In the past few years, deep-learning-based algorithms have been extraordinarily successful across many domains, including computer vision, machine translation, engineering, and science. Also, in physics, applications are accumulating due to the need for fast and precise algorithms that are able to exploit huge amounts of data. So, could it even become a new paradigm for data-driven knowledge...
The field of deep learning has become increasingly important for particle physics experiments, yielding a multitude of advances, predominantly in event classification and reconstruction tasks. Many of these applications have been adopted from other domains. However, data in the field of physics are unique in the context of machine learning, insofar as their generation process and the laws and...
The IceCube Neutrino Observatory, located at the South Pole, is a multi-component detector that detects high-energy particles from astrophysical sources. Cosmic Rays (CRs) are charged particles from these astrophysical accelerators. CRs and CR-induced air-showers furnish us with the possibility to discern the fundamental properties and behavior of such sources. When coupled to the IceTop...
The IceTop and IceCube detectors at the South Pole provide the opportunity to simultaneously measure the electromagnetic and low-energy muonic component of a cosmic-ray air shower at the surface, and the penetrating muons in the deep ice. Various properties of the bundle of muons above several 100 GeV measured in IceCube are sensitive to the mass of the primary cosmic ray and contain...
The IceAct telescopes are prototype Imaging Air Cherenkov telescopes (IACTs) situated at the IceCube Neutrino Observatory at the geographic South Pole. The telescopes camera consist of 61 silicon photomultipliers (SiPMs) with a hexagonal light guide glued to each SiPM. The IceAct telescopes measure the electromagnetic air shower component of cosmic rays in the atmosphere, which...
The IceAct telescopes are prototype Imaging Air Cherenkov telescopes (IACTs) situated at the IceCube Neutrino Observatory at the geographic South Pole. The IceAct telescopes measure the electromagnetic air shower component of cosmic rays in the atmosphere, which is complementary to the muonic component measured by the IceCube in-ice detector and the particle footprint measured at the surface...
The flux of Galactic cosmic rays at Earth is modulated by the long term magnetic variations of the Sun (11-year sunspot cycle and 22-year magnetic solar cycle). This process known as Solar modulation is most pronounced at 1 GeV and below. However, it also operates at much higher energy, still exhibiting solar magnetic polarity dependence. For the last decades, ground-based neutron monitors...
IceTop is the surface component of the IceCube South Pole Neutrino Observatory and dedicated to the indirect detection of cosmic rays (CRs). The recent implementation of a new trigger that only requires 2 of IceTop's 6 central infill stations hit by a CR-induced air shower allowed to reduce the primary energy threshold for the detection of low-energy CRs from 1.6 PeV to 250 TeV. This lead to a...
The IceCube Neutrino Observatory at the South Pole is capable of measuring two components of the cosmic rays air shower. The electromagnetic component using a km2 surface array IceTop, and the high-energy muonic component using km3 in-ice array IceCube between 1.5 and 2.5 km below the surface. The combination of both arrays in conjunction with a new flexible curvature and new timing...
IceTop, the surface component of the IceCube Neutrino Observatory, consists of 81 stations that detect air showers produced by cosmic ray interactions with the atmosphere. An accurate energy estimator for IceTop is essential for studying the nature of the cosmic ray spectrum around the knee (300 TeV - 1 EeV). Using over 400,000 simulated events, we trained an array of convolutional deep neural...
The surface detector of the Telescope Array (TA) experiment is the largest one in the northern hemisphere. We overview the machine learning based event reconstruction methods being developed by the TA collaboration. The key idea is to use full detector Monte Carlo simulation to obtain the raw detector signal as a function of the primary particle properties and to train deep convolutional...
Once again, the last several years reshaped the state-of-the-art in Computer Vision (CV). Non-convolutional approaches, such as Vision Transformers (ViT) and self-attention multi-layer perceptrons (SA-MLP), are quickly emerging, combined with novel optimization techniques and pre-training methods. Note that ViTs and SA-MLPs are evidently better at incorporating global information about the...
The measurement of the mass composition of ultra-high energy cosmic rays constitutes one of the biggest challenges in astroparticle physics. Detailed information on the composition can be obtained from measurements of the depth of maximum of air showers, Xmax, with the use of fluorescence telescopes, which can be operated only during clear and moonless nights.
Using deep neural networks, it...
We present a method based on the use of Recurrent Neural Networks to extract the muon component from the time traces registered with water-Cherenkov detector (WCD) stations of the Surface Detector of the Pierre Auger Observatory. With the current design of the WCDs it is not straightforward to separate the contribution of muons to the time traces from those of photons, electrons and positrons...
