BEHIND THE ZONE OF AVOIDANCE OF THE MILKY WAY: WHAT CAN WE RESTORE BY DIRECT AND INDIRECT METHODS?
PACS number: 98.35.-a °Purpose: to present a brief overview of methods for restoring the large-scale structure of the Universe behind the Zone of Avoidance (ZoA) of the Milky Way; to propose a new “algorithm of darning the ZoA” and new approach based on the Generative adversarial network (GAN) to r...
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large-scale structure of the Universe Milky Way galaxies galaxy clusters zone of avoidance machine learning Generative adversarial network (GAN) “algorithm of darning the ZoA” крупномасштабная структура Вселенной Млечный Путь галактики скопления галактик зона избегания галактик машинное обучение генерирующая состязательная сеть (GAN) “алгоритм штопки зоны избегания” великомасштабна структура Всесвіту Чумацький Шлях галактики скупчення галактик зона уникнення галактик машинне навчання генеруюча змагальна мережа (GAN) “алгоритм штопання зони уникнення” |
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large-scale structure of the Universe Milky Way galaxies galaxy clusters zone of avoidance machine learning Generative adversarial network (GAN) “algorithm of darning the ZoA” крупномасштабная структура Вселенной Млечный Путь галактики скопления галактик зона избегания галактик машинное обучение генерирующая состязательная сеть (GAN) “алгоритм штопки зоны избегания” великомасштабна структура Всесвіту Чумацький Шлях галактики скупчення галактик зона уникнення галактик машинне навчання генеруюча змагальна мережа (GAN) “алгоритм штопання зони уникнення” Vavilova, I. B. Elyiv, A. A. Vasylenko, M. Yu. BEHIND THE ZONE OF AVOIDANCE OF THE MILKY WAY: WHAT CAN WE RESTORE BY DIRECT AND INDIRECT METHODS? |
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large-scale structure of the Universe Milky Way galaxies galaxy clusters zone of avoidance machine learning Generative adversarial network (GAN) “algorithm of darning the ZoA” крупномасштабная структура Вселенной Млечный Путь галактики скопления галактик зона избегания галактик машинное обучение генерирующая состязательная сеть (GAN) “алгоритм штопки зоны избегания” великомасштабна структура Всесвіту Чумацький Шлях галактики скупчення галактик зона уникнення галактик машинне навчання генеруюча змагальна мережа (GAN) “алгоритм штопання зони уникнення” |
| format |
Article |
| author |
Vavilova, I. B. Elyiv, A. A. Vasylenko, M. Yu. |
| author_facet |
Vavilova, I. B. Elyiv, A. A. Vasylenko, M. Yu. |
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Vavilova, I. B. |
| title |
BEHIND THE ZONE OF AVOIDANCE OF THE MILKY WAY: WHAT CAN WE RESTORE BY DIRECT AND INDIRECT METHODS? |
| title_short |
BEHIND THE ZONE OF AVOIDANCE OF THE MILKY WAY: WHAT CAN WE RESTORE BY DIRECT AND INDIRECT METHODS? |
| title_full |
BEHIND THE ZONE OF AVOIDANCE OF THE MILKY WAY: WHAT CAN WE RESTORE BY DIRECT AND INDIRECT METHODS? |
| title_fullStr |
BEHIND THE ZONE OF AVOIDANCE OF THE MILKY WAY: WHAT CAN WE RESTORE BY DIRECT AND INDIRECT METHODS? |
| title_full_unstemmed |
BEHIND THE ZONE OF AVOIDANCE OF THE MILKY WAY: WHAT CAN WE RESTORE BY DIRECT AND INDIRECT METHODS? |
| title_sort |
behind the zone of avoidance of the milky way: what can we restore by direct and indirect methods? |
| title_alt |
ЗА ЗОНОЙ ИЗБЕГАНИЯ МЛЕЧНОГО ПУТИ: ЧТО МОЖНО ВОССОЗДАТЬ ПРЯМЫМИ И НЕПРЯМЫМИ МЕТОДАМИ? ЗА ЗОНОЮ УНИКНЕННЯ ЧУМАЦЬКОГО ШЛЯХУ: ЩО МОЖНА ВІДТВОРИТИ ПРЯМИМИ І НЕПРЯМИМ МЕТОДАМИ? |
| description |
