arXiv subjects

T. Miener

Publications and source records attributed to T. Miener.

10 recordsLinked to original sources

arXiv2025

Development of a Central Trigger Processor board for the Advanced SiPM based camera of the CTA Large-Sized Telescopes

We present ongoing work on the Central Trigger Processor board (CTPb), a trigger subsystem for the future advanced SiPM-based Large-Sized Telescope (LST) camera of the Cherenkov Telescope Array Observatory (CTAO). The camera will implement a fully digital trigger, exploiting the increased resolution to improve discrimination of low-energy $\gamma$-ray events from Night Sky Background noise. This approach aims to enhance telescope sensitivity while satisfying strict timing and data rate constraints. We describe the CTPb conceptual design and report initial results from hardware prototypes under evaluation within this next-generation trigger architecture.

astro-ph.IM↗

arXiv2025

Deep Learning-Based Stereoscopic Event Reconstruction for CTAO using CTLearn

The Cherenkov Telescope Array Observatory (CTAO), a next-generation ground-based gamma-ray observatory, will be composed of two arrays of multiple imaging atmospheric Cherenkov telescopes (IACTs) located in both the Northern and Southern Hemispheres. Its goal is to enhance the sensitivity of current instruments by a factor of five to ten over an energy range from 20 GeV to over 300 TeV. IACT arrays are used to probe the very-high-energy (VHE) gamma-ray sky, operating by simultaneously observing air showers triggered by the interaction of VHE gamma rays and cosmic rays with the atmosphere. Cherenkov photons produced by these showers create a stereoscopic record of the event. By reconstructing the event using machine learning techniques, the properties of the originating VHE particle-including its type, energy, and incoming direction-can be determined. In this contribution, we present a fully deep-learning-driven approach to reconstruct simulated, stereoscopic IACT events using CTLearn. CTLearn is a package designed for loading and manipulating IACT data and for running deep learning models with pixel-wise camera data as input.

astro-ph.HE↗

arXiv2025

First IACT Waveform Analysis Based on Deep Convolutional Neural Networks Using CTLearn

Imaging atmospheric Cherenkov telescopes (IACTs) detect extended air showers (EASs) generated when very-high-energy (VHE) gamma rays or cosmic rays interact with the Earth's atmosphere. Cherenkov photons produced during an EAS are captured by fast-imaging cameras, which record both the spatial and temporal development of the shower, along with calorimetric data. By analyzing these recordings, the properties of the original VHE particle-such as its type, energy, and direction of arrival-can be reconstructed through machine learning techniques. This contribution focuses on the Large-Sized Telescopes (LSTs) of the Cherenkov Telescope Array Observatory, a next-generation ground-based gamma-ray observatory. LSTs are responsible for reconstructing lower-energy gamma rays in the tens of GeV range. We explore a novel event reconstruction technique based on deep convolutional neural networks (CNNs) applied on calibrated and cleaned waveforms of the IACT camera pixels using CTLearn. Our approach explicitly incorporates the time development of the shower, enabling a more accurate reconstruction of the event. This method eliminates the need for charge integration or handcrafted feature extraction, allowing the model to directly learn from waveform data.

astro-ph.HE↗

arXiv2022

The performance of the MAGIC telescopes using deep convolutional neural networks with CTLearn

The Major Atmospheric Gamma Imaging Cherenkov (MAGIC) telescope system is located on the Canary Island of La Palma and inspects the very high-energy (VHE, few tens of GeV and above) gamma-ray sky. MAGIC consists of two imaging atmospheric Cherenkov telescopes (IACTs), which capture images of the air showers originating from the absorption of gamma rays and cosmic rays by the atmosphere, through the detection of Cherenkov photons emitted in the shower. The sensitivity of IACTs to gamma-ray sources is mainly determined by the ability to reconstruct the properties (type, energy, and arrival direction) of the primary particle generating the air shower. The state-of-the-art IACT pipeline for shower reconstruction is based on the parameterization of the shower images by extracting geometric and stereoscopic features and machine learning algorithms like random forest or boosted decision trees. In this contribution, we explore deep convolutional neural networks applied directly to the pixelized images of the camera as a promising method for IACT full-event reconstruction and present the performance of the method on observational data using CTLearn, a package for IACT event reconstruction that exploits deep learning.

astro-ph.IM↗

arXiv2022

Combined search in dwarf spheroidal galaxies for branon dark matter annihilation signatures with the MAGIC Telescopes

One of the most pressing questions for modern physics is the nature of dark matter (DM). Several efforts have been made to model this elusive kind of matter. The largest fraction of DM cannot be made of any of the known particles of the Standard Model (SM). We focus on brane world theory as a prospective framework for DM candidates beyond the SM of particle physics. The new degrees of freedom that appear in flexible brane world models, corresponding to brane fluctuations, are called branons. They behave as weakly interacting massive particles (WIMPs), which are one of the most favored candidates for DM. We present a multi-target DM search in dwarf spheroidal galaxies for branon DM annihilation signatures with the ground-based gamma-ray telescope MAGIC leading to the most constraining branon DM limits in the TeV mass range.

