1. Telescope Array 10-Year Monocular Spectrum MeasurementD. Bergman, Jon Paul Lundquist, 2022, published scientific conference contribution Abstract: Telescope Array (TA) is the largest cosmic ray detector in the northern hemisphere. We present a measurement of the cosmic ray energy spectrum for energies above 10^17.5 eV using fluorescence telescopes in monocular mode. A novel weather classification scheme using machine learning was used to select data parts with good weather to ensure the quality of the fluorescence data. The data from the Black Rock Mesa (BR) and Long Ridge (LR) fluorescence telescope stations were analysed separately in monocular mode, with the calculated fluxes combined into a single spectrum. The 10-year monocular combined cosmic ray energy spectrum is in excellent agreement with previous measurements from the northern hemisphere. We present fits of the combined spectrum to a series of broken power law models. A thrice-broken power law is observed to be the best fit considering the Poisson deviance per degrees of freedom. The three breaks suggest an additional feature of the spectrum between the previously observed Ankle at 10^18.7 eV and the GZK suppression at 10^19.8 eV.
Keywords: Telescope Array, indirect detection, fluorescence detection, ultra-high energy, cosmic rays, energy spectrum, composition, machine learning, weather classification
Published in RUNG: 02.10.2023; Views: 301; Downloads: 5
 Full text (1,91 MB)
This document has many files! More... |
2. Investigating the VHE Gamma-ray Sources Using Deep Neural NetworksVeronika VODEB, Saptashwa Bhattacharyya, Giacomo Principe, Gabrijela Zaharijaš, Roberto Ruiz Austri, Fiorenzo Stoppa, Sascha Caron, Dmitry Malyshev, 2023, published scientific conference contribution Abstract: The upcoming Cherenkov Telescope Array (CTA) will dramatically improve the point-source sensitivity compared to the current Imaging Atmospheric Cherenkov Telescopes (IACTs). One of the key science projects of CTA will be a survey of the whole Galactic plane (GPS) using both southern and northern observatories, specifically focusing on the inner galactic region. We extend a deep learning-based image segmentation software pipeline (autosource-id) developed on Fermi-LAT data to detect and classify extended sources for the simulated CTA GPS. Using updated instrument response functions for CTA (Prod5), we test this pipeline on simulated gamma-ray sources lying in the inner galactic region (specifically 0∘Keywords: Deep Neural Network, Cosmic-rays, CTA, Classification
Published in RUNG: 31.08.2023; Views: 257; Downloads: 3
Full text (962,45 KB)
This document has many files! More... |
3. Click-through rate estimation using CHAID classification tree model : case study of direct benefit transfer in IndiaRajan Gupta, Saibal K. Pal, 2019, published scientific conference contribution Abstract: Click-Through Rate (CTR) is referred to as the number of clicks on a particular advertisement as compared to the number of impressions on it. It is an important measure to find the effectiveness of any online advertising campaign. The effectiveness of online advertisements through calculations of ROI can be done through the measurement of CTR. There are multiple ways of detecting CTR in past; however, this study focuses on machine learning based classification model. Important parameters are judged on the basis of user behavior toward online ads and CHAID tree model is used to classify the pattern for successful and unsuccessful clicks. The model is implemented using SPSS version 21.0. The dataset used for the testing has been taken from Kaggle website as the data is from anonymous company’s ad campaign given to Kaggle for research purpose. A total of 83.8% accuracy is reported for the classification model used. This implies that CHAID can be used for less critical problems where very high stakes are not involved. This study is useful for online marketers and analytics professionals for assessing the CHAID model’s performance in online advertising world.
Keywords: click-through rate, online advertisements, classification tree, mobile ads, click estimation
Published in RUNG: 02.04.2021; Views: 1422; Downloads: 13
 Link to full text
This document has many files! More... |
4. Exploring deep learning as an event classification method for the Cherenkov Telescope ArrayD. Nieto, Christopher Eckner, Gašper Kukec Mezek, Samo Stanič, Serguei Vorobiov, Lili Yang, Gabrijela Zaharijas, Danilo Zavrtanik, Marko Zavrtanik, 2017, published scientific conference contribution Keywords: CTA, event classification, deep learning
Published in RUNG: 16.02.2018; Views: 3131; Downloads: 143
Full text (313,07 KB) |
5. |
6. |
7. |
