Search Results (95)

ComPair, a prototype of the All-sky Medium Energy Gamma-ray Observatory (AMEGO), completed a short-duration high-altitude balloon campaign on 27 August 2023 from Fort Sumner, New Mexico, USA. The goal of the balloon flight was to demonstrate ComPair as both a Compton and Pair telescope in flight, reject the charged particle background, and measure the background $\gamma$-ray spectrum. This analysis compares measurements from the balloon flight with Monte Carlo simulations to benchmark the instrument. The comparison finds good agreement between the measurements and simulations and supports the conclusion that ComPair accomplished its goals for the balloon campaign. Additionally, two charged particle background rejection schemes are discussed: a soft ACD veto that records a higher charged particle event rate but with less risk of event loss, and a hard ACD veto that limits the charged particle event rate on board. There was little difference in the measured spectra from the soft and hard ACD veto schemes, indicating that the hard ACD veto could be used for future flights. The successes of ComPair’s engineering flight will inform the development of the next generation of ComPair with upgraded detector technology and larger active area. Full article

We present early follow-up observations of Einstein Probe (EP) X-ray transients, following its first year of operation. EP is a dedicated wide-field X-ray observatory that is transforming our understanding of the dynamic X-ray universe. During its first year, EP successfully detected a diverse range of high-energy transients—including gamma-ray bursts (GRBs), tidal disruption events (TDEs), and fast X-ray transients (FXTs), besides many stellar flares, disseminating 128 alerts in the aggregate. Ground-based optical follow-up observations, particularly those performed by our BOOTES telescope network, have played a crucial role in multi-wavelength campaigns carried out so far. Out of the 128 events, the BOOTES Network has been able to follow up 58 events, detecting 6 optical counterparts at early times. These complementary optical measurements have enabled rapid identification of counterparts, precise redshift determinations (such as EP250215a at $z=4.61$), and detailed characterization of the transient phenomena. The synergy between EP’s cutting-edge X-ray monitoring and the essential optical follow-up provided by facilities, such as the above-mentioned BOOTES Global Network or other Spanish ground-based facilities we have access to, underscores the importance and necessity of coordinated observations in the era of time-domain and multi-messenger astrophysics. Full article

Here, we briefly describe the current state of the Zeiss-1000 telescope of the Special Astrophysical Observatory of Russian Academy of Sciences (SAO RAS). Principal attention is given to research programs from recent years. The observations made according to allocated requests both by researchers from the observatory and scientists from other institutions and organizations are planned within a half-year schedule. The instrumental facilities provide a wide range of methods for studying objects of interest. They include standard photometry, moderate- and high-resolution spectroscopy, and polarimetry, along with unique “guest” methods (e.g., emission line imaging). The research programs cover different fields of astrophysics. The topic of “hot stars” and other important developments have become possible due to the introduction of new research methods at the telescope. Blazars, gamma-ray burst optical transients, massive supernovae, cataclysmic variables, magnetic stars, white dwarfs, luminous blue variables, red dwarfs, and many others are among its targets. Full article

Searching for very-high-energy photons arising from dark matter interactions in selected astrophysical environments is a promising strategy to probe the existence and particle nature of dark matter. Among the many particle candidates, motivated by the extensions of the Standard Model, Weakly Interacting Massive Particles (WIMPs) are considered the most compelling candidate for the elusive dark matter in the universe. In this contribution, we report an overview of the important developments in the field of indirect searching for dark matter through cosmic gamma-ray observations. We mainly focus on the role of atmospheric Cherenkov telescopes in probing the dark matter. Finally, we emphasize the opportunities for the Major Atmospheric Cherenkov Experiment (MACE) situated in Hanle, India, to explore WIMPs in the mass range of 200 GeV to 10 TeV for Segue1 and Draco dwarf–spheroidal galaxies. Full article

