Wednesday 3 July 2019

Deep-CEE: The AI deep learning tool helping astronomers explore deep space

Galaxy clusters are some of the most massive structures in the cosmos, but despite being millions of lightyears across, they can still be hard to spot. Researchers at Lancaster University have turned to artificial intelligence for assistance, developing "Deep-CEE" (Deep Learning for Galaxy Cluster Extraction and Evaluation), a novel deep learning technique to speed up the process of finding them. Matthew Chan, a Ph.D. student at Lancaster University, is presenting this work at the Royal Astronomical Society's National Astronomy meeting on 4 July at 3:45pm in the Machine Learning in Astrophysics session.

source https://www.lifetechnology.com/blogs/life-technology-news-blog/deep-cee-the-ai-deep-learning-tool-helping-astronomers-explore-deep-space

Collision course: Amateur astronomers play a part in efforts to keep space safe

Heavy traffic is commonplace on Earth but now congestion is becoming an increasing problem in space. With over 22,000 artificial satellites in orbit it is essential to keep track of their positions in order to avoid unexpected collisions. Amateur astronomers from the Basingstoke Astronomical Society have been helping the Ministry of Defence explore what is possible using high-end consumer equipment to track objects in space.

source https://www.lifetechnology.com/blogs/life-technology-news-blog/collision-course-amateur-astronomers-play-a-part-in-efforts-to-keep-space-safe

Substantial increase in body weight since 1960s due to interplay between genes and environment

People with a genetic predisposition to obesity are not only at greater risk of excess weight, their genes interact with an increasingly "obesogenic" environment, resulting in higher body mass index (BMI) in recent decades, finds a study from Norway published by The BMJ today.

source https://www.lifetechnology.com/blogs/life-technology-news-blog/substantial-increase-in-body-weight-since-1960s-due-to-interplay-between-genes-and-environment