Search for γ-ray emission from supernova remnants in the Large Magellanic Cloud: A new cluster analysis at energies above 4 GeV

¹ Department of Astronomy, University of Geneva, Chemin Pegasi 51, 1290 Versoix, Switzerland
² INAF/OAS, via Gobetti 101, 40129 Bologna, Italy
³ INAF/IAPS, via del Fosso del Cavaliere 100, I-00113 Roma, Italy
⁴ INAF/Osservatorio Astronomico di Palermo, Piazza del Parlamento 1, I-90134 Palermo, Italy
⁵ Dipartimento di Fisica e Chimica E. Segrè, Università di Palermo, Piazza del Parlamento 1, 90134 Palermo, Italy

^⋆ Corresponding author: andrea.tramacere@unige.ch

Received: 2 January 2025
Accepted: 21 March 2025

Abstract

Context. A search for γ-ray emission from supernova remnants (SNRs) in the Large Magellanic Cloud (LMC) based on the detection of concentrations in the arrival direction Fermi-LAT images of photons at energies higher than 10 GeV found significant evidence for nine sources. This analysis was based on data collected in the time window from August 4, 2008, to August 4, 2020 (12 years).

Aims. In the present contribution, we report the results of a new search extended using a 15-year long (up to August 4, 2023) dataset and to a broad energy range (higher than 4 GeV). The longer baseline and the softer energy lower limit are required to further understand the relation between the X-ray and gamma ray SNRs in the LMC and to investigate the completeness of the sample at low luminosities.

Methods. Two different methods of clustering analysis were applied: minimum spanning tree (MST) and a combination of density-based spatial clustering of applications with noise (DBSCAN) and DENsity-based CLUstEring (DENCLUE) algorithms.

Results. We confirm all previous detections and found positive indications for at least eight new clusters with a spatial correspondence with other SNRs, thus increasing the number of candidate remnants in the LMC or detected in the high-energy γ-rays to 16 sources.

Conclusions. This study extends previous analyses of γ-ray emission from SNRs in the LMC by incorporating a longer observational baseline and a broader energy range. The improved dataset and advanced clustering techniques enhance our understanding of the connection between X-ray and γ-ray SNRs, providing new insights into their high-energy properties and contributing to the assessment of the completeness of the sample at lower luminosities.