Chao-Nan Tong | Searcharxiv

arXiv2026

Revisiting the XMM-Newton Observations of the Galactic Microquasar SS 433: Implications for the Origin of the Ultrahigh-Energy Emission Detected by LHAASO

Recently, the Large High Altitude Air Shower Observatory (LHAASO) detected ultrahigh-energy (UHE; photon energy E>100TeV) $γ$-ray emission toward SS 433, the microquasar embedded in the W50 nebula, making it a promising Galactic PeVatron candidate. We reanalyze the archival XMM-Newton observations covering the bipolar jets and the thermal X-ray shell north of SS 433, and derive spatially resolved profiles of the nonthermal X-ray intensity and photon index along both jets. The jet emission softens with distance from the source, implying a correspondingly evolving electron population. In particular, a hard electron component appears close to the jet bases, which can account for the UHE emission from SS 433 via inverse Compton radiation if the magnetic field remains approximately uniform along the jets. The result, however, is highly sensitive to the magnetic field profile. For flux-conserving configurations in which the field decreases as the jet expands, the stronger field required in the inner regions may reduce the number of X-ray-emitting electrons and suppress their inverse Compton emission. Furthermore, electron transport calculations show that injection only at the jet bases cannot reproduce the observed intensity and spectral evolution, particularly the downstream re-brightening features, indicating additional particle injection and/or re-acceleration within the jets.

astro-ph.HE↗

arXiv2022

Deep learning on nuclear mass and $α$ decay half-lives

Ab-initio calculations of nuclear masses, the binding energy and the $α$ decay half-lives are intractable for heavy nucleus, because of the curse of dimensionality in many body quantum simulations as proton number($\mathrm{N}$) and neutron number($\mathrm{Z}$) grow. We take advantage of the powerful non-linear transformation and feature representation ability of deep neural network(DNN) to predict the nuclear masses and $α$ decay half-lives. For nuclear binding energy prediction problem we achieve standard deviation $σ=0.263$ MeV on 10-fold cross validation on 2149 nuclei. Word-vectors which are high dimensional representation of nuclei from the hidden layers of mass-regression DNN help us to calculate $α$ decay half-lives. For this task, we get $σ=0.797$ on 100 times 10-fold cross validation on 350 nuclei on $log_{10}T_{1/2}$ and $σ=0.731 $ on 486 nuclei. We also find physical a priori such as shell structure, magic numbers and augmented inputs inspired by Finite Range Droplet Model are important for this small data regression task.

nucl-th↗