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--- ml4gamma:wdocs:main [17/01/2026 15:53] – [Форматы данных] admin
+++ ml4gamma:wdocs:main [26/02/2026 09:11] (current) – [Нормализующие потоки] demichev
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 ==== Параметры Хилласа ====
-//Jashwanth S2 , Sudeep Ghosh1 , Kavitha Yogaraj1 , Neha Shah2 , and Ankhi Roy3. "G AMMA - HADRON S EPARATION IN I MAGING ATMOSPHERIC C HERENKOV T ELESCOPES USING Q UANTUM C LASSIFIERS". ArXiv:2210.03771//
-.2 Image parametrization
+  * {{ :ml4gamma:wdocs:2210.03771v1-gamma-hadron_separation.pdf |Jashwanth S, Sudeep Ghosh, Kavitha Yogaraj, Neha Shah, and Ankhi Roy. "G AMMA - HADRON S EPARATION IN I MAGING ATMOSPHERIC C HERENKOV T ELESCOPES USING Q UANTUM C LASSIFIERS"}}. ArXiv:2210.03771
+  * {{ :ml4gamma:wdocs:описание_параметров_хилласа.pdf |Описание параметров Хилласа}} (Е.Гресь)
+  * Вычисление {{ :ml4gamma:wdocs:iact_image_hp_calc.py.zip |параметров Хилласа для IACT}} (Е.Гресь)
-The hadronic shower images are observed to be more longer and broader compared to gamma ray shower images.
-Effective discrimination of primary gamma ray shower and hadronic background shower is possible on the basis of the width,length and orientation of these images. The showers with axis parallel to the optic axis and landing directly on the detector will produce circular images. But if it lands at some distance(impact parameter) away from the detector it will be a bivariate Gaussian distribution which is an elliptical cluster. For gamma ray showers the major axis is oriented towards the camera center whereas the hadronic showers have random axial orientation. The pixel image of the shower after some pre processing and image cleaning is then converted to into few image parameters, defined by moment analysis on the pixel signal amplitudes. The moments are defined as:
-(1)
-where, x and y are the coordinates of pixels and n is the number of digital counts in a pixel. The summation runs over all the pixels in the image.
-Figure 1: Definitions of some hillas parameters in the camera plane.
-Image spreads are derived from the moments in (1):
-(2)
-Image parameters can be derived from the moments and the image spreads in (2):
-(3)
-where, miss is the perpendicular distance between camera center and the major axis as shown in Fig. 1.
 ===== Multimodal =====
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   * {{ :ml4gamma:wdocs:nf_pres-2.pdf|Нормализующие потоки. Часть 2: Детектирование аномальных и редких явлений.}}
   * См. также {{ :ml4gamma:wdocs:understandingdeeplearning_08_28_24_c.pdf |Understanding Deep Learning}}
+  * {{ ::ml4gamma:wdocs:inn_inverseproblem.pdf |Inverse Problems with Invertible Neural Networks}}
+  * {{ :ml4gamma:meetings:imagedenoising.pdf |Disentangling Noise from Images: A Flow-Based Image Denoising Neural Network}}
+  * {{ ::ml4gamma:wdocs:freia.pdf |FrEIA: Framework for Easily Invertible Architectures}}
 ===== Domain Adaptation =====