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Artificial Intelligence and Advances in Physics in the Field of Gravitational Waves (I)

Artificial Intelligence and Advances in Physics in the Field of Gravitational Waves (I)

As an important branch of natural sciences, physics studies fundamental laws and phenomena such as matter, energy, mechanics and motion, thus providin

L’intelligenza artificiale e i progressi della fisica nel campo delle onde gravitazionali (I)
Forze esotiche: i raggi traenti infrangono le leggi della fisica?
Multiverse: we don’t know anything about it

As an important branch of natural sciences, physics studies fundamental laws and phenomena such as matter, energy, mechanics and motion, thus providing an important theoretical basis for human beings to understand and explore the natural world. To be precise, physics models nature mathematically.

With the advancement of science and technology and the fast development of Artificial Intelligence, physics is facing new challenges and opportunities. The AI application is changing the research methods and development trajectory of physics, thus offering new possibilities for progress and innovation.

Artificial Intelligence can help physicists to build more accurate and complex models and to analyse and interpret experiments and data provided by observation. We must keep in mind algorithms such as machine learning, of which deep learning is a part.

The difference lies in the fact that deep learning is more advanced: a deep learning algorithm is not conditioned by the user’s experience. Just to make an example, in non-deep machine learning, to distinguish cats and dogs you have to tell “do it by ears, hair, etc…”, while in deep learning the distinguishing features are extracted by the code itself and, often or always, they are actually patterns that we humans would never be able to have!

It does this in the following way: you give it a set of training data and the expected results. The algorithm starts to do tests on this recognition until it reaches an acceptable accuracy value based on what it should come up with by using iterative mathematics (and obviously there is the human hand in the construction of the algorithm). When it has “adjusted”, you can use it on unknown pictures of cats and dogs, not used for learning, so that it classifies them to the human without the human having to do it himself/herself. Considering the above, Artificial Intelligence can discover hidden patterns and correlations from large amounts of data, thus helping physicists to understand and predict related phenomena.

Artificial Intelligence can be applied to theoretical physics and computational physics research to improve the efficiency and accuracy of computational models and methods. For example, Artificial Intelligence can help physicists develop numerical simulation methods since machine learning is not only for classification, but also for numerical prediction, which is especially useful in the financial field, as it is more efficient at speeding up experiments and calculations.

Artificial Intelligence also has broad applications in the fields of quantum physics and quantum computing. Quantum physics is a branch of science that studies the behaviour of microscopic particles and the laws of quantum mechanics, while quantum computing is an emerging field that utilises the characteristics of quantum mechanics for information processing and calculations. Artificial Intelligence can help physicists design more complex quantum systems and algorithms and promote the development and application of computer science.

The AI application in high-energy physics and particle physics experiments is also very important. High-energy physics studies the structure and interaction of microscopic particles, while particle physics studies the origin and evolution of the universe. Artificial Intelligence can help physicists analyse and process large amounts of experimental data and discover potential new particles and physical phenomena.

Al technology can improve the efficiency of physics research and accelerate the scientific research process. Physics research often requires large amounts of experimental data and complex computational models, and Artificial Intelligence can streamline the work of physicists in discovering hidden patterns and correlations in this data. Artificial Intelligence can also provide more accurate and detailed physics models, helping physicists solve even more complex scientific problems.

Traditional physics research often relies on existing theories and experiments, while Artificial Intelligence can help physicists discover new phenomena and physics laws. By bringing to light patterns and correlations from large amounts of data, Artificial Intelligence stimulates physicists to propose new hypotheses and theories, thus promoting development and innovation.

The AI application explores unknown fields and phenomena. By analysing and extracting information from large amounts of data, Artificial Intelligence expands the scope and depth of physics research.

The development of Artificial Intelligence offers new opportunities for the integration of physics with other disciplines. For example, the combination of Artificial Intelligence and biological sciences can help physicists study complex biological systems and related phenomena. The combination of Artificial Intelligence and chemistry can help physicists study molecular structure and chemical reactions.

Although AI technology has broad application prospects in physics research, it also has to face some challenges including the acquisition and processing of data as this is the main problem, especially when dealing with new issues for which databases are scarce; the creation and verification of the physical model; and the selection and optimisation of algorithms. In this regard, it must be said that the boom in deep learning has mainly been due to the increase in available data thanks to the Internet and the advancement of hardware. The networks that anyone uses can run on their laptops, albeit slowly, but this would have been unthinkable in the 1990s, when deep learning was already being thought of in a very vague way. It is not for nothing that we speak of the “democratisation of deep learning”.

Future development requires cooperation and exchanges between physicists and AI professionals to jointly resolve these challenges and better apply this new technology to physics research and applications.

As an emerging technology, Artificial Intelligence is revolutionising traditional physics. By applying Artificial Intelligence, physicists can build more accurate and complex models, analyse and explain physics experiments and observational data. Artificial Intelligence necessarily accelerates the research process in physics and promote the development and innovation of so-called traditional physics.

Artificial Intelligence, however, still has to face some challenges and problems in physics research, which require further study and exploration. In the future, AI technology will be further utilised in physics research and applications, thus providing more opportunities and challenges for development and innovation.

AI technology is also used in gravitational wave research, whose 2017 Nobel Prize in Physics was awarded to Rainer Weiss (Germany), Barry C. Barish (USA) and Kip S. Thorne (USA).

On 14 September 2015 this group of scientists detected the gravitational wave signal of a system of two black holes merging for the first time. At that moment, it triggered a revolution in the astrophysics community: the research group involved in the discovery of gravitational waves was listed as a candidate for the Nobel Prize in Physics ever since.

The two black holes are located about 1.8 billion light years from Earth. Their masses before the merger were equivalent to 31 and 25 suns in size, respectively. After the merger, the total mass was equivalent to 53 suns in size. Three suns were converted into energy and released in the form of gravitational waves.

For some time, gravitational waves have attracted the attention and curiosity not only of scientists, but also of ordinary citizens. Despite being a weak force – a child lifting a toy amply demonstrates this – gravitational interaction has always created questions: but what are gravitational waves?

To put it simply and briefly, this concept of gravitational waves comes from Einstein’s theory of general relativity. We all know that the theory of relativity always discusses the dialectical relationship between space-time and matter, and the viewpoint of gravitational waves is that matter causes ripples and bends into space-time. The curve propagates outwards from the radiation source in the form of a wave. This wave transmits energy as gravitational radiation and the speed of gravitational waves is close to that of light. An extreme case is a black hole. Its supermass causes a distortion of space-time; light cannot escape and slips into it.

Because our basic understanding of traditional physics is based on Newton’s theory of universal gravitation, it is assumed that all objects have a mutual attraction. The size of this force is proportional to the mass of each object. Einstein believed this theory to be superficial. The reason for what appears to be the effect of gravity is due to the distortion of space and time. Hence, if Newton’s law of universal gravitation is approximate, is our current knowledge based on traditional physics going astray? The question is an awkward one. Hence let us leave it to scientists to further study who is right and who is wrong.

Having said that, however, cosmic scientific research currently uses ever more AI techniques, such as the aforementioned detection and discovery of gravitational waves.

The biggest challenge in capturing gravitational waves is that the sampling rate of LIGO (Laser Interferometer Gravitational-Wave Observatory) data is extremely high, reaching a frequency higher than 16,000 times per second, with tens of thousands of sampling channels. Hence the amount of data is extremely large. It is then understood that with AI machine learning, etc. and state-of-the-art methods in the field of data processing, research efficiency can be improved. (1. continued)

a cura di Giancarlo Elia Valori