Увійти

Готові списки джерел за темами / Classification exoplanets / Статті в журналах

Щоб переглянути інші типи публікацій з цієї теми, перейдіть за посиланням: Classification exoplanets.

Статті в журналах з теми "Classification exoplanets"

Автор: Grafiati

Опубліковано: 28 травня 2022

Оновлено: 31 липня 2025

Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями

Оберіть тип джерела:

Ознайомтеся з топ-50 статей у журналах для дослідження на тему "Classification exoplanets".

Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.

Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.

Переглядайте статті в журналах для різних дисциплін та оформлюйте правильно вашу бібліографію.

1

Jiang, Jonathan H., Philip E. Rosen, Christina X. Liu, Qianzhuang Wen, and Yanbei Chen. "Analysis of Habitability and Stellar Habitable Zones from Observed Exoplanets." Galaxies 12, no. 6 (2024): 86. https://doi.org/10.3390/galaxies12060086.

Повний текст джерела

Анотація:

The investigation of exoplanetary habitability is integral to advancing our knowledge of extraterrestrial life potential and detailing the environmental conditions of distant worlds. In this analysis, we explore the properties of exoplanets situated with respect to circumstellar habitable zones by implementing a sophisticated filtering methodology on data from the NASA Exoplanet Archive. This research encompasses a thorough examination of 5595 confirmed exoplanets listed in the Archive as of 10 March 2024, systematically evaluated according to their calculated average surface temperatures and

Стилі APA, Harvard, Vancouver, ISO та ін.

2

Donnelly, Shane, and Ayan Dutta. "Kepler Light Curve Classification Using Deep Learning and Markov Transition Field (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 21 (2024): 23475–76. http://dx.doi.org/10.1609/aaai.v38i21.30435.

Повний текст джерела

Анотація:

An exoplanet is a planet, which is not a part of our solar system. Whether life exists in one or more of these exoplanets has fascinated humans for centuries. NASA’s Kepler Space Telescope has discovered more than 70% of known exoplanets in our universe. However, manually determining whether a Kepler light curve indicates an exoplanet or not becomes infeasible with the large volume of data. Due to this, we propose a deep learning-based strategy to automatically classify a Kepler light curve. More specifically, we first convert the light curve time series into its corresponding Markov Transitio

Стилі APA, Harvard, Vancouver, ISO та ін.

3

Jin, Yucheng, Lanyi Yang, and Chia-En Chiang. "Exoplanets Identification and Clustering with Machine Learning Methods." Machine Learning and Applications: An International Journal 9, no. 1 (2022): 1–14. http://dx.doi.org/10.5121/mlaij.2022.9101.

Повний текст джерела

Анотація:

The discovery of habitable exoplanets has long been a heated topic in astronomy. Traditional methods for exoplanet identification include the wobble method, direct imaging, gravitational microlensing, etc., which not only require a considerable investment of manpower, time, and money, but also are limited by the performance of astronomical telescopes. In this study, we proposed the idea of using machine learning methods to identify exoplanets. We used the Kepler dataset collected by NASA from the Kepler Space Observatory to conduct supervised learning, which predicts the existence of exoplanet

Стилі APA, Harvard, Vancouver, ISO та ін.

4

Odrzywolek, A., and J. Rafelski. "Classification of Exoplanets According to Density." Acta Physica Polonica B 49, no. 11 (2018): 1917. http://dx.doi.org/10.5506/aphyspolb.49.1917.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

5

Sarkar, Jyotirmoy, Kartik Bhatia, Snehanshu Saha, Margarita Safonova, and Santonu Sarkar. "Postulating exoplanetary habitability via a novel anomaly detection method." Monthly Notices of the Royal Astronomical Society 510, no. 4 (2021): 6022–32. http://dx.doi.org/10.1093/mnras/stab3556.

Повний текст джерела

Анотація:

ABSTRACT A profound shift in the study of cosmology came with the discovery of thousands of exoplanets and the possibility of the existence of billions of them in our Galaxy. The biggest goal in these searches is whether there are other life-harbouring planets. However, the question which of these detected planets are habitable, potentially-habitable, or maybe even inhabited, is still not answered. Some potentially habitable exoplanets have been hypothesised, but since Earth is the only known habitable planet, measures of habitability are necessarily determined with Earth as the reference. Sev

Стилі APA, Harvard, Vancouver, ISO та ін.

6

Sasmita Kumari Nayak. "Classification of Kepler exoplanet searching from galaxy using machine learning model." World Journal of Advanced Research and Reviews 21, no. 1 (2024): 227–30. http://dx.doi.org/10.30574/wjarr.2024.21.1.0012.

Повний текст джерела

Анотація:

The Kepler exoplanet mission was designed specifically to search the Milky Way galaxy for numerous small planets the size of Earth that are either in or close to the habitable zone. An exoplanet is a planet that orbits stars outside of our solar system. The dataset, which was gathered from the Kaggle website, has 50 columns and 9564 samples. The target variable in the dataset, KOI-disposition, comprises candidate, false positive and confirmed data. We discovered that 5000 samples out of 9564 samples were false positives; each confirmed sample had 2282 candidates. While there are many stars and

Стилі APA, Harvard, Vancouver, ISO та ін.

7

Sasmita, Kumari Nayak. "Classification of Kepler exoplanet searching from galaxy using machine learning model." World Journal of Advanced Research and Reviews 21, no. 1 (2024): 227–30. https://doi.org/10.5281/zenodo.13142011.