The Extreme Universe Space Observatory Super Pressure Balloon 2 (EUSO-SPB2) is under development, and will prototype instrumentation for future satellite-based missions, including the Probe of Extreme Multi-Messenger Astrophysics (POEMMA). EUSO-SPB2 will consist of two telescopes. The first is a Cherenkov telescope (CT) being developed to identify and estimate the background sources for future...
In order to properly train neural networks to analyze air shower data, it is necessary to have accurate simulations providing the necessary level of details required to extract the required information. The most popular tool is certainly the current version of CORSIKA and its fast option for 1D simulation CONEX. We will present the basic principles of these tools and how to use them properly....
The proliferation of innovative next-generation cosmic ray and neutrino observatories, with unique geometries (Earth-skimming, orbital, in-ice, etc.), and detection techniques (Cherenkov, radio, radar, etc.), requires the simulation of ultrahigh energy particle cascades which are challenging, if not impossible, to perform with current simulation tools like CORSIKA 7 and AIRES. These...
The use of computational algorithms, implemented on a computer, to extract information from data has a history that dates back to at least the middle of the 20th century. However, the confluence of three recent developments has led to rapid advancements in this methodology over the past 15-20 years: the advent of the era of large datasets in which massive of amounts of data can be collected,...
Baikal-GVD is a large-scale underwater neutrino telescope currently under construction in Lake Baikal. Its principal component is a three-dimensional array of optical modules (OMs) registering Cherenkov light associated with the neutrino-induced particles. The OMs are organized in clusters, each containing 8 vertical strings with 36 OMs per string.
Located in a natural water reservoir, the...
KASCADE was an air-shower detector located in Karlsruhe Institute of Technology. It consisted of scintillating detectors which were arranged in a 16×16 grid and recorded signals from secondary particles of air-showers. Data has been acquired from 1996 till 2013 and then has been made available online. Our goal is to find out, whether we can accurately reconstruct the initial particle by that...
CTLearn is a project that aims at IACT event reconstruction through the usage deep-learning models. The associated software packages include modules for loading and manipulating IACT data, and handling the training and test of deep-learning architectures with TensorFlow, using pixel-wise camera data as input. In this contribution we will comment on the challeges we faced so far, the lessons...
We focus on the novel data analysis from KASCADE, one of the most successful cosmic ray detectors in the >PeV range. The detector operated for about 15 years, its data are publicly accessible. The data archive includes about half a billion recorded air showers. Extensive air showers generated by ultrahigh-energy gamma-rays (not detected at the moment) are of particular research interest, since...
The IceCube Neutrino Observatory is a unique experiment located at the geographic South Pole. It is composed of two detectors: an optical array deep in the ice and an array of ice-Cerenkov tanks at the surface called IceTop. The combination of the two detectors can be exploited for the study of cosmic rays and the search for PeV photons. In particular, the in-ice detector measures the...
Nowadays the deep learning techniques are broadly applied for the processing of radio signals generated in air-showers. The majority of the implementations are based on the convolutional neural networks (CNN) running of 1D arrays containing finite waveforms with radio impulses. This approach has shown its feasibility and is able to be implemented for the both trigger- and high- levels of data...
Radio emission, produced mainly as a result of the geomagnetic deflection of oppositely charged particles within the cosmic-ray air showers, is contaminated by backgrounds such as the continuous Galactic background and thermal noise. This irreducible background poses a significant challenge for radio detection of air showers. To mitigate this effect of background we employ machine learning...
Cosmic-ray air showers produce radio signals which can be detected from Earth’s surface. However, the radio background that is detected along with these signals can make it difficult to identify an air shower signal from the local background. To solve this problem, this project aims to train two convolutional neural networks (CNNs): a “classifier” and a “denoiser”. The classifier distinguishes...
In this presentation, I will describe the Zooniverse.org citizen science platform as a tool to gather labels from over 2.5 million dedicated volunteers worldwide who are motivated to participate in scientific research. Hundreds of research teams now turn to Zooniverse for crowdsourcing tasks such as image classification and annotation which provide the large labeled data sets needed for...
Cosmic ray analysis relies on multiple steps including calibration, simulation, event reconstruction and interpretation, etc. Because of their broad energy coverage and sophisticated analysis and simulation techniques, large cosmic ray projects often suffer from their science analysis falling behind data collection. This challenge may be more severe in next generation multi-messenger...
Summary of the 3-day workshop and outlook into the future of cosmic rays analysis using state of the art machine learning techniques.
