Машинне навчання для морфологічної класифікації галактик із огляду SDSS. II. Морфологічні каталоги зображень галактик на 0,02<z<0,1
DOI:
https://doi.org/10.15407/knit2022.01.003Ключові слова:
великомасштабна структура Всесвіту, згорткові нейронні мережі; галактики: морфологічна класифікація, зображення галактик, каталоги галактик, машинне навчання, Методи: аналіз данихАнотація
Ми застосували згорткову нейронну мережу (CNN) до вибірки зображень галактик на малих червоних зміщеннях із –24m<Mr<–19.4m огляду неба SDSS DR9. Ми розділили її на дві підвибірки галактик SDSS DR9 і галактик Galaxy Zoo 2 (GZ2), розглядаючи їх як цільову з невідомими параметрами (inference) та навчальну (training), відповідно. Щоб визначити основні морфологічні параметри галактик, визначені в рамках проекту GZ2, ми класифікували галактики на п’ять візуальних класів (повністю заокруглені, майже заокруглені, гладкі сигароподібні, видимі з ребра, спіральні). Використовуючи класифікацію морфології галактик GZ2, ми також визначити 34 морфологічні характеристики галактик із вибірки SDSS DR9, які не збігаються з навчальною підвибіркою галактик GZ2. У результаті ми створили морфологічний каталог зображень 315782 галактик на 0,02<z<0,1, де морфологічні п’ять класів і 34 детальні характеристики були вперше визначені для 216148 галактик із застосуванням CNN класифікатора. Для решти галактик початкову морфологічну класифікацію було перевизначено, як у проекті GZ2. Розроблений нами метод демонструє багатообіцяючу ефективність морфологічної класифікації, що досягає >93 % точності для прогнозування морфології п’яти класів, за винятком сигароподібних (~75 %) та повністю округлених (~ 83 %) галактик. В результаті були отримані каталоги 19468 повністю заокруглених, 27321 майже закруглених, 3235 сигароподібних, 4099 видимих з ребра, 18615 спіральних та 72738 інших галактик на малих червоних зміщеннях досліджуваної вибірки SDSS. Що стосується класифікації галактик за їх детальними структурними морфологічними особливостями, то наша модель CNN дає точність у діапазоні 92–99 % залежно від морфологічної ознаки та якості зображення галактики. Створено каталоги, де вперше 34 детальні морфологічні особливості (бар, кільця, кількість спіральних рукавів, злиття тощо) визначено для понад 160000 галактик цільової підвибірки SDSS DR9. Ми вперше показуємо, що застосування моделі CNN зі змагальною валідацією та математичними перетвореннями зображень галактик покращує класифікацію менших за розмірами та слабкіших mr <17.7 галактик SDSS. Запропонована модель CNN дозволяє вирішити різні проблеми класифікації галактик, наприклад, таких як швидкий відбір галактик із баром, балджем, кільцем та іншими морфологічними особливостями для їх подальшого аналізу.Посилання
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