Imaging Atmospheric Cherenkov Telescopes (IACTs) have revolutionized our understanding of the universe at very high energies (VHEs), enabling groundbreaking discoveries of extreme astrophysical phenomena. These instruments capture the brief flashes of Cherenkov light produced when VHE particles interact with Earth’s atmosphere, providing unique insights into cosmic accelerators and high-energy radiation sources. A fundamental challenge in IACT observations lies in distinguishing the rare gamma-ray signals from an overwhelming background of cosmic-ray events. For every gamma-ray photon detected from even the brightest sources, thousands of cosmic-ray-induced atmospheric showers trigger the telescopes. This profound signal-to-background imbalance necessitates sophisticated discrimination techniques that can effectively isolate genuine gamma-ray events while maintaining high rejection efficiency for cosmic-ray backgrounds. The most common method involves the parametrization of the morphological feature of the shower images. However, we know that gamma-ray and hadron showers also differ in their time evolution. Here, we describe how the pixel time tags (i.e., the record of when each camera pixel is lit up by the incoming shower) can help in the discrimination between photonic and hadronic showers, with a focus on the ASTRI Mini-Array Cherenkov Event Reconstruction. Our methodology employs a Random Forest classifier with optimized hyperparameters, trained on a balanced dataset of gamma and hadron events. The model incorporates feature importance analysis to select the most discriminating temporal parameters from a comprehensive set of time-based features. This machine learning approach enables effective integration of both morphological and temporal information, resulting in improved classification performance, especially at lower energies. Full article

This paper presents the technical design of the pathfinder Barcelona Raman LIDAR (pBRL) for the northern site of the Cherenkov Telescope Array Observatory (CTAO-N) located at the Roque de los Muchachos Observatory (ORM). The pBRL is developed for continuous atmospheric characterization, essential for correcting high-energy gamma-ray observations captured by Imaging Atmospheric Cherenkov Telescopes (IACTs). The LIDAR consists of a steerable telescope with a 1.8 m parabolic mirror and a pulsed Nd:YAG laser with frequency doubling and tripling. It emits at wavelengths of 355 nm and 532 nm to measure aerosol scattering and extinction through two elastic and Raman channels. Built upon a former Cherenkov Light Ultraviolet Experiment (CLUE) telescope, the pBRL’s design includes a Newtonian mirror configuration, a coaxial laser beam, a near-range system, a liquid light guide and a custom-made polychromator. During a one-year test at the ORM, the stability of the LIDAR and semi-remote-controlled operations were tested. This pathfinder leads the way to designing a final version of a CTAO Raman LIDAR which will provide real-time atmospheric monitoring and, as such, ensure the necessary accuracy of scientific data collected by the CTAO-N telescope array. Full article

Observations of Very High Energy (VHE) gamma ray sources using the ground-based Imaging Atmospheric Cherenkov Telescopes (IACTs) play a pivotal role in understanding the non-thermal energetic phenomena and acceleration processes under extreme astrophysical conditions. However, detection of the VHE gamma ray signal from the astrophysical sources is very challenging, as these telescopes detect the photons indirectly by measuring the flash of Cherenkov light from the Extensive Air Showers (EAS) initiated by the cosmic gamma rays in the Earth’s atmosphere. This requires fast detection systems, along with advanced data acquisition and analysis techniques to measure the development of extensive air showers and the subsequent segregation of gamma ray events from the huge cosmic ray background, followed by the physics analysis of the signal. Here, we report the development of a python-based package for analyzing the data from the Major Atmospheric Cherenkov Experiment (MACE), which is operational at Hanle in India. The Python-based MACE data Analysis Package (PyMAP) analyzes data by using advanced methods and machine learning algorithms. Data recorded by the MACE telescope are passed through different utilities developed in the PyMAP to extract the gamma ray signal from a given source direction. We also propose a new image cleaning method called DIOS (Denoising Image of Shower) and compare its performance with the standard image cleaning method. The working performance of DIOS indicates an advantage over the standard method with an improvement of ≈$25\%$ in the sensitivity of MACE. Full article

In June 2024, the Fermi Gamma-Ray Space Telescope (FGST) celebrated its 16th year of operations. The Fermi Large Area Telescope (Fermi-LAT) is the main instrument onboard the FGST satellite and is designed to be sensitive to $\gamma$-rays in the energy range from about $20\mathrm{MeV}$ up to the $\mathrm{TeV}$ regime. From its launch, the Fermi-LAT has collected more than $4.53\mathrm{billion}$ photon events, providing crucial information to improve our understanding of particle acceleration and $\gamma$-ray production phenomena in astrophysical sources. The most abundant in the last 4FGL-data release 4 (4FGL-DR4), most powerful and persistent $\gamma$-ray emitters in the sky are the Active Galactic Nuclei (AGNs). These sources are extremely luminous galaxy cores powered by a super massive black hole (SMBH) with a mass ranging from millions to billions of times the mass of the Sun. The ASI-SSDC, a facility of the Agenzia Spaziale Italiana (ASI), plays a pivotal role in supporting Fermi-LAT by providing the essential infrastructure for the storage, processing, and analysis of the vast amounts of data generated by the mission. As a key asset to various space missions, ASI-SSDC contributes significantly to advancing research in high-energy astrophysics and $\gamma$-ray observations. Full article