Повний текст джерела

Анотація:

The Kepler exoplanet mission was designed specifically to search the Milky Way galaxy for numerous small planets the size of Earth that are either in or close to the habitable zone. An exoplanet is a planet that orbits stars outside of our solar system. The dataset, which was gathered from the Kaggle website, has 50 columns and 9564 samples. The target variable in the dataset, KOI-disposition, comprises candidate, false positive and confirmed data. We discovered that 5000 samples out of 9564 samples were false positives; each confirmed sample had 2282 candidates. While there are many stars and

Стилі APA, Harvard, Vancouver, ISO та ін.

8

Mondal, S. "Classification of Hot Jupiter Population through Statistical Framework." Journal of Scientific Research 14, no. 2 (2022): 513–19. http://dx.doi.org/10.3329/jsr.v14i2.56497.

Повний текст джерела

Анотація:

The recent trend in the discovery of a range of exoplanets opens up a door to evaluate their origin and classification under the light of different planetary attributes. This paper enthusiastically focused on a typical branch of exoplanets, hot Jupiter, and several planetary characteristics were observed to frame the population into substantive categories. In this paper, a statistical framework was also established to understand different planetary formation processes for hot Jupiters. Finally, the relevance of hot Jupiters in search of habitable planets is also discussed briefly.

Стилі APA, Harvard, Vancouver, ISO та ін.

9

Margot, Jean-Luc, Brett Gladman, and Tony Yang. "Quantitative Criteria for Defining Planets." Planetary Science Journal 5, no. 7 (2024): 159. http://dx.doi.org/10.3847/psj/ad55f3.

Повний текст джерела

Анотація:

Abstract The current International Astronomical Union (IAU) definition of “planet” is problematic because it is vague and excludes exoplanets. Here, we describe aspects of quantitative planetary taxonomy and examine the results of unsupervised clustering of solar system bodies to guide the development of possible classification frameworks. Two unsurprising conclusions emerged from the clustering analysis: (1) satellites are distinct from planets and (2) dynamical dominance is a natural organizing principle for planetary taxonomy. To generalize an existing dynamical dominance criterion, we adop

Стилі APA, Harvard, Vancouver, ISO та ін.

10

Hasegawa, Yasuhiro, and Mark R. Swain. "Bulk and Atmospheric Metallicities as Direct Probes of Sequentially Varying Accretion Mechanisms of Gas and Solids Onto Planets." Astrophysical Journal Letters 973, no. 2 (2024): L46. http://dx.doi.org/10.3847/2041-8213/ad7957.

Повний текст джерела

Анотація:

Abstract Core accretion is the standard scenario of planet formation, wherein planets are formed by sequential accretion of gas and solids, and is widely used to interpret exoplanet observations. However, no direct probes of the scenario have been discussed yet. Here, we introduce an onion-like model as one idealization of sequential accretion and propose that bulk and atmospheric metallicities of exoplanets can be used as direct probes of the process. Our analytical calculations, coupled with observational data, demonstrate that the trend of observed exoplanets supports the sequential accreti

Стилі APA, Harvard, Vancouver, ISO та ін.

11

Battley, Matthew P., David J. Armstrong, and Don Pollacco. "YOUNG Star detrending for Transiting Exoplanet Recovery (YOUNGSTER) – II. Using self-organizing maps to explore young star variability in sectors 1–13 of TESS data." Monthly Notices of the Royal Astronomical Society 511, no. 3 (2022): 4285–304. http://dx.doi.org/10.1093/mnras/stac278.

Повний текст джерела

Анотація:

ABSTRACT Young exoplanets and their corresponding host stars are fascinating laboratories for constraining the time-scale of planetary evolution and planet–star interactions. However, because young stars are typically much more active than the older population, in order to discover more young exoplanets, greater knowledge of the wide array of young star variability is needed. Here Kohonen self-organizing maps (SOMs) are used to explore young star variability present in the first year of observations from the Transiting Exoplanet Survey Satellite (TESS), with such knowledge valuable to perform

Стилі APA, Harvard, Vancouver, ISO та ін.

12

Valizadegan, Hamed, Miguel J. S. Martinho, Laurent S. Wilkens, et al. "ExoMiner: A Highly Accurate and Explainable Deep Learning Classifier That Validates 301 New Exoplanets." Astrophysical Journal 926, no. 2 (2022): 120. http://dx.doi.org/10.3847/1538-4357/ac4399.

Повний текст джерела

Анотація:

Abstract The Kepler and Transiting Exoplanet Survey Satellite (TESS) missions have generated over 100,000 potential transit signals that must be processed in order to create a catalog of planet candidates. During the past few years, there has been a growing interest in using machine learning to analyze these data in search of new exoplanets. Different from the existing machine learning works, ExoMiner, the proposed deep learning classifier in this work, mimics how domain experts examine diagnostic tests to vet a transit signal. ExoMiner is a highly accurate, explainable, and robust classifier

Стилі APA, Harvard, Vancouver, ISO та ін.

13

Howe, Alex R., Juliette C. Becker, Christopher C. Stark, and Fred C. Adams. "Architecture Classification for Extrasolar Planetary Systems." Astronomical Journal 169, no. 3 (2025): 149. https://doi.org/10.3847/1538-3881/adabdb.

