Valentina Fioretti

Context. On April 14, 2009, the AGILE satellite detected a γ -ray flare from an unknown transient source. Subsequent X-ray follow-up observations with Swift and INTEGRAL identified the supergiant fast X-ray transient (SFXT) IGR J17354−3255 as the best candidate counterpart, based on positional coincidence and a similar temporal behaviour. Aside from this hint, no SFXT has been firmly detected at high energies to date. Overall, SFXTs comprise a subclass of high-mass X-ray binaries (HMXBs) that host a massive OB supergiant star as a companion donor. They tend to display the most extreme X-ray variability among HMXBs. These systems might be able to emit photons at MeV-TeV energies in the form of fast flares lasting from hours to a few days, with a low-duty cycle. Aims. In this work, we analyse archival AGILE data to search for γ -ray flares consistent with IGR J17354−3255 and evaluate their possible physical origin. Methods. We identified a transient source, AGL J1736−3250, which emitted 19 γ -ray flares and was seen to be positionally consistent with IGR J17354−3255. Most flares, detected on a 1 d timescale, concentrate most of their emission on two, four, and six hour timescales, resembling those observed in the X-ray band from IGR J17354−3255. Results. An orbital phase analysis revealed that approximately half of the γ -ray activity occurs around the apastron passage of the compact object hosted in the binary system. We also incorporated archival Swift and INTEGRAL observations to provide phase-folded light curves at lower energies. Our collected results strongly support a physical association between IGR J17354−3255 and AGL J1736−3250, offering evidence that SFXTs could constitute a new class of Galactic high-energy transient emitters.

The detection of gravitational waves (GWs) from a binary neutron star (BNS) merger by Advanced LIGO and Advanced Virgo (GW170817), together with its electromagnetic counterpart, the short gamma-ray burst GRB 170817A, heralded the birth of multimessenger astronomy. The detection of TeV emission from GRBs motivates follow-up observations with the Cherenkov Telescope Array Observatory (CTAO), which is ideal for detecting such signals due to its unprecedented sensitivity, rapid response, and wide-field survey capabilities. The aim of this work is to evaluate GeV–TeV GW follow-up strategies for CTAO using a multistep simulation pipeline and to estimate the expected rate of joint GW–GRB detections during observing run O5. Using a simulated sample of BNS systems with corresponding GW detections, gamma-ray emission is simulated through phenomenological prescriptions based on the observed population of short GRBs, including off-axis jet scenarios. CTAO observations are simulated to account for instrument response, sky tiling strategies, integration times, and varying observing conditions. Strategies with variable and constant integration times are investigated. We find that, via an optimized follow-up strategy, about 5% of simulated GW-associated short GRBs produce GeV–TeV radiation detectable by CTAO. Detectability is strongly influenced by the jet opening angle and viewing angle, suggesting that even rough estimates of the viewing angle in GW alerts could enhance targeting. This framework motivates future follow-ups of GW-detectable events, including neutron star–black hole mergers, and further supports the development of advanced strategies incorporating galaxy distributions and synergies with future detectors such as the Einstein Telescope.

The Compton Spectrometer and Imager (COSI) is a Compton telescope designed to survey the 0.2–5 MeV sky, consisting of a compact array of cross-strip germanium detectors. It is planned to be launched in 2027 into an equatorial low-Earth (530 km) orbit with a prime mission duration of 2 yr. The observation of MeV gamma rays is dominated by background. Thus, background simulation and identification are crucial for predicting the sensitivities of instruments. In this work we perform Monte Carlo simulations of the background for the first 3 months in orbit, and we extrapolate the results to 2 yr in orbit in order to determine the buildup of the activation due to long-lived isotopes. The simulations account for the known background components and include time-dependent rate variations due to the geomagnetic cutoff and South Atlantic Anomaly (SAA) passages. In addition, they include detailed modeling of the delayed activation due to short- and long-lived isotopes. We determine the rates of events induced by the background that are reconstructed as Compton events in the simulated COSI data. We find that the extragalactic background photons dominate at low energies (b ∣ ≤ 1°) between recent measurement of the SAA and the AP9/AE9 model has been made, showing an overestimation of the flux by a factor ∼9 by the model. The systematic uncertainties associated with these components are quantified.

