A science team has discovered a new possible pathway for forming carbon structures in space using an X-ray technique at Berkeley Lab.
Source: newscenter.lbl.gov/2019/09/03/radical-wrinkle-complex-carbon-molecules-in-space/
We’re often asked whether CMOS or CCD sensors are better. The simple answer is, “it depends.”
Both types of sensors detect light the exact same way. An incoming photon hits an atom of silicon, which is a semiconductor. When this happens one of the electrons in the atom is boosted to a higher energy level (orbital), referred to as the conduction band. Silicon normally behaves like an insulator, so its electrons can’t move around. But once an electron is boosted up to the conduction band, it is freed to move around to other adjacent atoms, as if the silicon was a metal. What was an insulator becomes a conductor – this is why silicon is called a semiconductor. In optical sensors these now-mobile electrons are referred to as photoelectrons.
Both types of sensors use pixels. Pixels are simply a tiny square region of silicon, which collect and hold these photoelectrons. The usual analogy is an array of rain buckets in a field, each collecting rain water. If you want to know how much it rained in any part of the field, you just have to measure how full each bucket is. So far everything is the same for CCD and CMOS; it’s the measuring process where things are very different.
Open Sourcing the Hunt for Exoplanets
Recently, we discovered two exoplanets by training a neural network to analyze data from NASA’s Kepler space telescope and accurately identify the most promising planet signals. And while this was only an initial analysis of ~700 stars, we consider this a successful proof-of-concept for using machine learning to discover exoplanets, and more generally another example of using machine learning to make meaningful gains in a variety of scientific disciplines (e.g. healthcare, quantum chemistry, and fusion research).
Today, we’re excited to release our code for processing the Kepler data, training our neural network model, and making predictions about new candidate signals. We hope this release will prove a useful starting point for developing similar models for other NASA missions, like K2 (Kepler’s second mission) and the upcoming Transiting Exoplanet Survey Satellite mission. As well as announcing the release of our code, we’d also like take this opportunity to dig a bit deeper into how our model works.
Source: research.googleblog.com/2018/03/open-sourcing-hunt-for-exoplanets.html