Повний текст джерела

Анотація:

Abstract This paper presents a classification framework for the architectures of planetary systems based on a complete survey of the confirmed exoplanet population. With nearly 6000 confirmed exoplanets discovered, including more than 300 multiplanet systems with N ≥ 3 planets, the current observational sample has reached a point where it is both feasible and useful to build a classification system that divides the observed population into meaningful categories. This framework provides a criterion for splitting planetary systems into inner and outer regimes, then further dividing inner systems

Стилі APA, Harvard, Vancouver, ISO та ін.

14

Conzo, G., and M. Moriconi. "TOI-6883.01: A Single-transit Planet Candidate Detected from TESS." Research Notes of the AAS 8, no. 2 (2024): 53. http://dx.doi.org/10.3847/2515-5172/ad2c85.

Повний текст джерела

Анотація:

Abstract A new candidate exoplanet, proposed in ExoFOP by authors, it was promoted from TIC 393818343 to TOI-6883.01 at coordinates R.A.(J2000)20:41:10.01 decl.(J2000) + 3:38:17.87 in the Delphinus constellation and its distance is (93.73 ± 0.35) pc from Earth. The target star is a Sun-type having G0 class according to Skiff spectral classification and it has photometric magnitude V(Johnson) = 9.5 mag. We analyzed the transit light curve using Transit Exoplanet Survey Satellite’s ExoMAST tool, and evaluated the Lightcurve Analysis Tool for Transiting Exoplanets-report by excluding false positi

Стилі APA, Harvard, Vancouver, ISO та ін.

15

Wang, Li-Heng, Kai Li, Xiang Gao, Ya-Ni Guo, and Guo-You Sun. "Using Machine Learning Method for Variable Star Classification Using the TESS Sectors 1–57 Data." Astrophysical Journal 986, no. 1 (2025): 19. https://doi.org/10.3847/1538-4357/add159.

Повний текст джерела

Анотація:

Abstract The Transiting Exoplanet Survey Satellite is a wide-field all-sky survey mission designed to detect Earth-sized exoplanets. After over 4 yr of photometric surveys, data from sectors 1–57, including approximately 1,050,000 light curves with a 2 minute cadence, were collected. By crossmatching the data with Gaia’s variable star catalogue, we obtained labeled data sets for further analysis. Using a random forest classifier, we performed classification of variable stars and designed distinct classification processes for each subclass: 6770 EA, 2971 EW, 980 CEP, 8347 DSCT, 457 RRab, 404 RR

Стилі APA, Harvard, Vancouver, ISO та ін.

16

Pruthviraj Sunil Rajput. "Exoplanet Detection Using Machine Learning : A Comparative Study Using Kepler Mission Data." International Journal of Scientific Research in Science and Technology 11, no. 5 (2024): 43–48. http://dx.doi.org/10.32628/ijsrst2411426.

Повний текст джерела

Анотація:

The search for habitable planets outside our solar system has captivated scientists throughout the centuries. Discovery and characterization of exoplanets have been one of the most important endeavors of modern astronomy. With various space missions, we have significantly expanded our observational capacity, resulting in an abundance of information about the universe. The influx of more data necessitates the development of techniques that can aid astronomers in processing all the information more efficiently and in an automated manner. Machine learning in recent years has become an indispensab

Стилі APA, Harvard, Vancouver, ISO та ін.

17

Unterborn, C. T., S. J. Desch, J. Haldemann, et al. "The Nominal Ranges of Rocky Planet Masses, Radii, Surface Gravities, and Bulk Densities." Astrophysical Journal 944, no. 1 (2023): 42. http://dx.doi.org/10.3847/1538-4357/acaa3b.

Повний текст джерела

Анотація:

Abstract The two primary observable quantities of an exoplanet—its mass and radius—alone are not sufficient to probe a rocky exoplanet’s interior composition and mineralogy. To overcome this, host-star abundances of the primary planet-building elements (Mg, Si, Fe) are typically used as a proxy for the planet’s bulk composition. The majority of small exoplanet hosts, however, do not have available abundance data. Here we present the open-source ExoPlex mass–radius–composition solver. Unlike previous open-source mass–radius solvers, ExoPlex calculates the core chemistry and equilibrium mantle m

Стилі APA, Harvard, Vancouver, ISO та ін.

18

RAO, T. SRINIVAS, P. KRISHNA BALAMOHAN, M. DURGA SAI SANDEEP, P. SIVA SAGAR, P. BRAHMA REDDY, and M. YUVA KARTHIK. "EXOPLANET DETECTION USING MACHINE LEARNING." Journal of Nonlinear Analysis and Optimization 16, no. 01 (2025): 126–29. https://doi.org/10.36893/jnao.2025.v16i01.016.

Повний текст джерела

Анотація:

The identification of exoplanets, planets that orbit stars beyond our solar system, plays a crucial role in advancing our understanding of planetary formation, system evolution, and the search for extraterrestrial life. Conventional detection techniques, such as the transit method, depend largely on manual interpretation and computationally demanding processes, which struggle to handle the rapidly growing astronomical data from missions like Kepler. Machine learning (ML) presents a groundbreaking approach to enhance and automate exoplanet detection, improving efficiency and accuracy. This rese

Стилі APA, Harvard, Vancouver, ISO та ін.

19

Pham, Dang, and Lisa Kaltenegger. "Color classification of Earth-like planets with machine learning." Monthly Notices of the Royal Astronomical Society 504, no. 4 (2021): 6106–16. http://dx.doi.org/10.1093/mnras/stab1144.

