain a statistically proven solution for COVID-19 testing. The results obtained through ANN indicated that sample pooling is not only an effective way but also regarded as a “Gold standard” for testing samples when the prevalence of the disease is low in the population and the chances of getting a positive result are less. We further demonstrated through algorithms that pooling samples from 16 individuals is better than pooling samples of 8 individuals when there is a high likelihood of getting negative test results. These findings provide ground to the fact that if sample pooling will be employed on a larger scale, testing capacity will be considerably increased within limited available resources without compromising the test specificity. It will provide healthcare units and enterprises with solutions through scientifically proven algorithms, thus, saving a considerable amount of time and finances. This will eventually help in containing the spread of the pandemic in densely populated areas including vulnerably confined groups, such as nursing homes, hospitals, cruise ships, and military ships.

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