PACS number: 98.35.-a °Purpose: to present a brief overview of methods for restoring the large-scale structure of the Universe behind the Zone of Avoidance (ZoA) of the Milky Way; to propose a new “algorithm of darning the ZoA” and new approach based on the Generative adversarial network (GAN) to recover galaxy distribution in the ZoA using optical surveys as an additional platform for programming the artificial neural networks.Design/methodology/approach: Due to the extensive monitoring observations in radio (DOGS project, in HI line), infrared (IRAS and 2MASS surveys), and X-ray spectral ranges, the ZoA has been decreased significantly in size and now the obscured part is about 10% of the sky in the visible spectral range. The Cosmic Microwave Background (CMB) measurements showed a 180° asymmetry known as the dipole: despite the fact that the resulting vector lies within 20° of the observed CMB dipole, the calculations remain highly ambiguous, partly because the galaxies in the ZoA are not taken into account and the concept of “attractors” should be reconsidered. Hence, the analysis of the spatial distribution of galaxies and their groups in the regions surrounding and behind the ZoA of Milky Way remains a complex and unresolved problem, and estimation of the “invisible” content of the spatial galaxy distribution, which is obscured by this absorption zone, becomes a highly actual one. Restoring the ZoA is possible by indirect methods (signal processing applied to obscured and incomplete data; Voronoi tessellation, etc.). These recovery methods, however, work only for large-scale structures in the ZoA; they are practically not sensitive to individual galaxies and small galaxy systems. We suggest the machine learning technique such as the GAN to apply for modeling the “invisible” spatial galaxy distribution behind the ZoA.Findings: We present “the algorithm of darning the ZoA” for dividing the real extragalactic surveys (e.g, the SDSS DR 14 galaxy sample) on the slices by redshifts, stellar magnitudes, coordinates and other parameters to form a training sample, and the general GAN scheme for the ZoA filling. We discuss principal tasks to generate galaxy distributions and their properties in the ZoA from latent space of features and describe how the discriminative network will compare the obtained artificial survey with the real one and evaluate how it is a realistic one.Conclusions: The incompleteness of data depending on wavelengths indicates that there are steal not resolved problems such as the dynamics in the Local Group and the near Universe; the large-scale structure of the Universe in the sky region obscured by the Milky Way; the velocity flow fields towards the Great Attractor; the CMB dipole. 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Видавничий дім «Академперіодика» |
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2018 |
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http://rpra-journal.org.ua/index.php/ra/article/view/1299 |
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oai:ri.kharkov.ua:article-12992020-06-09T10:31:20Z BEHIND THE ZONE OF AVOIDANCE OF THE MILKY WAY: WHAT CAN WE RESTORE BY DIRECT AND INDIRECT METHODS? ЗА ЗОНОЙ ИЗБЕГАНИЯ МЛЕЧНОГО ПУТИ: ЧТО МОЖНО ВОССОЗДАТЬ ПРЯМЫМИ И НЕПРЯМЫМИ МЕТОДАМИ? ЗА ЗОНОЮ УНИКНЕННЯ ЧУМАЦЬКОГО ШЛЯХУ: ЩО МОЖНА ВІДТВОРИТИ ПРЯМИМИ І НЕПРЯМИМ МЕТОДАМИ? Vavilova, I. B. Elyiv, A. A. Vasylenko, M. Yu. large-scale structure of the Universe; Milky Way; galaxies; galaxy clusters; zone of avoidance; machine learning; Generative adversarial network (GAN); “algorithm of darning the ZoA” крупномасштабная структура Вселенной; Млечный Путь; галактики; скопления галактик; зона избегания галактик; машинное обучение; генерирующая состязательная сеть (GAN); “алгоритм штопки зоны избегания” великомасштабна структура Всесвіту; Чумацький Шлях; галактики; скупчення галактик; зона уникнення галактик; машинне навчання; генеруюча змагальна мережа (GAN); “алгоритм штопання зони уникнення” PACS number: 98.35.