Publications

This paper explores how primordial black holes (PBHs) are potential sources of axions and axion-like particles (ALPs), with a focus on detecting their decay into photons.

• Source of Axions: PBHs emit axions and ALPs as part of Hawking radiation when their temperature surpasses the particle's mass.
• Detection Method: Introduces a new method to track the decay of axions into photons over time on a cosmological scale, predicting the photon spectrum and flux.
• Future Implications: Highlights the potential of upcoming detectors, like e-ASTROGAM, to detect these signals under certain assumptions about PBHs, such as a monochromatic mass spectrum and isotropic distribution.

DeeLeMa is a deep learning network designed for analyzing energy and momentum in high-energy particle collisions, specifically those involving multiple invisible particles.

• Event Analysis: Addresses the challenge in high-energy physics experiments of analyzing collision events with multiple invisible particles.
• Design and Robustness: Built on kinematic constraints and symmetry of event topologies, DeeLeMa effectively estimates mass distribution despite combinatorial uncertainties and detector smearing.
• Flexibility and Impact: Adaptable to various event topologies, leveraging kinematic symmetries, and holds significant potential for advancing high-energy particle collision data analysis.

This study introduces an unsupervised learning approach to assess motion-induced reliability degradation of cardiac volume signals (CVS) in multi-channel electrical impedance-based hemodynamic monitoring.

• Innovative Methodology: Utilizes long-short term memory and variational auto-encoder structures in an encoder-decoder model for self-reproducing CVS input sequences and extrapolating future sequences.
• Advanced Detection and Analysis: Detects low-quality, motion-influenced CVS by comparing input sequences with their neural representations, using a two-sigma rule for determining cutoff values.
• Enhanced Annotation and Error Reduction: Demonstrates that machine-guided annotation can effectively identify motion-induced anomalies with less human error and effort, achieving competitive performance even without labeled data.