hep-ph↗

arXiv2021

Open-source Analysis Tools for Multi-instrument Dark Matter Searches

The nature of dark matter (DM) is still an open question in Physics. Gamma-ray and neutrino telescopes have been searching for DM signatures for several years and no detection has been obtained so far. In their quest, these telescopes have gathered a wealth of observations that, if properly combined and analyzed, can improve on the constraints to the nature of DM set by individual instruments. In this contribution, we present two open-source analysis tools aimed at performing the before mentioned combined analysis: gLike, a general-purpose ROOT-based code framework for the numerical maximization of joint likelihood functions, and LklCom, a Python-based tool combining likelihoods from different instruments to produce combined exclusion limits on the DM annihilation cross-section.

astro-ph.IM↗

arXiv2021

IACT event analysis with the MAGIC telescopes using deep convolutional neural networks with CTLearn

The Major Atmospheric Gamma Imaging Cherenkov (MAGIC) telescope system consists of two imaging atmospheric Cherenkov telescopes (IACTs) and is located on the Canary island of La Palma. IACTs are excellent tools to inspect the very-high-energy (few tens of GeV and above) gamma-ray sky by capturing images of the air showers, originated by the absorption of gamma rays and cosmic rays by the atmosphere, through the detection of Cherenkov photons emitted in the shower. One of the main factors determining the sensitivity of IACTs to gamma-ray sources, in general, is how well reconstructed the properties (type, energy, and incoming direction) of the primary particle triggering the air shower are. We present how deep convolutional neural networks (CNNs) are being explored as a promising method for IACT full-event reconstruction. The performance of the method is evaluated on observational data using the standard MAGIC Analysis and Reconstruction Software, MARS, and CTLearn, a package for IACT event reconstruction through deep learning.

astro-ph.IM↗

arXiv2021

Reconstruction of IACT events using deep learning techniques with CTLearn

Arrays of imaging atmospheric Cherenkov telescopes (IACT) are superb instruments to probe the very-high-energy gamma-ray sky. This type of telescope focuses the Cherenkov light emitted from air showers, initiated by very-high-energy gamma rays and cosmic rays, onto the camera plane. Then, a fast camera digitizes the longitudinal development of the air shower, recording its spatial, temporal, and calorimetric information. The properties of the primary very-high-energy particle initiating the air shower can then be inferred from those images: the primary particle can be classified as a gamma ray or a cosmic ray and its energy and incoming direction can be estimated. This so-called full-event reconstruction, crucial to the sensitivity of the array to gamma rays, can be assisted by machine learning techniques. We present a deep-learning driven, full-event reconstruction applied to simulated IACT events using CTLearn. CTLearn is a Python package that includes modules for loading and manipulating IACT data and for running deep learning models with TensorFlow, using pixel-wise camera data as input.

astro-ph.IM↗

arXiv2019

CTLearn: Deep Learning for Gamma-ray Astronomy

CTLearn is a new Python package under development that uses the deep learning technique to analyze data from imaging atmospheric Cherenkov telescope (IACT) arrays. IACTs use the Cherenkov light emitted from air showers, initiated by very-high-energy gamma rays, to form an image of the longitudinal development of the air shower on the camera plane. The spatial, temporal, and calorimetric information of the originating high-energy particle is then recorded electronically. The sensitivity of IACTs to astrophysical sources depends strongly on the efficient rejection of the background of much more numerous cosmic-ray showers. CTLearn includes modules for running machine learning models with TensorFlow, using pixel-wise camera data as input. Its high-level interface provides a configuration-file-based workflow to drive reproducible training and prediction. We illustrate the capabilities of CTLearn by presenting some results using IACT simulated data.

astro-ph.IM↗

arXiv2019

Studying deep convolutional neural networks with hexagonal lattices for imaging atmospheric Cherenkov telescope event reconstruction

Deep convolutional neural networks (DCNs) are a promising machine learning technique to reconstruct events recorded by imaging atmospheric Cherenkov telescopes (IACTs), but require optimization to reach full performance. One of the most pressing challenges is processing raw images captured by cameras made of hexagonal lattices of photo-multipliers, a common layout among IACT cameras which topologically differs from the square lattices conventionally expected, as their input data, by DCN models. Strategies directed to tackle this challenge range from the conversion of the hexagonal lattices onto square lattices by means of oversampling or interpolation to the implementation of hexagonal convolutional kernels. In this contribution we present a comparison of several of those strategies, using DCN models trained on simulated IACT data.

astro-ph.IM↗