-a °Purpose: to present a brief overview of methods for restoring the large-scale structure of the Universe behind the Zone of Avoidance (ZoA) of the Milky Way; to propose a new “algorithm of darning the ZoA” and new approach based on the Generative adversarial network (GAN) to recover galaxy distribution in the ZoA using optical surveys as an additional platform for programming the artificial neural networks.Design/methodology/approach: Due to the extensive monitoring observations in radio (DOGS project, in HI line), infrared (IRAS and 2MASS surveys), and X-ray spectral ranges, the ZoA has been decreased significantly in size and now the obscured part is about 10% of the sky in the visible spectral range. The Cosmic Microwave Background (CMB) measurements showed a 180° asymmetry known as the dipole: despite the fact that the resulting vector lies within 20° of the observed CMB dipole, the calculations remain highly ambiguous, partly because the galaxies in the ZoA are not taken into account and the concept of “attractors” should be reconsidered. Hence, the analysis of the spatial distribution of galaxies and their groups in the regions surrounding and behind the ZoA of Milky Way remains a complex and unresolved problem, and estimation of the “invisible” content of the spatial galaxy distribution, which is obscured by this absorption zone, becomes a highly actual one. Restoring the ZoA is possible by indirect methods (signal processing applied to obscured and incomplete data; Voronoi tessellation, etc.). These recovery methods, however, work only for large-scale structures in the ZoA; they are practically not sensitive to individual galaxies and small galaxy systems. We suggest the machine learning technique such as the GAN to apply for modeling the “invisible” spatial galaxy distribution behind the ZoA.Findings: We present “the algorithm of darning the ZoA” for dividing the real extragalactic surveys (e.g, the SDSS DR 14 galaxy sample) on the slices by redshifts, stellar magnitudes, coordinates and other parameters to form a training sample, and the general GAN scheme for the ZoA filling. We discuss principal tasks to generate galaxy distributions and their properties in the ZoA from latent space of features and describe how the discriminative network will compare the obtained artificial survey with the real one and evaluate how it is a realistic one.Conclusions: The incompleteness of data depending on wavelengths indicates that there are steal not resolved problems such as the dynamics in the Local Group and the near Universe; the large-scale structure of the Universe in the sky region obscured by the Milky Way; the velocity flow fields towards the Great Attractor; the CMB dipole. 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E-print arXiv.org. arXiv:1712.08955 PACS number: 98.35.-aПредмет и цель работы: представить краткий обзор методов, которые применяются для восстановления распределения крупномасштабных структур Вселенной за зоной избегания (ZoA) Млечного Пути; предложить новый “алгоритм штопки зоны избегания” и новый подход, основанный на Генерирующих состязательных сетях (GAN) для восстановления распределения галактик в ZoA с использованием оптических обзоров в качестве дополнительной платформы для программирования искусственных нейронных сетей.