Повний текст джерела

Анотація:

ABSTRACT Atmospheric characterization of directly imaged exoplanets is currently limited to Giant planets and Mini-Neptunes. However, upcoming ground-based Extremely Large Telescopes (ELTs) and space-based concepts such as Origins, HabEx, and LUVOIR are designed to characterize rocky exoplanets. But spectroscopy of Earth-like planets is time-intensive even for upcoming telescopes; therefore, initial photometry has been discussed as a promising avenue to faster classify and prioritize exoplanets. Thus, in this article we explore whether photometric flux – using the standard Johnson filters – ca

Стилі APA, Harvard, Vancouver, ISO та ін.

20

Gaurav and Sumit Gupta. "Harnessing the Power of Convolutional Neural Network for Exoplanet Discovery." American Journal of Advanced Computing 2, no. 1 (2023): 76–80. http://dx.doi.org/10.15864/ajac.21023.

Повний текст джерела

Анотація:

The discovery of planets apart from Earth that can sustain lives has always been fascinating as well as challenging. Discussion around such planets, popularly termed as "Exoplanets" have been doing the rounds for quite some time now. These exoplanets are often considered to be "Earth-like" or "habitable" because they may have conditions that could potentially support life. This work focuses on how Deep Learning techniques can be useful in identifying potential exoplanets. To do so, astronomical data gathered by space telescopes such as Kepler and BRITE have been utilized. The method employed t

Стилі APA, Harvard, Vancouver, ISO та ін.

21

Nikouravan, Bijan. "Estimating Physical Properties of Confirmed Exoplanets: I. Calculation of essential planetary properties-Possible M-R cataloging of exoplanets." International Journal of Fundamental Physical Sciences 11, no. 1 (2021): 17–29. http://dx.doi.org/10.14331/ijfps.2021.330146.

Повний текст джерела

Анотація:

The discovery of extrasolar planets outside our solar system and around other stars is now well underway. In the presented paper, calculations of some physical properties for confirmed exoplanets have been done. We have estimated physical properties such as the semi-major axis for potentially habitable exoplanets, the mass of planets by applying Kepler's third law around the mass of solar, Jupiter, and Earth-mass, stellar luminosity, habitability zone for the inner center and outer regions, radial velocity amplitude, planetary equilibrium temperature (PET) or effective radiation emission tempe

Стилі APA, Harvard, Vancouver, ISO та ін.

22

Zotos, Euaggelos E., Konstantinos E. Papadakis, and S. Wageh. "Mapping exomoon trajectories around Earth-like exoplanets." Monthly Notices of the Royal Astronomical Society 502, no. 4 (2021): 5292–301. http://dx.doi.org/10.1093/mnras/stab421.

Повний текст джерела

Анотація:

ABSTRACT We consider a system in which both the parent star and the Earth-like exoplanet move on circular orbits. Using numerical methods, such as the orbit classification technique, we study all types of trajectories of possible exomoons around the exoplanet. In particular, we scan the phase space around the exoplanet and we distinguish between bounded, collisional, and escaping trajectories, considering both retrograde and prograde types of motion. In the case of bounded regular motion, we also use the grid method and a standard predictor-corrector procedure for revealing the corresponding n

Стилі APA, Harvard, Vancouver, ISO та ін.

23

Hao, Nicole, Laura Flagg, and Ray Jayawardhana. "Detecting and Classifying Flares in High-resolution Solar Spectra with Supervised Machine Learning." Astrophysical Journal 973, no. 2 (2024): 109. http://dx.doi.org/10.3847/1538-4357/ad5be3.

Повний текст джерела

Анотація:

Abstract Flares are a well-studied aspect of the Sun’s magnetic activity. Detecting and classifying solar flares can inform the analysis of contamination caused by stellar flares in exoplanet transmission spectra. In this paper, we present a standardized procedure to classify solar flares with the aid of supervised machine learning. Using flare data from the RHESSI mission and solar spectra from the HARPS-N instrument, we trained several supervised machine-learning models, and found that the best-performing algorithm is C-Support Vector Classification (SVC) with nonlinear kernels, specifically

Стилі APA, Harvard, Vancouver, ISO та ін.

24

Luz, Thiago S. F., Rodrigo A. S. Braga, and Enio R. Ribeiro. "Assessment of Ensemble-Based Machine Learning Algorithms for Exoplanet Identification." Electronics 13, no. 19 (2024): 3950. http://dx.doi.org/10.3390/electronics13193950.

Повний текст джерела

Анотація:

This paper presents a comprehensive assessment procedure for evaluating Ensemble-based Machine Learning algorithms in the context of exoplanet classification. Each of the algorithm hyperparameter values were tuned. Deployments were carried out using the cross-validation method. Performance metrics, including accuracy, sensitivity, specificity, precision, and F1 score, were evaluated using confusion matrices generated from each implementation. Machine Learning (ML) algorithms were trained and used to identify exoplanet data. Most of the current research deals with traditional ML algorithms for

Стилі APA, Harvard, Vancouver, ISO та ін.

25

Cui, Kaiming, Junjie Liu, Fabo Feng, and Jifeng Liu. "Identify Light-curve Signals with Deep Learning Based Object Detection Algorithm. I. Transit Detection." Astronomical Journal 163, no. 1 (2021): 23. http://dx.doi.org/10.3847/1538-3881/ac3482.

