most citedDark Matter searches in Dwarf Galaxies with the Southern Wide-field Gamma-ray Observatory

2 citations · 5 across the 6 of their papers we have counts for

collaborators

6 papers

astro-ph.HE20241 cited

Constraints on Ultra Heavy Dark Matter Properties from Dwarf Spheroidal Galaxies with LHAASO Observations

Zhen Cao, F. Aharonian, Q. An +276

In this work we try to search for signals generated by ultra-heavy dark matter at the Large High Altitude Air Shower Observatory (LHAASO) data. We look for possible gamma-ray by da…

astro-ph.IM20231 cited

Wavelength-shifting light traps for SWGO and other applications

M. Pihet, M. Mariotti, C. Arcaro

Wavelength-shifting (WLS) materials contain molecules that absorb light and reemit at longer wavelengths. They can be used for light detection because they provide a large effectiv…

astro-ph.HE20232 cited

Dark Matter searches in Dwarf Galaxies with the Southern Wide-field Gamma-ray Observatory

Micael Andrade, Aion Viana

Dark matter is thought to make up most of the matter density of the Universe, yet its true nature remains uncertain. Among dark matter theories, Weakly Interacting Massive Particle…

cs.CV2023

Point Contrastive Prediction with Semantic Clustering for Self-Supervised Learning on Point Cloud Videos

Xiaoxiao Sheng, Zhiqiang Shen, Gang Xiao +3

We propose a unified point cloud video self-supervised learning framework for object-centric and scene-centric data. Previous methods commonly conduct representation learning at th…

cs.CV2023

Exploring the Physical World Adversarial Robustness of Vehicle Detection

Wei Jiang, Tianyuan Zhang, Shuangcheng Liu +3

Adversarial attacks can compromise the robustness of real-world detection models. However, evaluating these models under real-world conditions poses challenges due to resource-inte…

cs.CV20231 cited

Contrastive Predictive Autoencoders for Dynamic Point Cloud Self-Supervised Learning

Xiaoxiao Sheng, Zhiqiang Shen, Gang Xiao

We present a new self-supervised paradigm on point cloud sequence understanding. Inspired by the discriminative and generative self-supervised methods, we design two tasks, namely…