The Compton Spectrometer and Imager (COSI) is a NASA satellite mission under development designed to survey the entire sky at 0.2–5 MeV with a wide-field gamma-ray telescope. Its main instrument is a germanium detector array surrounded on the sides and bottom by bismuth germanium oxide scintillator active shields (the “Anticoincidence Subsystem” (ACS)) to reduce and monitor background and for detecting transients. COSI will have an onboard trigger algorithm to detect gamma-ray bursts (GRBs) in the ACS and send data to the ground for further analysis. In this paper, we present three localization methods that we evaluated for the localization of short GRBs (sGRBs) using the ACS light curves. The first method is the χ ² fit already used by the Fermi Gamma-ray Burst Monitor, which calculates the best fit between look-up tables and the GRB data. The second method is a maximum likelihood estimation fit implemented in bc-tools for the BurstCube mission that performs a fit between the instrument response function and the GRB data. The last method is based on deep learning techniques and consists of a neural network developed for the COSI mission and trained to perform a regression of the sGRB position, taking as input the count rates of each ACS panel. The localization errors obtained by analyzing simulated sGRBs with the three methods are consistent. Despite the theoretical similarity between the approaches, their consistency in results is noteworthy, as they differ substantially in their implementations and optimization processes. The bc-tools obtain the best localization accuracy.

Next-generation space observatories for high-energy gamma-ray astrophysics will increase scientific return using onboard machine learning (ML). This is now possible thanks to today’s low-power, radiation-tolerant processors and artificial intelligence accelerators. This paper provides an overview of current and future ML applications in gamma-ray space missions focused on high-energy transient phenomena. We discuss onboard ML use cases that will be implemented in the future, including real-time event detection and classification (e.g., gamma-ray bursts), and autonomous decision-making, such as rapid repointing to transient events or optimising instrument configuration based on the scientific target or environmental conditions.

Low-energy (<300 keV) protons entering the field of view of XMM-Newton are observed in the form of a sudden increase in the background level, the so-called soft proton flares, affecting up to 40% of the mission observing time. In-flight XMM-Newton's observations of soft protons represent a unique laboratory to validate and improve our understanding of their interaction with the mirror, optical filters, and X-ray instruments. At the same time, such models would link the observed background flares to the primary proton population encountered by the telescope, converting XMM-Newton into a monitor for soft protons. We built a Geant4 simulation of XMM-Newton, including a verified mass model of the X-ray mirror, the focal plane assembly, and the EPIC MOS and pn-CCDs. We encoded the energy redistribution and proton transmission efficiency into a redistribution matrix file (RMF) and an auxiliary response file (ARF). For the validation, three averaged soft proton spectra, one for each filter configuration, were extracted from a collection of 13 years of MOS observations of the focused non X-ray background and analysed with Xspec. The best-fit model is in agreement with the power-law distribution predicted from independent measurements for the XMM-Newton orbit, spent mostly in the magnetosheath and nearby regions. For the first time we are able to link detected soft proton flares with the proton radiation environment in the Earth's magnetosphere, while proving the validity of the simulation chain in predicting the background of future missions. Benefiting from this work and contributions from the Athena instrument consortia, we also present the response files for the Athena mission and updated estimates for its focused charged background.

Many science goals carried by instruments of future X-ray observatories, such as the Wide Field Imager (WFI)^{1 – 3} onboard Athena, are driven by the detection and modeling of faint, diffuse sources, such as galaxy cluster outskirts. Although measurements with current X-ray observatories are often limited by photon statistics, observatories with large collecting areas will mostly be impacted by systematic uncertainties. As shown, e.g., by Ref. 4, the systematics of the background, especially the non-X-ray background (NXB), limit the feasibility of measurements on cluster outskirts, independent of the statistical significance of the measurement: Once the signal is below the background, longer observing times cannot make up for the level of uncertainty in the background modeling. This is not unique to cluster outskirts but applies to any faint source, such as distant black holes, or detecting the first galaxy clusters and groups, both major science drivers of Athena/WFI.^1,5,6 Therefore, the level and properties of the background produced by high-energy galactic cosmic ray (GCR) particles play a crucial role in the design of future X-ray observatories.