Related papers: Deep Learning and IACT: Bridging the gap between M…
Real world applications of stereo depth estimation require models that are robust to dynamic variations in the environment. Even though deep learning based stereo methods are successful, they often fail to generalize to unseen variations in…
The next generation of observatories will facilitate the discovery of new types of astrophysical transients. The detection of such phenomena, whose characteristics are presently poorly constrained, will hinge on the ability to perform blind…
The Cherenkov Telescope Array Observatory (CTAO) will greatly improve upon sensitivities in the field of very-high-energy gamma-ray astrophysics. The CTAO northern site (CTAO-North, La Palma, Spain) currently hosts LST-1 with the remaining…
Gamma-ray astronomy from hundreds of GeV to PeV is confined to ground-based experiments that detect air showers induced by $\gamma$-rays entering Earth's atmosphere. While particle detector arrays feature huge detection areas, accurately…
Imaging Atmospheric Cherenkov Telescopes (IACTs) are ground-based instruments devoted to the study of very high energy gamma-rays coming from space. The detection technique consists of observing images created by the Cherenkov light emitted…
We present a new approach to the pointing determination of Imaging Atmospheric Cherenkov Telescopes (IACTs). This method is universal and can be applied to any IACT with minor modifications. It uses the trajectories of the stars in the…
The next generation of observatories will facilitate the discovery of new types of astrophysical transients. The detection of such phenomena, whose characteristics are presently poorly constrained, will hinge on the ability to perform blind…
Continual Test Time Adaptation (CTTA) has emerged as a critical approach for bridging the domain gap between the controlled training environments and the real-world scenarios, enhancing model adaptability and robustness. Existing CTTA…
Domain adaptation (DA) techniques help deep learning models generalize across data shifts for point cloud semantic segmentation (PCSS). Test-time adaptation (TTA) allows direct adaptation of a pre-trained model to unlabeled data during…
Deep domain adaption has emerged as a new learning technique to address the lack of massive amounts of labeled data. Compared to conventional methods, which learn shared feature subspaces or reuse important source instances with shallow…
The Cherenkov Telescope Array (CTA) is the next-generation ground-based very-high-energy gamma-ray observatory. The Large-Sized Telescope (LST) of CTA is designed to detect gamma rays between 20 GeV and a few TeV with a 23-meter diameter…
Very High Energy (VHE) gamma rays and charged cosmic rays (CCRs) provide an observational window into the acceleration mechanisms of extreme astrophysical environments. One of the major challenges at Imaging Air Cherenkov Telescopes (IACTs)…
Deep learning models for dermatological image analysis remain sensitive to acquisition variability and domain-specific visual characteristics, leading to performance degradation when deployed in clinical settings. We investigate how visual…
The Large High Altitude Air Shower Observatory project is proposed to study high energy gamma ray astronomy ( 40 GeV-1 PeV ) and cosmic ray physics ( 20 TeV-1 EeV ). The wide field of view Cherenkov telescope array, as a component of the…
This research note concerns the application of deep-learning-based multi-view-imaging techniques to data from the H.E.S.S. Imaging Atmospheric Cherenkov Telescope array. We find that the earlier the fusion of layer information from…
The Cherenkov Telescope Array (CTA) is the next generation ground based observatory for gamma ray astronomy at very high energies. Employing more than 100 Imaging Atmospheric Cherenkov Telescopes in the northern and southern hemispheres, it…
The aftermath of air raids can still be seen for decades after the devastating events. Unexploded ordnance (UXO) is an immense danger to human life and the environment. Through the assessment of wartime images, experts can infer the…
The Cherenkov Telescope Array (CTA) is an international initiative to build the next generation ground based very-high energy gamma-ray observatory. It will consist of telescopes of three different sizes, employing several different…
In this work, we study the potential of the Cherenkov Telescope Array (CTA) for the detection of Galactic dark matter (DM) subhalos. We focus on low-mass subhalos that do not host any baryonic content and therefore lack any multiwavelength…
Domain adaptation (DA) strives to mitigate the domain gap between the source domain where a model is trained, and the target domain where the model is deployed. When a deep learning model is deployed on an aerial platform, it may face…