arXiv subjects

Tim Unbehaun

Publications and source records attributed to Tim Unbehaun.

5 recordsLinked to original sources

arXiv2026

Identifying potentially missed extended sources in the Fermi-LAT 4FGL Catalog using clustering analysis

Context. Since its launch in 2008, the Fermi Large Area Telescope (LAT) has detected thousands of sources, many of which remain unassociated. Some may be extended sources represented in the catalog by multiple point-like entries. The reinterpretation of HESS J1813-178 as a single extended source motivates a systematic search for further missed extended sources. Aims. We search for clusters of unassociated Fermi-LAT sources and test whether single extended-source models describe them better than multiple catalog sources. Methods. We apply the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm to 4FGL sources with a linking scale of $\epsilon = 0.3^\circ$ over the 5 GeV to 1 TeV energy range. Each cluster contains at least one unassociated source and up to one extended or point-like associated source from selected categories, including pulsars, pulsar wind nebulae, and supernova remnants. Using Fermipy, we compare extended- and multiple-source models and characterize each candidate spectrally and morphologically, focusing on the Galactic plane. Results. We identify 48 clusters containing 124 sources, each with at least one unassociated source. For all eight clusters passing our quality selection, an extended-source model is statistically preferred. At linking scales of $0.4^\circ$ and $0.5^\circ$, all eight retain their core sources. Cross-matches with the Second Fermi Galactic Extended Sources Catalog (2FGES) and the HESS Galactic Plane Survey (HGPS) show that five overlap known extended sources. The other three, including one with morphology dependent on the interstellar emission model, have no counterpart in either catalog and are new extended-source candidates. Conclusions. Spatial clustering combined with likelihood-based model comparison can uncover extended sources missed in the Fermi-LAT catalog and complements existing searches.

astro-ph.HE↗

arXiv2025

Event types in H.E.S.S.: a combined analysis for different telescope types and energy ranges

Imaging atmospheric Cherenkov telescopes (IACTs) are the main technique for detecting gamma rays with energies between tens of GeV and hundreds of TeV. Amongst them, the High Energy Stereoscopic System (H.E.S.S.) has pioneered the use of different telescope types to achieve an energy range as broad as possible. A large, 28 m diameter telescope is used in monoscopic mode to access the lowest energies ($E \gtrsim 30$ GeV), while the four smaller, 12 m diameter telescopes are used in stereoscopic mode to study energies between 150 GeV and 100 TeV. Nevertheless, a combination of both telescope types and trigger strategies has proven to be challenging. In this work, we propose for the first time an analysis based on event types capable of exploiting both telescope types, trigger strategies, and the whole energy range of the experiment. Due to the large differences between monoscopic and stereoscopic reconstructions, the types are defined based on Hillas parameters of individual events, resulting in three types (Type M, Type B, and Type A), each dominating over a different energy range. The performances of the new analysis configurations are compared to the standard configurations in the H.E.S.S. Analysis Package (HAP), Mono and Stereo. The proposed analysis provides optimal sensitivity over the whole energy range, in contrast to Mono and Stereo, which focus on smaller energy ranges. On top of that, improvements in sensitivity of 25-45% are found for most of the energy range. The analysis is validated using real data from the Crab Nebula, showing the application to data of an IACT analysis capable of combining significantly different telescope types with significantly different energy ranges. Larger energy coverage, lower energy threshold, smaller statistical uncertainty, and more robustness are observed. The need for a run-by-run correction for the observation conditions is also highlighted.

astro-ph.HE↗

arXiv2025

Clustering analysis of Fermi-LAT unidentified point sources

The Fermi Large Area Telescope (LAT) has detected thousands of sources since its launch in 2008, with many remaining unidentified. Some of these point sources may arise from source confusion. Specifically, there could be extended sources erroneously described as groups of point sources. Using the DBSCAN clustering algorithm, we analyze unidentified Fermi-LAT sources alongside some classified objects from the 4FGL-DR4 catalog. We identified 44 distinct clusters containing 106 sources, each including at least one unidentified source. Detailed modeling of selected clusters reveals some cases where extended source models are statistically preferred over multiple point sources. The work is motivated by prior observations of extended TeV gamma-ray sources, such as HESS J1813-178, and their GeV counterparts. In the case of HESS J1813-178, two unidentified Fermi-LAT point sources were detected in the region. Subsequent multiwavelength analysis combining TeV and GeV data showed that a single extended source is a better description of the emission in this region than two point-like sources.

astro-ph.HE↗

arXiv2025

Improvements to monoscopic analysis for imaging atmospheric Cherenkov telescopes: Application to H.E.S.S

Imaging atmospheric Cherenkov telescopes (IACTs) detect gamma rays by measuring the Cherenkov light emitted by secondary particles in the air shower when the gamma rays hit the atmosphere. At low energies, the limited amount of Cherenkov light produced typically implies that the event is registered by one IACT only. Such events are called monoscopic events, and their analysis is particularly difficult. Challenges include the reconstruction of the event's arrival direction, energy, and the rejection of background events. Here, we present a set of improvements, including a machine-learning algorithm to determine the correct orientation of the image, an intensity-dependent selection cut that ensures optimal performance, and a collection of new image parameters. To quantify these improvements, we use the central telescope of the H.E.S.S. IACT array. Knowing the correct image orientation, which corresponds to the arrival direction of the photon in the camera frame, is especially important for the angular reconstruction, which could be improved in resolution by 57% at 100 GeV. The event selection cut, which now depends on the total measured intensity of the events, leads to a reduction of the low-energy threshold for source analyses by ~50%. The new image parameters characterize the intensity and time distribution within the recorded images and complement the traditionally used Hillas parameters in the machine learning algorithms. We evaluate their importance to the algorithms in a systematic approach and carefully evaluate associated systematic uncertainties. We find that including subsets of the new variables in machine-learning algorithms improves the reconstruction and background rejection, resulting in a sensitivity improved by 41% at the low-energy threshold.

astro-ph.IM↗

arXiv2023

Gammapy: A Python package for gamma-ray astronomy

In this article, we present Gammapy, an open-source Python package for the analysis of astronomical $\gamma$-ray data, and illustrate the functionalities of its first long-term-support release, version 1.0. Built on the modern Python scientific ecosystem, Gammapy provides a uniform platform for reducing and modeling data from different $\gamma$-ray instruments for many analysis scenarios. Gammapy complies with several well-established data conventions in high-energy astrophysics, providing serialized data products that are interoperable with other software packages. Starting from event lists and instrument response functions, Gammapy provides functionalities to reduce these data by binning them in energy and sky coordinates. Several techniques for background estimation are implemented in the package to handle the residual hadronic background affecting $\gamma$-ray instruments. After the data are binned, the flux and morphology of one or more $\gamma$-ray sources can be estimated using Poisson maximum likelihood fitting and assuming a variety of spectral, temporal, and spatial models. Estimation of flux points, likelihood profiles, and light curves is also supported. After describing the structure of the package, we show, using publicly available $\gamma$-ray data, the capabilities of Gammapy in multiple traditional and novel $\gamma$-ray analysis scenarios, such as spectral and spectro-morphological modeling and estimations of a spectral energy distribution and a light curve. Its flexibility and power are displayed in a final multi-instrument example, where datasets from different instruments, at different stages of data reduction, are simultaneously fitted with an astrophysical flux model.

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