Повний текст джерела

Анотація:

Abstract Deep learning techniques have been well explored in the transiting exoplanet field; however, previous work mainly focuses on classification and inspection. In this work, we develop a novel detection algorithm based on a well-proven object detection framework in the computer vision field. Through training the network on the light curves of the confirmed Kepler exoplanets, our model yields about 90% precision and recall for identifying transits with signal-to-noise ratio higher than 6 (set the confidence threshold to 0.6). Giving a slightly lower confidence threshold, recall can reach h

Стилі APA, Harvard, Vancouver, ISO та ін.

26

Molaverdikhani, Karan, Thomas Henning, and Paul Mollière. "From Cold to Hot Irradiated Gaseous Exoplanets: Toward an Observation-based Classification Scheme." Astrophysical Journal 873, no. 1 (2019): 32. http://dx.doi.org/10.3847/1538-4357/aafda8.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

27

Safron, E. J., T. S. Boyajian, and N. Eisner. "The satchel pipeline: a general tool for data classified through citizen science." Monthly Notices of the Royal Astronomical Society 512, no. 3 (2022): 3972–91. http://dx.doi.org/10.1093/mnras/stac787.

Повний текст джерела

Анотація:

ABSTRACT Citizen science is a powerful analysis tool, capable of processing large amounts of data in a very short time. To bridge the gap between classification data products from web-based citizen science platforms to statistically robust signal significance scores, we present the Search Algorithm for Transits in the Citizen science Hunt for Exoplanets in Light curves (satchel) pipeline. This open source, customizable pipeline was constructed to identify and assign significance estimates to one-dimensional features marked by volunteers. We describe the functional capabilities of the satchel p

Стилі APA, Harvard, Vancouver, ISO та ін.

28

Lo, Tinfei, and Jiani Fu. "Analysis of Eclipsing Binary Stars and Identification of Exoplanets Using Transit Timing Variation Using data from TESS." Journal of Physics: Conference Series 2381, no. 1 (2022): 012106. http://dx.doi.org/10.1088/1742-6596/2381/1/012106.

Повний текст джерела

Анотація:

Abstract This study uses openly available TESS (Transiting Exoplanet Sky Survey) data to detect evidence for potential exoplanet transits in an eclipsing binary system. Using the Python transit light curve analysis package lightcurve, we discuss the characteristics of an eclipsing binary star’s light curve in determining the classification of a system. In addition, we also study phenomena on a light curve that indicates the existence of a potential exoplanet. Although this investigation yields null results and finds no conclusive evidence, suggesting the presence of exoplanet transits in known

Стилі APA, Harvard, Vancouver, ISO та ін.

29

Cuéllar, Sara, Paulo Granados, Ernesto Fabregas, et al. "Deep learning exoplanets detection by combining real and synthetic data." PLOS ONE 17, no. 5 (2022): e0268199. http://dx.doi.org/10.1371/journal.pone.0268199.

Повний текст джерела

Анотація:

Scientists and astronomers have attached great importance to the task of discovering new exoplanets, even more so if they are in the habitable zone. To date, more than 4300 exoplanets have been confirmed by NASA, using various discovery techniques, including planetary transits, in addition to the use of various databases provided by space and ground-based telescopes. This article proposes the development of a deep learning system for detecting planetary transits in Kepler Telescope light curves. The approach is based on related work from the literature and enhanced to validation with real ligh

Стилі APA, Harvard, Vancouver, ISO та ін.

30

Fiscale, Stefano, Alessio Ferone, Angelo Ciaramella, et al. "Detection of Exoplanets in Transit Light Curves with Conditional Flow Matching and XGBoost." Electronics 14, no. 9 (2025): 1738. https://doi.org/10.3390/electronics14091738.

Повний текст джерела

Анотація:

NASA’s space-based telescopes Kepler and Transiting Exoplanet Survey Satellite (TESS) have detected billions of potential planetary signatures, typically classified with Convolutional Neural Networks (CNNs). In this study, we introduce a hybrid model that combines deep learning, dimensionality reduction, decision trees, and diffusion models to distinguish planetary transits from astrophysical false positives and instrumental artifacts. Our model consists of three main components: (i) feature extraction using the CNN VGG19, (ii) dimensionality reduction through t-Distributed Stochastic Neighbor

Стилі APA, Harvard, Vancouver, ISO та ін.

31

Amarante, João A. S., and Helio J. Rocha-Pinto. "Planetary populations according to the orbital angular momentum." Proceedings of the International Astronomical Union 5, S265 (2009): 420–21. http://dx.doi.org/10.1017/s1743921310001122.

Повний текст джерела

Анотація:

AbstractWe investigate the angular momentum distribution of known exoplanetary systems, as a function of the planetary mass, orbital semimajor axis and metallicity of the host star. We find exoplanets seems to be classified according to at least two ‘populations’, with respect to their angular momentum properties. This classification is independent on the composition of the planet and seems to be valid for both jovian and neptunian planets, and probably can be extrapolated to the terrestrial planets of the Solar System. We analyse these ‘populations’ considering the phenomenon of planetary mig

Стилі APA, Harvard, Vancouver, ISO та ін.

32

Tito, Elizabeth P., and Vadim I. Pavlov. "“In-System” Fission-Events: An Insight into Puzzles of Exoplanets and Stars?" Universe 7, no. 5 (2021): 118. http://dx.doi.org/10.3390/universe7050118.

