The LHAASO collaboration recently reported the measurement of the gamma-ray spectra of GRB 221009A, which is the brightest burst ever, covering an energy range from 0.3 TeV to about 10 TeV. Based on the observation, we investigate the ALP-photon oscillation effect in the host galaxy of GRB 221009A and the Milky Way. The CL_s method is applied to set constraints on the ALP parameters in this study. Given the uncertain magnetic field configuration in the host galaxy, we use three different models: a homogeneous magnetic field model, a magnetic field model identical to that of the Milky Way, and a model constructed from the HST observations of the host galaxy. We find that the constraints derived using these three host galaxy magnetic field models are comparable. Our results are complementary in the small ALP mass regions compared with other experiments.

The interaction between axion-like particles (ALPs) and photons induces ALP-photon oscillations in astrophysical magnetic fields, leading to spectral distortions in the γ-ray spectrum of blazars. The primary uncertainty of this phenomenon may originate from the magnetic field within the jet of the blazar. Many studies focus on a simple jet magnetic field model with a toroidal component exerting a predominant influence on regions far from the central region. While many investigations have explored the effects of ALP-photon oscillations using typical parameter values in this model, it is important to recognize that these parameters can be constrained by multi-wavelength observations. In this study, we utilize the high energy γ-ray spectrum of Mrk 421 obtained from MAGIC and Fermi-LAT observations. By employing multi-wavelength fitting with a one-zone synchrotron self-Compton model, we derive the parameters characterizing the simple toroidal balazar jet magnetic field model, and investigate their impacts on the ALP constraints.

High energy photons traveling through astrophysical magnetic fields have the potential to undergo oscillations with axion-like particles (ALPs), resulting in modifications to the observed photon spectrum. High energy -ray sources with significant magnetic field strengths provide an ideal environment to investigate this phenomenon. Ton 599, a flat spectrum radio quasar with a magnetic field strength on the order of Gauss in its emission region, presents a promising opportunity for studying ALP-photon oscillations. In this study, we analyze the effects of ALP-photon oscillations on the γ-ray spectrum of Ton 599, as observed by Fermi-LAT. Our investigation considers the potential influences of the broad-line region and dusty torus on the -ray spectrum of Ton 599. We set the constraints on the ALP parameters at a 95% confidence level and show that the constraints on can reach approximately for .

In axion models, interactions between axions and electromagnetic waves induce frequency-dependent time delays determined by the axion mass and decay constant. These small delays are difficult to detect, limiting the effectiveness of traditional methods. We compute such delays under realistic radio telescope conditions and identify a prominent dispersive feature near half the axion mass, which appears nondivergent within the limits of observational resolution. Based on this, we develop a machine learning method that achieves 90% classification accuracy and demonstrates performance well in low signal-to-noise regimes. The method’s robustness is confirmed against false positives using both simulated noisy data and real-world, known-null observations. Future improvements in optical clock precision and telescope bandwidth, particularly with instruments such as the Qitai Radio Telescope, may enhance constraints on the axion decay constant by up to 4 orders of magnitude in the 10 ⁻⁶ ∼ 10 ⁻⁴ eV mass range.

Axion-like particles (ALPs) can induce energy-dependent irregularities in gamma-ray spectra through ALP-photon oscillation in astrophysical magnetic fields. In this study, we investigate the impact of this effect on the $\gamma$-ray emission from the Andromeda Galaxy (M31). We employ the ${\mathrm{CL}}_{\mathrm{s}}$ method to set constraints on the ALP parameters. We find that ALP-photon oscillation can produce characteristic oscillatory features in the gamma-ray spectrum within the mass range $m_a\sim {10}^{-9}$–${10}^{-7}\mathrm{eV}$. No significant deviation from the standard astrophysical model is observed, allowing us to place constraints on the ALP parameter space. The resulting limits probe a region complementary to existing constraints from other astrophysical observations.

Accurate real-time positioning of Unmanned Aerial Vehicles (UAVs) is critical for navigation and mapping but remains challenging in complex environments due to signal blockages and multipath effects. This study presents a comparative framework for real-time error prediction of the Global Navigation Satellite System (GNSS), evaluating two machine learning models (Random Forest and XGBoost) and a deep learning model (Long Short-Term Memory network) against an Extended Kalman Filter baseline. A high-precision total station provides ground-truth coordinates, enabling the derivation of positioning error labels from synchronized GNSS raw data. Among the evaluated models, the tree-based XGBoost model achieves a significantly lower Mean Squared Error (MSE) and a considerably higher Coefficient of Determination ($R^2$) score than other models in predicting positioning deviations. The high-accuracy error predictions from the optimal model establish the core of a software-only solution for positioning integrity. The framework demonstrates that reliable, real-time error estimates can be derived directly from observation data, providing the essential input required for future compensation systems without necessitating additional hardware.