Методы и методология: Благодаря мониторинговым наблюдениям всего неба в радио (проект DOGS, наблюдение в линии HI), инфракрасном (IRAS и 2MASS обзоры) и рентгеновском спектральных диапазонах, ZoA “уменьшилась” в размерах и закрывает от наблюдателя около 10 % пространственного распределения галактик в оптическом диапазоне. Измерения реликтового излучения (CMB) показали асимметрию в 180°, известную как диполь: несмотря на то, что результирующий вектор находится в пределах 20° наблюдаемого диполя CMB, расчеты остаются весьма неоднозначными, отчасти потому, что не учитываются галактики в ZoA и концепция “аттракторов” требует пересмотра. На сегодняшний день анализ пространственного распределения галактик и их групп в областях, окружающих зону избегания галактик, остается сложной и нерешенной проблемой, а оценка “невидимого” пространственного распределения галактик, которое закрывает от наблюдателя зона поглощения, - крайне своевременной. Для восстановления распределения галактик в ZoA можно использовать косвенные методы, включая методы обработки сигналов, применяемые к скрытым и неполным данным; методы мозаики Вороного и т. д. Эти методы восстановления, однако, работают только для крупномасштабных структур в зоне избегания галактик; они практически не чувствительны к отдельным галактикам и малонаселенным скоплениям галактик. Одним из решений является использование методик машинного обучения, например GAN, для моделирования “невидимого” пространственного распределения галактик за этой зоной.Результаты: Мы предлагаем новый подход, названный нами “алгоритм штопки зоны избегания”, для разбивания существующих внегалактических обзоров (например, SDSS DR 14) на срезы в зависимости от красного смещения, звездных величин, координат и других параметров для формирования тренировочной выборки машинного обучения, а также описываем общую схему GAN метода для применения к восстановлению ZoA. Мы обсуждаем основные задачи генерирования искусственных распределений галактик и их свойств в ZoA и описываем, как дискриминационная сеть будет сравнивать полученное распределение с реальным и оценивать его реалистичность.Заключение: Неполнота данных, зависящая от длины волны, на которой проводились обзоры, говорит о том, что остались такие проблемы, как динамика Местной Группы и ближней Вселенной; крупномасштабная структура Вселенной в области неба, скрытой нашей Галактикой; поля потоков скоростей галактик к Великому Аттрактору; диполь CMB. Мы предлагаем новый “алгоритм штопки зоны избегания” и общую схему GAN в качестве дополнительной платформы машинного обучения для восстановления пространственного распределения за зоной избегания нашей Галактики.Ключевые слова: крупномасштабная структура Вселенной, Млечный Путь, галактики, скопления галактик, зона избегания галактик, машинное обучение, генерирующая состязательная сеть (GAN), “алгоритм штопки зоны избегания”Статья поступила в редакцию 19.10.2018Radio phys. radio astron. 2018, 23(4): 244-257СПИСОК ЛИТЕРАТУРЫ1. Kraan-Korteweg R. C. and Lahav O. The Universe behind the Milky Way. Astron. Astrophys. Rev. 2000. Vol. 10, No. 3. P. 211–261. DOI: 10.1007/s0015900000112. Maffei P. My Researches at the Infrared Doors. Mem. S. A. It. 2003. Vol. 74, No. 1. P. 19–28.3. Spinrad H., Sargent W. L. W., Oke J. B., Neugebauer G., Landau R., King I. R., Gunn J. E., Garmire G., and Dieter N. H. Maffei 1: a New Massive Member of the Local Group? Astrophys. J. 1971. Vol. 163. id. L25. 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Machine learning technique for morphological classification of galaxies at z<0.1 from the SDSS. E-print arXiv.org. 2017. arXiv:1712.08955 Предмет і мета роботи: подати короткий огляд методів, які застосовуються для відтворення розподілу великомасштабних структур Всесвіту за зоною уникнення (ZoA) Чумацького Шляху;запропонувати новий “алгоритм штопання зони уникнення” і новий підхід, що грунтується на генеруючій змагальній мережі (GAN) для відновлення розподілу галактик в ZoA з використанням оптичних оглядів у якості додаткової платформи для програмування штучних нейронних мереж.