Повний текст джерела

Анотація:

In expansion of our recent proposal that the solar system’s evolution occurred in two stages—during the first stage, the gaseous giants formed (via disk instability), and, during the second stage (caused by an encounter with a particular stellar-object leading to “in-system” fission-driven nucleogenesis), the terrestrial planets formed (via accretion)—we emphasize here that the mechanism of formation of such stellar-objects is generally universal and therefore encounters of such objects with stellar-systems may have occurred elsewhere across galaxies. If so, their aftereffects may perhaps be o

Стилі APA, Harvard, Vancouver, ISO та ін.

33

Basak, S., S. Saha, A. Mathur, et al. "CEESA meets machine learning: A Constant Elasticity Earth Similarity Approach to habitability and classification of exoplanets." Astronomy and Computing 30 (January 2020): 100335. http://dx.doi.org/10.1016/j.ascom.2019.100335.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

34

Saha, Snehanshu, Nithin Nagaraj, Archana Mathur, Rahul Yedida, and Sneha H R. "Evolution of novel activation functions in neural network training for astronomy data: habitability classification of exoplanets." European Physical Journal Special Topics 229, no. 16 (2020): 2629–738. http://dx.doi.org/10.1140/epjst/e2020-000098-9.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

35

Tey, Evan, Dan Moldovan, Michelle Kunimoto, et al. "Identifying Exoplanets with Deep Learning. V. Improved Light-curve Classification for TESS Full-frame Image Observations." Astronomical Journal 165, no. 3 (2023): 95. http://dx.doi.org/10.3847/1538-3881/acad85.

Повний текст джерела

Анотація:

Abstract The TESS mission produces a large amount of time series data, only a small fraction of which contain detectable exoplanetary transit signals. Deep-learning techniques such as neural networks have proved effective at differentiating promising astrophysical eclipsing candidates from other phenomena such as stellar variability and systematic instrumental effects in an efficient, unbiased, and sustainable manner. This paper presents a high-quality data set containing light curves from the Primary Mission and 1st Extended Mission full-frame images and periodic signals detected via box leas

Стилі APA, Harvard, Vancouver, ISO та ін.

36

Airlangga, Gregorius. "Exoplanet Classification Through Machine Learning: A Comparative Analysis of Algorithms Using Kepler Data." MALCOM: Indonesian Journal of Machine Learning and Computer Science 4, no. 3 (2024): 753–63. http://dx.doi.org/10.57152/malcom.v4i3.1303.

Повний текст джерела

Анотація:

This study delves into the classification of exoplanets using data from the Kepler Space Telescope, comparing a suite of machine learning (ML) models to ascertain their efficacy in distinguishing confirmed planets, candidates, and false positives. With a dataset meticulously preprocessed for quality, completeness, and relevance, we embarked on an analytical journey employing models like Decision Tree, Random Forest, Hist Gradient Boosting, CatBoost, AdaBoost, LightGBM, XGBoost, Extra Trees, Logistic Regression, and XGBoost RF. These models underwent rigorous evaluation across metrics such as A

Стилі APA, Harvard, Vancouver, ISO та ін.

37

Mitani, Hiroto, Riouhei Nakatani, and Naoki Yoshida. "Physics of the atmospheric escape driven by EUV photoionization heating: Classification of the hydrodynamic escape in close-in planets." Proceedings of the International Astronomical Union 17, S370 (2021): 155–60. http://dx.doi.org/10.1017/s1743921322003556.

Повний текст джерела

Анотація:

AbstractThe intense extreme ultraviolet radiation heats the upper atmosphere of close-in exoplanets and drives the atmospheric escape. The escaping process determines the planetary evolution of close-in planets. The mass loss rate depends on the UV flux at the planet. We introduce the relevant physical quantities which describe the dominant physics in the atmosphere. We find that the equilibrium temperature and the characteristic temperature determine whether the system becomes energy-limited or recombination-limited. We classify the observed close-in planets using the physical conditions. We

Стилі APA, Harvard, Vancouver, ISO та ін.

38

Abdelhadi, Belal, and David Rubin. "Picture Perfect: Photometric Transient Classification Using the ParSNIP Model with Roman Hourglass Simulations." Publications of the Astronomical Society of the Pacific 136, no. 12 (2024): 124504. https://doi.org/10.1088/1538-3873/ad9a7d.

Повний текст джерела

Анотація:

Abstract The Roman Space Telescope, equipped with a 2.4 m primary mirror and optical–NIR wide-field camera, promises to revolutionize our understanding of dark energy, exoplanets, and infrared astrophysics. One of the Roman Core Community Surveys is the High Latitude Time Domain Survey (HLTDS), which will measure more than 10,000 SN Ia light curves but obtain a fraction of this number with spectra. The remaining SNe will have to be photometrically classified to achieve the full potential of the Roman HLTDS. To investigate transient yields and classifications, Rose et al. updated the Photometri

Стилі APA, Harvard, Vancouver, ISO та ін.

39

Ivanov, Valentin D., Juan Carlos Beamín, Claudio Cáceres, and Dante Minniti. "A qualitative classification of extraterrestrial civilizations." Astronomy & Astrophysics 639 (July 2020): A94. http://dx.doi.org/10.1051/0004-6361/202037597.

