In the vast depths of our Milky Way there are constant movements and processes that create a tangled web of ionized particles and powerful radiation from space. These particles, which come from as yet unexplored parts of our galaxy, are called Space signals reaching Earth have aroused the interest of scientists for decades. Significant scientific progress may be underway that could shed light on the mystery surrounding the origin of these cosmic rays.
Space signal: this is where it comes from
A comprehensive analysis of data collected by the IceCube Neutrino Observatory in Antarctica over the past decade provides compelling evidence of neutrino emissions from the center of our galaxy. These discoveries have significant implications for unraveling the mysterious origins of cosmic rays. As physicist Luigi Antonio Fusco from the Università degli Studi di Salerno in Italy explains, this groundbreaking evidence heralds an exciting future for astroparticle physics in our galaxy.
Our view of the Milky Way is now being redefined, providing a unique image through neutrinos. This innovative perspective offers new insights into our galactic plane and could potentially reshape our understanding of galactic studies. Despite its apparently calm appearance, Earth is constantly bombarded by cosmic particles – protons and charged atomic nuclei – originating from within our galaxy. These particles are driven by powerful cosmic fields at incredible speeds, making the task of tracking them to their origin a colossal challenge.
Neutrinos They are extremely light subatomic elementary particles that move at almost the speed of light. They come in three types: electron, muon, and tau neutrinos. Their characteristic is minimal interaction with matter, which allows them to travel undisturbed by stars, planets and galaxies. They have a small but measurable mass, which was surprising since the Standard Model of particle physics originally predicted they would be massless.
Lots of patience and advanced features
“Electrically charged cosmic ray particles are not suitable for studying cosmic ray sources,” Lindsey Bignell, a particle physicist at the Australian National University, told ScienceAlert. “They are influenced by magnetic fields and therefore do not move in a straight line from their source to us.” One way to detect these cosmic particles is to study the consequences of their collisions with interstellar gas and dust. These violent interactions produce pairs of quarks and antiquarks called pions. Neutral pions quickly decay into gamma rays, which can be observed from a distance as a signal from space and provide a rough indication of where cosmic rays might come from.
On the other hand, the decay of charged pions leads to the creation of a high-energy electron neutrino, which is fascinating. Neutrinos, often called “ghost particles” due to their low mass and lack of electrical charge, can travel almost unhindered through the universe at the speed of light and only stop when they collide with an atomic nucleus.
Detecting such rare and random collisions on Earth requires a lot of patience and advanced facilities like the IceCube Neutrino Observatory. However, it is a great challenge to distinguish neutrinos from interactions with cosmic rays from those resulting from atmospheric phenomena.
New discoveries expected
Researchers from the IceCube Collaboration approached this challenge with a new approach. Using machine learning, they trained a computer to distinguish between traces of muon neutrinos found in our atmosphere and traces of electron neutrinos in the form of signals coming from space.
According to Bignell, this innovative use of machine learning has significantly improved its data analysis methods, allowing it to include twenty times more events in its dataset and obtain better directional insights. Although the elusive neutrino is barely noticeable, it could soon reveal previously unimaginable aspects of the cosmos.
Sources: ScienceAlert; “Galactic neutrinos in the Milky Way – A source of neutrinos may be in the midplane of the Galaxy” (Science, 2023)
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