Related papers: Gamma/hadron segregation for a ground based imagin…
This dissertation presents a unified framework for medium characterization with hard probes spanning from Cherenkov light in quantum electrodynamics (QED) to jet drift in quantum chromodynamics (QCD). We first develop a dispersive fit to…
Data from high-energy observations are usually obtained as lists of photon events. A common analysis task for such data is to identify whether diffuse emission exists, and to estimate its surface brightness, even in the presence of point…
Imaging Atmospheric Cherenkov Telescopes (IACTs) detect very-high-energy gamma rays from ground level by capturing the Cherenkov light of the induced particle showers. Convolutional neural networks (CNNs) can be trained on IACT camera…
Gene selection is an important part of microarray data analysis because it provides information that can lead to a better mechanistic understanding of an investigated phenomenon. At the same time, gene selection is very difficult because of…
We present a novel unsupervised learning approach to automatically segment and label images in astronomical surveys. Automation of this procedure will be essential as next-generation surveys enter the petabyte scale: data volumes will…
Isotopic composition measurements of singly charged cosmic rays (CR) provide essential insights into CR transport in the Galaxy. The Alpha Magnetic Spectrometer (AMS-02) can identify singly charged isotopes up to about 10 GeV/n. However,…
Reconstruction of energies of very-high-energy gamma-rays observed by imaging atmospheric Cherenkov telescopes is affected by changes in the atmospheric conditions and the performance of telescope components. Reliable calibration schemes…
This work presents a method for semantic segmentation of mango trees in high resolution aerial imagery, and, a novel method for individual crown detection of mango trees using segmentation output. Mango Tree Net, a fully convolutional…
Intrinsic differences in the processes involved in the development of electromagnetic and hadronic showers in the atmosphere have been evidenced by means of a careful analysis of the event image given by the ARGO-YBJ detector. The images…
In light of the recent advancements in machine learning, we propose a novel approach to neutron source distribution estimation through the utilisation of probabilistic generative models. The estimation is based on a Monte Carlo particle…
Cherenkov imaging enables real-time visualization of megavoltage X-ray or electron beam delivery to the patient during Radiation Therapy (RT). Bio-morphological features, such as vasculature, seen in these images are patient-specific…
In this work, we present a random forest framework that learns the weights, shapes, and sparsities of feature representations for real-time semantic segmentation. Typical filters (kernels) have predetermined shapes and sparsities and learn…
Imaging Atmospheric Cherenkov Telescopes (IACTs) of gamma ray observatory TAIGA detect the Extesnive Air Showers (EASs) originating from the cosmic or gamma rays interactions with the atmosphere. Thereby, telescopes obtain images of the…
Imaging Atmospheric Cherenkov Telescopes (IACTs) are ground-based indirect detectors for cosmic gamma rays with energies above tens of GeV. The major backgrounds for gamma-ray observations in IACTs are cosmic-ray charged particles. The…
The Cherenkov Telescope Array (CTA) is a future ground-based gamma-ray observatory that will provide unprecedented sensitivity and angular resolution for the detection of gamma rays with energies above a few tens of GeV. In comparison to…
Remote sensing of atmosphere is conventionally done via a study of extinction / scattering of light from natural (Sun, Moon) or artificial (laser) sources. Cherenkov emission from extensive air showers generated by cosmic rays provides one…
This paper is devoted to the problem of detection of forest and non-forest areas on Earth images. We propose two statistical methods to tackle this problem: one based on multiple hypothesis testing with parametric distribution families,…
The HAWC observatory reconstructs atmospheric showers induced by very high-energy primary cosmic- or gamma-rays. Cosmic-rays with hadronic nature are several orders of magnitude more frequent than primary gammas and therefore it is…
In the current study we examine an application of the machine learning methods to model the retention constants in the thin layer chromatography (TLC). This problem can be described with hundreds or even thousands of descriptors relevant to…
Conventional seismic techniques for detecting the subsurface geologic features are challenged by limited data coverage, computational inefficiency, and subjective human factors. We developed a novel data-driven geological feature detection…