Повний текст джерела

Анотація:

Context. Interest in searches for extraterrestrial civilizations (ETCs) has been boosted in recent decades by the discovery of thousands of exoplanets. Aims. We turn to the classification of ETCs for new considerations that may help to design better strategies for searching for ETCs. Methods. This study is based on analogies with our own biological, historical, technological, and scientific development. We took a basic taxonomic approach to ETCs and investigated the implications of the new classification on ETC evolution and observational patterns. Finally, we used the quantitative scheme of K

Стилі APA, Harvard, Vancouver, ISO та ін.

40

Oelkers, Ryan J., Lucas M. Macri, Jennifer L. Marshall, et al. "A Wide Angle Search for Hot Jupiters and Pre-Main Sequence Binaries in Young Stellar Associations." Proceedings of the International Astronomical Union 10, S314 (2015): 73–74. http://dx.doi.org/10.1017/s1743921315006183.

Повний текст джерела

Анотація:

AbstractThe past two decades have seen a significant advancement in the detection, classification and understanding of exoplanets and binary star systems. The vast majority of these systems consist of stars on the main sequence or on the giant branch, leading to a dearth of knowledge of properties at early times (<50 Myr). Only one transiting planet candidate and a dozen eclipsing binaries are known among pre-main sequence objects, yet these are the systems that can provide the best constraints on stellar and planetary formation models. We have recently completed a photometric survey of 3 y

Стилі APA, Harvard, Vancouver, ISO та ін.

41

Jiang, Xiaoming, Jonathan H. Jiang, Remo Burn, and Zong-Hong Zhu. "Classify and Explore the Diversity of Planetary Population and Interior Properties." Astrophysical Journal 957, no. 1 (2023): 56. http://dx.doi.org/10.3847/1538-4357/acf0bf.

Повний текст джерела

Анотація:

Abstract Classification is an essential method and has been developed widely in astronomy. However, planets still lack a universal classification framework, because the solar system planet sample is too small for statistical analysis. Fortunately, exoplanets supply large samples to help build up synthetic planetary populations then support a classification framework. In this study, we use synthetic populations to explore the diversity and evolution relations of planets. We detect six outstanding clusters in mass–radius space with the kernel density estimation and extract typical planets for ea

Стилі APA, Harvard, Vancouver, ISO та ін.

42

Billingsley, Berry, Joshua M. Heyes, Tim Lesworth, and Marc Sarzi. "Can a robot be a scientist? Developing students’ epistemic insight through a lesson exploring the role of human creativity in astronomy." Physics Education 58, no. 1 (2022): 015501. http://dx.doi.org/10.1088/1361-6552/ac9d19.

Повний текст джерела

Анотація:

Abstract Artificial intelligence is transforming the practice of science worldwide. Breakthroughs in machine learning are enabling, for example, the discovery of potentially habitable exoplanets beyond our solar system. The growing role of artificial intelligence (AI) in science raises questions for scientists, philosophers, computer scientists … and educators. How will the scholarship and practice of science education respond to the growing role of artificial intelligence in science? Questions like ‘Can a robot be a scientist?’ can help stimulate students’ epistemic curiosity, about the natur

Стилі APA, Harvard, Vancouver, ISO та ін.

43

Peralta, Javier, Takeshi Imamura, Peter L. Read, David Luz, Arianna Piccialli, and M. A. Lopez-Valverde. "Analytical solution for Waves in planets with atmospheric superrotation. I. Acoustic and Inertia-Gravity Waves." Astrophysical Journal Supplement Series 213, no. 1 (2014): 17. https://doi.org/10.1088/0067-0049/213/1/17.

Повний текст джерела

Анотація:

This paper is the first of a two-part study devoted to developing tools for a systematic classification of the wide variety of atmospheric waves expected on slowly rotating planets with atmospheric superrotation. Starting with the primitive equations for a cyclostrophic regime, we have deduced the analytical solution for the possible waves, simultaneously including the effect of the metric terms for the centrifugal force and the meridional shear of the background wind. In those cases when the conditions for the method of the multiple scales in height are met, these wave solutions are also vali

Стилі APA, Harvard, Vancouver, ISO та ін.

44

Glymour, Clark, Joseph D. Ramsey, and Kun Zhang. "The Evaluation of Discovery: Models, Simulation and Search through “Big Data”." Open Philosophy 2, no. 1 (2019): 39–48. http://dx.doi.org/10.1515/opphil-2019-0005.

Повний текст джерела

Анотація:

Abstract A central theme in western philosophy was to find formal methods that can reliably discover empirical relationships and their explanations from data assembled from experience. As a philosophical project, that ambition was abandoned in the 20th century and generally dismissed as impossible. It was replaced in philosophy by neo-Kantian efforts at reconstruction and justification, and in professional statistics by the more limited ambition to estimate a small number of parameters in pre-specified hypotheses. The influx of “big data” from climate science, neuropsychology, biology, astrono

Стилі APA, Harvard, Vancouver, ISO та ін.

45

Wang, Shijie, Kazuhiro D. Kanagawa, and Yasushi Suto. "Architecture of Planetary Systems Predicted from Protoplanetary Disks Observed with ALMA. I. Mass of the Possible Planets Embedded in the Dust Gap." Astrophysical Journal 923, no. 2 (2021): 165. http://dx.doi.org/10.3847/1538-4357/ac2d95.