Blazars—a subclass of active galaxies—are intrinsically time-variable broadband sources of electromagnetic radiation. In this contribution, we explored relativistic proton (hadronic) signatures in the time domain blazar emission and searched for those parameter combinations that unveil their presence during flaring epochs. We generated time series for key model parameters, like magnetic field strength and the power-law index of radiating particles, which were motivated from a simulated time series with statistical properties describing the observed GeV gamma-ray flux. We chose the TeV blazar Mrk 501 as our test case, as it had been the study ground for extensive investigations during individual flaring events. Using the code LeHaMoC, we computed the electromagnetic and neutrino emissions for a period of several years that contained several flares of interest. We show that for both of those particle distributions the power-law index variations that were tied to moderate changes in the magnetic field strength of the emitting region might naturally lead to hard X-ray flares with very-high-energy $\gamma$-ray counterparts. We found spectral differences measurable by the Cherenkov Telescope Array Observatory at sub-TeV energies, and we computed the neutrino fluence over 14.5 years. The latter predicted ∼0.2 muon and anti-muon neutrinos, consistent with the non-detection of high-energy neutrinos from Mrk 501. Full article

Ground-based observations of Very High Energy (VHE) gamma rays from extreme astrophysical sources are significantly influenced by atmospheric conditions. This is due to the atmosphere being an integral part of the detector when utilizing Imaging Atmospheric Cherenkov Telescopes (IACTs). Clouds and dust particles diminish atmospheric transmission of Cherenkov light, thereby impacting the reconstruction of the air showers and consequently the reconstructed gamma-ray spectra. Precise measurements of atmospheric transmission above Cherenkov observatories play a pivotal role in the accuracy of the analysed data, among which the corrections of the reconstructed energies and fluxes of incoming gamma rays, and in establishing observation strategies for different types of gamma-ray emitting sources. The Major Atmospheric Gamma Imaging Cherenkov (MAGIC) telescopes and the Cherenkov Telescope Array Observatory (CTAO), both located on the Observatorio del Roque de los Muchachos (ORM), La Palma, Canary Islands, use different sets of auxiliary instruments for real-time characterisation of the atmosphere. In this paper, historical data taken by MAGIC LIDAR (LIght Detection And Ranging) and CTAO FRAM (F/Photometric Robotic Telescope) are presented. From the atmospheric aerosol transmission profiles measured by the MAGIC LIDAR and CTAO FRAM aerosol optical depth maps, we obtain the characterisation of the clouds above the ORM at La Palma needed for data correction and optimal observation scheduling. Full article

Ultra-high-energy cosmic rays (UHECRs) are extremely rare energetic particles of ordinary matter in the Universe, traveling astronomical distances before reaching the Earth’s atmosphere. When primary cosmic rays interact with atmospheric nuclei, cascading extensive air showers (EASs) of secondary elementary particles are developed. Radio detectors have proven to be a reliable method for reconstructing the properties of EASs, such as the shower’s axis, its energy, and its maximum (${\mathrm{X}}_{\max }$). This aids in understanding fundamental astrophysical phenomena, like active galactic nuclei and gamma-ray bursts. Concurrently, data science has become indispensable in UHECR research. By applying statistical, computational, and deep learning methods to both real-world and simulated radio data, researchers can extract insights and make predictions. We introduce a convolutional neural network (CNN) architecture designed to classify simulated air shower events as either being generated by protons or by iron nuclei. The classification achieved a stable test error of 10%, with Accuracy and ${\mathrm{F}}_1$ scores of 0.9 and an MCC of 0.8. These metrics indicate strong prediction capability for UHECR’s nuclear composition, based on data that can be gathered by detectors at the world’s largest cosmic rays experiment on Earth, the Pierre Auger Observatory, which includes radio antennas, water Cherenkov detectors, and fluorescence telescopes. Full article

