1. Machine learning-based analyses using surface detector data of the Pierre Auger ObservatorySteffen Hahn, Andrej Filipčič, Jon Paul Lundquist, Shima Ujjani Shivashankara, Samo Stanič, Serguei Vorobiov, Danilo Zavrtanik, Marko Zavrtanik, 2025, objavljeni znanstveni prispevek na konferenci Opis: The Pierre Auger Observatory is the largest detector for the study of extensive air showers induced
by ultra-high-energy cosmic rays (UHECRs). Its hybrid detector design allows the simultaneous
observation of different parts of the shower evolution using various detection techniques. To
accurately understand the physics behind the origin of UHECRs, it is essential to determine their
mass composition. However, since UHECRs cannot be measured directly, estimating their masses
is highly non-trivial. The most common approach is to analyze mass-sensitive observables, such as
the number of secondary muons and the atmospheric depth of the shower maximum.
An intriguing part of the shower to estimate these observables is its footprint. The shower footprint is detected by ground-based detectors, such as the Water-Cherenkov detectors (WCDs) of the Surface Detector (SD) of the Observatory, which have an uptime of nearly 100%, resulting in a high number of observed events. However, the spatio-temporal information stored in the shower footprints is highly complex, making it very challenging to analyze the footprints using analytical and phenomenological methods. Therefore, the Pierre Auger Collaboration utilizes machine learning-based algorithms to complement classical methods in order to exploit the measured data with unprecedented precision. In this contribution, we highlight these machine learning-based analyses used to determine high-level shower observables that help to infer the mass of the primary particle, with a particular focus on analyses using the shower footprint detected by the WCDs and the Surface Scintillator Detectors (SSD) of the SD. We show that these novel methods show promising results on simulations and offer improved reconstruction performance when applied to measured data.
Ključne besede: ultra-high-energy cosmic rays (UHECRs), extensive air showers, Pierre Auger Observatory, surface detector, Water-Cherenkov detectors (WCDs), Surface Scintillator Detectors (SSDs), UHECR mass composition, air-shower footprint, machine learning
Objavljeno v RUNG: 16.05.2025; Ogledov: 138; Prenosov: 4
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2. Metagenomics studies in aquaculture systems : big data analysis, bioinformatics, machine learning and quantum computingOrkid Coskuner-Weber, Semih Alpsoy, Ozgur Yolcu, Egehan Teber, Ario De Marco, Spase Shumka, 2025, pregledni znanstveni članek Ključne besede: marine biology, machine learning, diagnostics
Objavljeno v RUNG: 07.04.2025; Ogledov: 373; Prenosov: 0
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4. Inference of the Mass Composition of Cosmic Rays with Energies from 10[sup]18.5 to 10[sup]20 eV Using the Pierre Auger Observatory and Deep LearningA. Abdul Halim, Andrej Filipčič, Jon Paul Lundquist, Shima Ujjani Shivashankara, Samo Stanič, Serguei Vorobiov, Danilo Zavrtanik, Marko Zavrtanik, 2025, izvirni znanstveni članek Opis: We present measurements of the atmospheric depth of the shower maximum Xmax,
inferred for the first time on an event-by-event level using the Surface Detector
of the Pierre Auger Observatory. Using deep learning, we were able to extend
measurements of the Xmax distributions up to energies of 100 EeV (10[sup]20 eV),
not yet revealed by current measurements, providing new insights into the mass
composition of cosmic rays at extreme energies.
Gaining a 10-fold increase in statistics compared to the Fluorescence Detector data,
we find evidence that the rate of change of the average Xmax with the logarithm
of energy features three breaks at 6.5 ± 0.6 (stat) ± 1 (sys) EeV,
11 ± 2 (stat) ± 1 (sys) EeV, and 31 ± 5 (stat) ± 3 (sys) EeV, in the vicinity to the three
prominent features (ankle, instep, suppression) of the cosmic-ray flux.