Повний текст джерела

Анотація:

Abstract Recent ALMA observations have identified a variety of dust gaps in protoplanetary disks, which are commonly interpreted to be generated by unobserved planets. Predicting mass of such embedded planets is of fundamental importance in comparing those disk architectures with the observed diversity of exoplanets. The prediction, however, depends on the assumption that whether the same gap structure exists in the dust component alone or in the gas component as well. We assume a planet can only open a gap in the gas component when its mass exceeds the pebble isolation mass by considering the

Стилі APA, Harvard, Vancouver, ISO та ін.

46

Do Ó, Clarissa R., Kelly K. O’Neil, Quinn M. Konopacky, et al. "The Orbital Eccentricities of Directly Imaged Companions Using Observable-based Priors: Implications for Population-level Distributions." Astronomical Journal 166, no. 2 (2023): 48. http://dx.doi.org/10.3847/1538-3881/acdc9a.

Повний текст джерела

Анотація:

Abstract The eccentricity of a substellar companion is an important tracer of its formation history. Directly imaged companions often present poorly constrained eccentricities. A recently developed prior framework for orbit fitting called “observable-based priors” has the advantage of improving biases in derived orbit parameters for objects with minimal phase coverage, which is the case for the majority of directly imaged companions. We use observable-based priors to fit the orbits of 21 exoplanets and brown dwarfs in an effort to obtain the eccentricity distributions with minimized biases. We

Стилі APA, Harvard, Vancouver, ISO та ін.

47

Tristan, Isaiah I., Yuta Notsu, Adam F. Kowalski, et al. "A 7 Day Multiwavelength Flare Campaign on AU Mic. I. High-time-resolution Light Curves and the Thermal Empirical Neupert Effect." Astrophysical Journal 951, no. 1 (2023): 33. http://dx.doi.org/10.3847/1538-4357/acc94f.

Повний текст джерела

Анотація:

Abstract We present light curves and flares from a 7 day, multiwavelength observational campaign of AU Mic, a young and active dM1e star with exoplanets and a debris disk. We report on 73 unique flares between the X-ray to optical data. We use high-time-resolution near-UV (NUV) photometry and soft X-ray (SXR) data from the X-ray Multi-Mirror Mission to study the empirical Neupert effect, which correlates the gradual and impulsive phase flaring emissions. We find that 65% (30 of 46) flares do not follow the Neupert effect, which is 3 times more excursions than seen in solar flares, and propose

Стилі APA, Harvard, Vancouver, ISO та ін.

48

Fletcher, Leigh N., Patrick G. J. Irwin, Joanna K. Barstow, Remco J. de Kok, Jae-Min Lee, and Suzanne Aigrain. "Exploring the diversity of Jupiter-class planets." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 372, no. 2014 (2014): 20130064. http://dx.doi.org/10.1098/rsta.2013.0064.

Повний текст джерела

Анотація:

Of the 900+ confirmed exoplanets discovered since 1995 for which we have constraints on their mass (i.e. not including Kepler candidates), 75% have masses larger than Saturn (0.3 M J ), 53% are more massive than Jupiter and 67% are within 1 AU of their host stars. When Kepler candidates are included, Neptune-sized giant planets could form the majority of the planetary population. And yet the term ‘hot Jupiter’ fails to account for the incredible diversity of this class of astrophysical object, which exists on a continuum of giant planets from the cool jovians of our own Solar System to the hig

Стилі APA, Harvard, Vancouver, ISO та ін.

49

Feigelson, Eric D., Federica B. Bianco, and Rosaria Bonito. "An Evenly Spaced LSST Cadence for Rapidly Variable Stars." Astrophysical Journal Supplement Series 268, no. 1 (2023): 11. http://dx.doi.org/10.3847/1538-4365/ace616.

Повний текст джерела

Анотація:

Abstract Stars exhibit a bewildering variety of rapidly variable behaviors ranging from explosive magnetic flares to stochastically changing accretion to periodic pulsations or rotation. The principal Rubin Observatory Legacy Survey of Space and Time (LSST) surveys will have cadences too sparse and irregular to capture many of these phenomena. We propose here an LSST microsurvey to observe a single Galactic field, rich in unobscured stars, in a continuous sequence of 30 s exposures for one long winter night in a single photometric band. The result will be a unique data set of ∼1 million regula

Стилі APA, Harvard, Vancouver, ISO та ін.

50

Sainsbury-Martinez, F., P. Tremblin, M. Mancip, S. Donfack, E. Honore, and M. Bourenane. "Characterizing the Atmospheric Dynamics of HD 209458b-like Hot Jupiters Using AI-driven Image Recognition/Categorization." Astrophysical Journal 958, no. 1 (2023): 68. http://dx.doi.org/10.3847/1538-4357/acf9ed.

Повний текст джерела

Анотація:

Abstract In order to understand the results of recent observations of exoplanets, models have become increasingly complex. Unfortunately, this increases both the computational cost and output size of said models. We intend to explore if AI image recognition can alleviate this burden. We used DYNAMICO to run a series of HD 209458-like models with different orbital radii. Training data for a number of features of interest was selected from the initial outputs of these models. This was used to train a pair of multi-categorization convolutional neural networks (CNNs), which we applied to our outer

Стилі APA, Harvard, Vancouver, ISO та ін.

Ми пропонуємо знижки на всі преміум-плани для авторів, чиї праці увійшли до тематичних добірок літератури. Зв'яжіться з нами, щоб отримати унікальний промокод!