Space radiation particle detection plays a crucial role in scientific research and engineering practice, especially in particle species identification. Currently, commonly used in-orbit particle identification techniques include telescope methods, electrostatic analysis time of flight (ESA × TOF), time-of-flight energy (TOF × E), and pulse shape discrimination (PSD). However, these methods usually fail to utilize the full waveform information containing rich features, and their particle identification results may be affected by the random rise and fall of particle deposition and noise interference. In this study, a low-latency and lightweight onboard FPGA real-time particle identification platform based on full waveform information was developed utilizing the superior target classification, robustness, and generalization capabilities of convolutional neural networks (CNNs). The platform constructs diversified input datasets based on the physical features of waveforms and uses Optuna and Pytorch software architectures for model training. The hardware platform is responsible for the real-time inference of waveform data and the dynamic expansion of the dataset. The platform was utilized for deep learning training and the testing of the historical waveform data of neutron and gamma rays, and the inference time of a single waveform takes 4.9 microseconds, with an accuracy rate of over 97%. The classification expectation FOM (figure-of-merit) value of this CNN model is 133, which is better than the traditional pulse shape discrimination (PSD) algorithm’s FOM value of 0.8. The development of this platform not only improves the accuracy and efficiency of space particle discrimination but also provides an advanced tool for future space environment monitoring, which is of great value for engineering applications. Full article

The quasar 3C 286, a well-known calibrator source in radio astronomy, was found to exhibit exceptional multiwavelength properties. Its rich and complex optical emission-line spectrum revealed its narrow-line Seyfert 1 (NLS1) nature. Given its strong radio emission, this makes 3C 286 one of the radio-loudest NLS1 galaxies known to date. 3C 286 is also one of very few known compact steep-spectrum (CSS) sources detected in the gamma-ray regime. Observations in the X-ray regime, rarely carried out so far, revealed evidence for variability, raising the question whether it is driven by the accretion disk or jet. 3C 286 is also well known for its damped Lyman alpha system from an intervening absorber at z = 0.692, triggering a search for the corresponding X-ray absorption along the line-of-sight. Here, we present new observations in the radio, X-ray, optical, and UV bands. The nature of the X-ray variability is addressed. Spectral evidence suggests that it is primarily driven by the accretion disk (not the jet), and the X-ray spectrum is well fit by a powerlaw plus soft excess model. The radio flux density and polarization remain constant at the Effelsberg telescope resolution, reconfirming the use of 3C 286 as radio calibrator. The amount of reddening/absorption along the line-of-sight intrinsic to 3C 286 is rigorously assessed. None is found, validating the derivation of a high Eddington ratio ($L/L_{\mathrm{Edd}}$ ∼ 1) and of the very high radio-loudness index of 3C 286. Based on the first deep Chandra image of 3C 286, tentative evidence for hard X-ray emission from the SW radio lobe is reported. A large variety of models for the gamma-ray emission of 3C 286 are briefly discussed. Full article

The development of the latest generation of Imaging Atmospheric Cherenkov Telescopes (IACTs) over recent decades has led to the discovery of new extreme astrophysical phenomena in the very-high-energy (VHE, E > 100 GeV) gamma-ray regime. Time-domain and multi-messenger astronomy are inevitably connected to the physics of transient VHE emitters, which show unexpected (and mostly unpredictable) flaring or exploding episodes at different timescales. These transients often share the physical processes responsible for the production of the gamma-ray emission, through cosmic-ray acceleration, magnetic reconnection, jet production and/or outflows, and shocks interactions. In this review, we present an up-to-date overview of the VHE transients field, spanning from novae to supernovae, neutrino counterparts or fast radio bursts, among others, and we outline the expectations for future facilities. Full article

The detection of Gamma-Ray Bursts (GRBs) using spaceborne X/gamma-ray photon detectors depends on a reliable background count rate estimate. This study focuses on evaluating a data-driven background estimator based on a neural network designed to adapt to various X/gamma-ray space telescopes. Three trials were conducted to assess the effectiveness and limitations of the proposed estimator. Firstly, quantile regression was employed to obtain an estimation with a confidence range prediction. Secondly, we assessed the performance of the neural network, emphasizing that a dataset of four months is sufficient for training. We tested its adaptability across various temporal contexts, identified its limitations and recommended re-training for each specific period. Thirdly, utilizing Explainable Artificial Intelligence (XAI) techniques, we delved into the neural network output, determining distinctions between a network trained during solar maxima and one trained during solar minima. This entails conducting a thorough analysis of the neural network behavior under varying solar conditions. Full article