The energy evolution of the mean and standard deviation of the measured Xmax
distributions indicates that the mass composition becomes increasingly heavier
and purer, thus being incompatible with a large fraction of light nuclei between
50 EeV and 100 EeV.
Ključne besede: ultra-high-energy cosmic rays (UHECRs), extensive air showers, Pierre Auger Observatory, UHECR mass composition, depth of the shower maximum, fluorescence detector, surface detector, deep learning
Objavljeno v RUNG: 20.01.2025; Ogledov: 752; Prenosov: 5
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5. Measurement of the depth of maximum of air-shower profiles with energies between ▫$10^{18.5} and 10^{20}$▫ eV using the surface detector of the Pierre Auger Observatory and deep learningA. Abdul Halim, P. Abreu, M. Aglietta, Andrej Filipčič, Jon Paul Lundquist, Shima Ujjani Shivashankara, Samo Stanič, Serguei Vorobiov, Danilo Zavrtanik, Marko Zavrtanik, 2025, izvirni znanstveni članek Opis: We report an investigation of the mass composition of cosmic rays with energies
from 3 to 100 EeV (1 EeV = 10[sup]18 eV) using the distributions of the depth of shower
maximum Xmax. The analysis relies on ∼50,000 events recorded by the surface detector
of the Pierre Auger Observatory and a deep-learning-based reconstruction algorithm.
Above energies of 5 EeV, the dataset offers a 10-fold increase in statistics with respect to
fluorescence measurements at the Observatory. After cross-calibration using
the fluorescence detector, this enables the first measurement of the evolution of the mean
and the standard deviation of the Xmax distributions up to 100 EeV.
Our findings are threefold: (i) The evolution of the mean logarithmic mass toward a heavier
composition with increasing energy can be confirmed and is extended to 100 EeV.
(ii) The evolution of the fluctuations of Xmax toward a heavier and purer composition
with increasing energy can be confirmed with high statistics. We report a rather heavy
composition and small fluctuations in Xmax at the highest energies.
(iii) We find indications for a characteristic structure beyond a constant change
in the mean logarithmic mass, featuring three breaks that are observed in proximity
to the ankle, instep, and suppression features in the energy spectrum.
Ključne besede: ultra-high-energy cosmic rays, UHECRs, extensive air showers, Pierre Auger Observatory, UHECR mass composition, depth of shower maximum, fluorescence detector, surface detector, deep learning
Objavljeno v RUNG: 20.01.2025; Ogledov: 782; Prenosov: 8
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6. P856 : a single-cell functional precision medicine landscape of multiple myelomaKlara Kropivšek, Paul Kachel, Sandra Goetze, Rebekka Wegmann, Yannik Severin, Benjamin D. Hale, Yasmin Festl, Julien Mena, Audrey Van Drogen, Nadja Dietliker, 2022, objavljeni povzetek znanstvenega prispevka na konferenci Opis: Multiple myeloma (MM) is a cancer of plasma cells, defined by complex genetics and extensive intra- and inter-patient heterogeneity. Despite improved patient survival driven by a plethora of treatment options, the disease remains incurable.
Molecularly-guided precision medicine to individualize treatment strategies in MM has had limited success, in part due to the genetic and molecular complexity of the disease. Functional precision medicine, a complementary approach in which patient treatment is guided by the ex vivo drug response of patient cells, has not yet been evaluated for MM systematically.
Ključne besede: mutliple myeloma, hematology, precision medicine, microscopy, deep learning, phenotyping, oncology, proteotype
Objavljeno v RUNG: 11.11.2024; Ogledov: 844; Prenosov: 4
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7. A roadmap of therapeutic strategies for patients with multiple myelomaBerend Snijder, Klara Kropivšek, 2023, drugi znanstveni članki Opis: Multiple myeloma is a rare and incurable cancer of plasma cells. To characterize this cancer, we developed an ex vivo drug screening method that combines imaging, deep learning and multiomics and applied it in an observational trial, uncovering new potential therapeutic strategies and underlying disease mechanisms.