Методи і методологія: Завдяки моніторинговим спостереженнями всього неба в радіо (проект DOGS, спостереження в лінії НІ), інфрачервоному (IRAS та 2MASS огляди) і рентгенівському спектральних діапазонах, ZoA “зменшила” свої розміри і наразі закриває від спостерігача близько 10 % просторового розподілу галактик в оптичному діапазоні. Вимірювання реліктового випромінювання (CMB) показали асиметрію в 180°, відому як диполь: незважаючи на те, що результуючий вектор знаходиться в межах 20° спостережуваного диполя CMB, розрахунки залишаються досить неоднозначними, почасти тому, що не враховуються галактики в ZoA і концепція “атракторів” вимагає перегляду. Наразі аналіз просторового розподілу галактик і їх скупчень у областях, що оточують зону уникнення галактик, залишається складною і невирішеною проблемою, а оцінка “невидимого” просторового розподілу галактик, яке закриває від спостерігача зона поглинання, - є вкрай своєчасною. Для відновлення розподілу галактик в ZoA можливе використання непрямих методів, включаючи методи обробки сигналів, що застосовуються до прихованих і неповних даних; методи мозаїки Вороного тощо. Ці методи відновлення, проте, працюють тільки для великомасштабних структур в зоні уникнення галактик; вони практично не чутливі до окремих галактик і малонаселених скупчень галактик. Одним з рішень є використання методик машинного навчання, наприклад GAN, для моделювання “невидимого” просторового розподілу галактик за цією зоною.Результати: Ми пропонуємо новий підхід, названий нами “алгоритм штопання зони уникнення”, для розбивання існуючих позагалактичних оглядів (наприклад, SDSS DR 14) на зрізи залежно від червоного зміщення, зоряних величин, координатів та інших параметрів для формування тренувальної вибірки машинного навчання, а також описуємо загальну схему GAN методу для застосування до відновлення ZoA. Ми обговорюємо основні завдання генерування штучних розподілів галактик та їх властивостей в ZoA і описуємо, як дискримінаційна мережа буде порівнювати отриманий розподіл з реальним і оцінювати його реалістичність.Висновок: Неповнота даних, що залежить від довжини хвилі, на якій виконувалися огляди, свідчить про те, що залишилися такі проблеми, як динаміка Місцевої Групи і ближнього Всесвіту; великомасштабна структура Всесвіту в області неба, прихованою нашою Галактикою; поля потоків швидкості галактик до Великого Атрактора; диполь CMB. Ми пропонуємо новий “алгоритм штопання зони уникнення” і загальну схему GAN в якості додаткової платформи машинного навчання для відновлення просторового розподілу галактик за зоною уникнення нашої Галактики.Ключові слова: великомасштабна структура Всесвіту, Чумацький Шлях, галактики, скупчення галактик, зона уникнення галактик, машинне навчання, генеруюча змагальна мережа (GAN), “алгоритм штопання зони уникнення”Стаття надійшла до редакції 19.10.2018Radio phys. radio astron. 2018, 23(4): 244-257СПИСОК ЛІТЕРАТУРИ1. Kraan-Korteweg R. C. and Lahav O. The Universe behind the Milky Way. Astron. Astrophys. Rev. 2000. Vol. 10, No. 3. P. 211–261. DOI: 10.1007/s0015900000112. Maffei P. My Researches at the Infrared Doors. Mem. S. A. It. 2003. Vol. 74, No. 1. P. 19–28.3. Spinrad H., Sargent W. L. W., Oke J. B., Neugebauer G., Landau R., King I. R., Gunn J. E., Garmire G., and Dieter N. H. Maffei 1: a New Massive Member of the Local Group? Astrophys. J. 1971. Vol. 163. id. L25. 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Machine learning technique for morphological classification of galaxies at z<0.1 from the SDSS. E-print arXiv.org. 2017. arXiv:1712.08955 Видавничий дім «Академперіодика» 2018-12-03 Article Article application/pdf http://rpra-journal.org.ua/index.php/ra/article/view/1299 10.15407/rpra23.04.244 РАДИОФИЗИКА И РАДИОАСТРОНОМИЯ; Vol 23, No 4 (2018); 244 RADIO PHYSICS AND RADIO ASTRONOMY; Vol 23, No 4 (2018); 244 РАДІОФІЗИКА І РАДІОАСТРОНОМІЯ; Vol 23, No 4 (2018); 244 2415-7007 1027-9636 10.15407/rpra23.04 en http://rpra-journal.org.ua/index.php/ra/article/view/1299/pdf Copyright (c) 2018 RADIO PHYSICS AND RADIO ASTRONOMY |