標(biāo)題: Titlebook: Intelligent Astrophysics; Ivan Zelinka,Massimo Brescia,Dalya Baron Book 2021 The Editor(s) (if applicable) and The Author(s), under exclus [打印本頁] 作者: deflate 時(shí)間: 2025-3-21 19:46
書目名稱Intelligent Astrophysics影響因子(影響力)
作者: 瑪瑙 時(shí)間: 2025-3-21 20:31
2194-7287 sical data processing.Discusses new ideas and interdisciplin.This present book discusses the application of the methods to astrophysical data from different perspectives. In this book, the reader will encounter interesting chapters that discuss data processing and pulsars, the complexity and informa作者: Hdl348 時(shí)間: 2025-3-22 03:50
Comparison of Outlier Detection Methods on Astronomical Image Data,plied to data extracted from SDSS stripe 82. After discussing the sensitivity of each method to its own set of hyperparameters, we combine the results from each method to rank the objects and produce a final list of outliers.作者: Integrate 時(shí)間: 2025-3-22 04:43
Book 2021er interesting chapters. The authors of these chapters are experts in their field and have been carefully selected to create this book so that the authors present to the community a representative publication that shows a unique fusion of artificial intelligence and astrophysics.?.作者: 態(tài)學(xué) 時(shí)間: 2025-3-22 09:16 作者: arbiter 時(shí)間: 2025-3-22 15:24 作者: HARD 時(shí)間: 2025-3-22 20:24
Ensemble Classifiers for Pulsar Detection,pically improved classification performance, while class imbalance can be addressed through careful sampling or through cost-sensitive classification. Our results demonstrate that such dedicated ensembles yield better results compared to methods that do not consider class balance.作者: 衰老 時(shí)間: 2025-3-22 22:43 作者: 高興一回 時(shí)間: 2025-3-23 05:07
The Voronoi Tessellation Method in Astronomy,he moving-mesh cosmology simulation. We briefly describe these results paying more attention to the practical application of the Voronoi tessellation related to the spatial large-scale galaxy distribution.作者: 高腳酒杯 時(shí)間: 2025-3-23 09:17
Statistical Characterization and Classification of Astronomical Transients with Machine Learning in is based on a test campaign performed on simulated data. The classification was carried out by comparing the performances among several Machine Learning algorithms on statistical parameters extracted from the light curves. The results make in evidence some critical aspects related to the data quali作者: 軍火 時(shí)間: 2025-3-23 10:27 作者: 我吃花盤旋 時(shí)間: 2025-3-23 15:06 作者: Supplement 時(shí)間: 2025-3-23 18:21
Periodic Astrometric Signal Recovery Through Convolutional Autoencoders,er, we demonstrate that a Deep Convolutional Auto-Encoder is able to retrieve such a signal from simplified simulations of the TOLIMAN data and we present the full experimental pipeline to recreate out experiments from the simulations to the signal analysis. In future works, all the more realistic s作者: 陳列 時(shí)間: 2025-3-24 01:49 作者: 小官 時(shí)間: 2025-3-24 04:25 作者: Boycott 時(shí)間: 2025-3-24 08:17
Franco Vazzaerventionsdienste, politische Fragen, Trauerberatung bei Tierverlust und Mitgefühlsermüdung in der Tier?rzteschaft. Au?erdem wird die tiergestützte Therapie (AAT) als eigenst?ndige und einzigartige Modalit?t eingehend er?rtert. Der anpassungsf?hige Charakter der AAT ist nicht nur auf die symbiotisch作者: VICT 時(shí)間: 2025-3-24 14:01 作者: subordinate 時(shí)間: 2025-3-24 17:12 作者: 震驚 時(shí)間: 2025-3-24 20:24
2194-7287 been carefully selected to create this book so that the authors present to the community a representative publication that shows a unique fusion of artificial intelligence and astrophysics.?.978-3-030-65869-4978-3-030-65867-0Series ISSN 2194-7287 Series E-ISSN 2194-7295 作者: 兇殘 時(shí)間: 2025-3-25 00:36 作者: uveitis 時(shí)間: 2025-3-25 07:01
Iryna Vavilova,Andrii Elyiv,Daria Dobrycheva,Olga Melnyk作者: freight 時(shí)間: 2025-3-25 11:01 作者: 放棄 時(shí)間: 2025-3-25 14:59
Alessandro Bruno,Antonio Pagliaro,Valentina La Parola作者: hereditary 時(shí)間: 2025-3-25 15:59 作者: 故意釣到白楊 時(shí)間: 2025-3-25 23:39
Lars Doorenbos,Stefano Cavuoti,Massimo Brescia,Antonio D’Isanto,Giuseppe Longo作者: 厭煩 時(shí)間: 2025-3-26 00:53 作者: 集聚成團(tuán) 時(shí)間: 2025-3-26 05:28 作者: 領(lǐng)先 時(shí)間: 2025-3-26 11:15
978-3-030-65869-4The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: 機(jī)密 時(shí)間: 2025-3-26 15:55
Intelligent Astrophysics978-3-030-65867-0Series ISSN 2194-7287 Series E-ISSN 2194-7295 作者: Irrigate 時(shí)間: 2025-3-26 17:24