Ključne besede: multiple myeloma, multiomics, deep learning, imaging, ex vivo drug screening
Objavljeno v RUNG: 11.11.2024; Ogledov: 851; Prenosov: 6
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8. Search for a signal from dark matter sub-halos with the galactic plane survey of CTA Observatory : master's thesisZoja Rokavec, 2024, magistrsko delo Opis: Dark matter (DM), known to be a dominant matter component in the Universe,
has been searched for extensively, yet remains undetected. One of the promising
avenues of detecting a DM signal is to observe the so called ’DM sub-halos’ within
our galaxy. These sub-halos, which are numerous within the Milky Way, are formed
by the clustering of DM, as predicted by cosmological simulations, and most of
them lack baryonic matter counterparts, making them challenging to detect. How-
ever, the annihilation or decay of Weakly Interacting Massive Particles (WIMPs),
a leading candidate for DM, within these sub-halos is expected to produce very
high-energy (VHE) photons (called gamma-rays) at TeV energies, offering possible
indirect DM detection.
In this thesis, we focus on the Galactic Plane Survey (GPS) of the Cherenkov Tele-
scope Array Observatory (CTAO), an upcoming ground-based gamma-ray obser-
vatory, which promises unprecedented sensitivity and resolution in the detection
of cosmic gamma-ray sources in the ∼ 30 GeV to ∼ 100 TeV energy range. As
dark sub-halos are expected to appear as unidentified (point) sources in the CTAO
GPS data, we employ a machine learning (ML)-based approach, the AutoSour-
ceID framework, leveraging U-shaped networks (U-Nets) and Laplacian of Gaus-
sian (LoG) filter, for automatic source detection and localization, and apply it to
simulated GPS data. We establish detection thresholds for U-Nets trained on dif-
ferently scaled counts (counts, square root or log of counts) and identify which
approach offers best results (in terms of flux sensitivity and location accuracy).
Our findings suggest that using log-scaled counts yields a factor of 1.7 lower flux
threshold compared to counts alone. In addition, we compare our ML outcomes
with traditional methods; however, this comparison is not straightforward, as ML
and traditional approaches fundamentally differ in their methodologies and un-
derlying assumptions. Nevertheless, The flux threshold obtained using log-scaled
counts is comparable to that of the traditional likelihood-based detection method
implemented in the Gammapy library, although further study is needed to estab-
lish a more definitive comparison. These preliminary results also suggest that the
flux threshold for detecting 90% of true sources with the ML approach is approx-
imately two times lower than the sensitivity reported for the GPS in the CTAO
publication. Although these results are not directly comparable due to differences
in methodology, they hint that ML methods may offer superior performance in
certain scenarios. Furthermore, we discuss the implications of our results on the
sensitivity to DM sub-halos, improving it by a factor of 4, highlighting the possi-
bility of detecting at least one sub-halo with a cross section approximately ⟨σv⟩ =
2.4 × 10−23 cm3 /s.
Ključne besede: Cherenkov Telescope Array Observatory, dark matter, sub-halos, machine learning, gamma-rays, master's thesis
Objavljeno v RUNG: 06.09.2024; Ogledov: 1352; Prenosov: 20
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9. Promoting the use of open educational resources to improve teaching and learning of science subjects in secondary schools in Tanzania : master's thesisLucian Vumilia Ngeze, 2024, magistrsko delo Opis: A number of challenges have hindered the integration of Open Educational Resources (OERs) in schools from developing countries. The rate of adoption of OERs in teaching and learning in schools in Tanzania is low. This research focused on capacity of secondary school science teachers on creating and adapting open educational resources to improve the teaching and learning of science subjects. The research used a Design-based Research methodology to achieve research objectives.
Results show that challenges such as lack of ICT devices, poor Internet connection, network accessibility issues, unstable power supply and large class sizes hindered the integration of OER in the teaching of science subjects. As teachers created OERs, they stated factors such as levels of the learners, developing engaging content, simple and self-explanatory content, alignment with learning objectives and relevancy of OER as initial considerations they considered when creating OERs. It was important to investigate the change in teachers’ attitude towards the use of OER in teaching science subjects OER creation ability, OER in teaching, teaching improvements, increased teaching resources, and application of skills.
It was concluded that regular teacher professional development programmes must be set to support school teachers in using OERs to improve teaching and learning and in creating OER for teaching. Engaging with relevant government bodies is encouraged to ensure that more teachers are involved in such developed online courses.
Ključne besede: Open Educational Resources, OER in Teaching and Learning, Online Courses, Design Based Research
Objavljeno v RUNG: 02.08.2024; Ogledov: 2561; Prenosov: 23
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