1. In situ identification of aerosol types in Athens, Greece, based on long-term optical and on online chemical characterizationDimitris G. Kaskaoutis, Georgios Grivas, Iasonas Stavroulas, Eleni Liakakou, Umesh Chandra Dumka, Konstantinos Dimitriou, Evangelos Gerasopoulos, Nikolaos Mihalopoulos, 2021, original scientific article Abstract: Absorption Ångström Exponent (AAE) and Scattering Ångström Exponent (SAE) values, derived from aethalometer and nephelometer measurements during a period of 3 years at an urban background site in Athens, are combined for the first aerosol type classification using in situ measurements in the eastern Mediterranean. In addition, single scattering albedo (SSA) and its wavelength dependence (dSSA), as well as the chemical composition of fine aerosols and precursor gases from collocated measurements, are utilized to provide further insights on the optical-chemical characterization and related sources of seven identified aerosol types. Urban aerosols are mostly characterized as Black Carbon (BC)-dominated (76.3%), representing a background atmosphere where fossil-fuel combustion is dominant throughout the year, while 14.3% of the cases correspond to the mixed Brown Carbon (BrC)-BC type, with a higher frequency in winter. The BrC type is associated with the highest scattering and absorption coefficients during winter nights, representing the impact from residential wood-burning emissions. Dust mixed with urban pollution (1.2%) and large particles mixed with BC (5.3%) have a higher frequency in spring. Furthermore, aging processes and BC coating with organic and inorganic species with weak spectral absorption (AAE<1) account for 2.2%, with a differentiation between small and large particles. dSSA is recognized as a useful parameter for aerosol characterization, since fine aerosols are associated with negative dSSA values. The identified aerosol types are examined on a seasonal, monthly, hourly basis and by potential source areas, as well as in comparison with fine-aerosol chemical composition and apportioned organic aerosol source contributions, in an attempt to explore the linkage between optical, physical and chemical aerosol properties. Chemical analysis indicates high organic fraction (60–68%) for the BrC and BrC/BC, 20–30% larger compared to other types. The results are essential for parametrization in chemical transport models and for reducing the uncertainty in the assessment of aerosol radiative effects.
Keywords: aerosol types, classification, AAE, SAE, dSSA, chemical composition, sources, Athens
Published in RUNG: 10.05.2024; Views: 130; Downloads: 2
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2. 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: 738; Downloads: 5
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3. Investigating the VHE gamma-ray sources using deep neural networksVeronika Vodeb, Saptashwa Bhattacharyya, G. Principe, Gabrijela Zaharijas, R. Austri, F. Stoppa, S. Caron, D. 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: 870; Downloads: 6
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4. 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: 1716; Downloads: 14
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5. 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: 3502; Downloads: 145
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