Ivan Zelinka,Massimo Brescia,Dalya BaronPresents new developments, advancements, and selected topics in the fields of artificial intelligence and related algorithms in the astrophysical data processing.Discusses new ideas and interdisciplin作者: 糾纏,纏繞 時(shí)間: 2025-3-26 20:59
Emergence, Complexity and Computationhttp://image.papertrans.cn/i/image/469365.jpg作者: EXUDE 時(shí)間: 2025-3-27 02:30 作者: medieval 時(shí)間: 2025-3-27 06:17 作者: –scent 時(shí)間: 2025-3-27 12:54
Jakub Holewik,Gerald Schaeferlanzungs- und als innersekretorisches Organ einstellt und der dadurch gesch?digte, aus seiner Harmonie gebrachte Organismus einer neuen Gleichgewichtslage zustrebt. Der Begriff des Klimakteriums erscheint umso klarer, je weniger man darüber nachgedacht hat. Wenn man sich n?her in den Gegenstand vert作者: Uncultured 時(shí)間: 2025-3-27 16:20
Michele Delli Veneri,Louis Desdoigts,Morgan A. Schmitz,Alberto Krone-Martins,Emille E. O. Ishida,Petlanzungs- und als innersekretorisches Organ einstellt und der dadurch gesch?digte, aus seiner Harmonie gebrachte Organismus einer neuen Gleichgewichtslage zustrebt. Der Begriff des Klimakteriums erscheint umso klarer, je weniger man darüber nachgedacht hat. Wenn man sich n?her in den Gegenstand vert作者: heterodox 時(shí)間: 2025-3-27 18:35
Artificial Intelligence in Astrophysics,n problems. A particular class of algorithms like bioinspired one mimic working principles from natural evolution (or swarm intelligence) by employing a population-based (swarm) approach, labelling each individual of the population with a fitness and including elements of random, albeit the random i作者: 全面 時(shí)間: 2025-3-28 01:03 作者: Fermentation 時(shí)間: 2025-3-28 03:59 作者: Conscientious 時(shí)間: 2025-3-28 09:08
Statistical Characterization and Classification of Astronomical Transients with Machine Learning inof synoptic (multi-band and multi-epoch) surveys, like LSST (Legacy Survey of Space and Time), requires an extensive use of automatic methods for data processing and interpretation. With data volumes in the petabyte domain, the discrimination of time-critical information has already exceeded the cap作者: peritonitis 時(shí)間: 2025-3-28 13:50
Application of Machine and Deep Learning Methods to the Analysis of IACTs Data,gh-energy regime (TeV) and is playing a significant role in the discovery and characterization of very high-energy gamma-ray emitters. However, the data collected by Imaging Atmospheric Cherenkov Telescopes (IACTs) are highly dominated, even for the most powerful sources, by the overwhelming backgro作者: 杠桿 時(shí)間: 2025-3-28 14:52 作者: Offset 時(shí)間: 2025-3-28 20:28
Ensemble Classifiers for Pulsar Detection,e learning methods can be adopted. One challenge here though is that training such methods is hampered by the inherent imbalance in the available data since signals related to actual pulsars are relatively rare. In this chapter, we show that ensemble classification methods that specifically address 作者: 脫離 時(shí)間: 2025-3-28 23:46
Periodic Astrometric Signal Recovery Through Convolutional Autoencoders,th-mass planets in temperate orbits around nearby sun-like stars. The TOLIMAN space telescope [.] is a low-cost, agile mission concept dedicated to narrow-angle astrometric monitoring of bright binary stars. In particular the mission will be optimised to search for habitable-zone planets around .?Ce作者: Relinquish 時(shí)間: 2025-3-29 06:44
Comparison of Outlier Detection Methods on Astronomical Image Data, . objects. Unsupervised outlier detection algorithms may provide a viable solution. In this work we compare the performances of six methods: the Local Outlier Factor, Isolation Forest, k-means clustering, a measure of novelty, and both a normal and a convolutional autoencoder. These methods were ap作者: athlete’s-foot 時(shí)間: 2025-3-29 10:29
Anomaly Detection in Astrophysics: A Comparison Between Unsupervised Deep and Machine Learning on K beginning. The ongoing and future large and complex multi-messenger sky surveys impose a wide exploiting of robust and efficient automated methods to classify the observed structures and to detect and characterize peculiar and unexpected sources. We performed a preliminary experiment on KiDS DR4 da作者: 衍生 時(shí)間: 2025-3-29 15:08 作者: 燈泡 時(shí)間: 2025-3-29 17:54
Large Astronomical Time Series Pre-processing for Classification Using Artificial Neural Networks,ies (a.k.a. light curves containing usually flux or magnitude on one axis and Julian date on the other axis) are a bit more challenging to classify. As they comes from multiple observational devices and observatories (designed for e.g. variable stars detection, stellar system analysis or extra-solla作者: 錢財(cái) 時(shí)間: 2025-3-29 21:24
