- Press Release
- Oct 31, 2023
Artificial Intelligence Probes Dark Matter In The Universe
Understanding the how our universe came to be what it is today and what will be its final destiny is one of the biggest challenges in science.
The awe-inspiring display of countless stars on a clear night gives us some idea of the magnitude of the problem, and yet that is only part of the story. The deeper riddle lies in what we cannot see, at least not directly: dark matter and dark energy. With dark matter pulling the universe together and dark energy causing it to expand faster, cosmologists need to know exactly how much of those two is out there in order to refine their models.
At ETH Zurich, scientists from the Department of Physics and the Department of Computer Science have now joined forces to improve on standard methods for estimating the dark matter content of the universe through artificial intelligence. They used cutting-edge machine learning algorithms for cosmological data analysis that have a lot in common with those used for facial recognition by Facebook and other social media. Their results have recently been published in the scientific journal Physical Review D.
Facial recognition for cosmology
While there are no faces to be recognized in pictures taken of the night sky, cosmologists still look for something rather similar, as Tomasz Kacprzak, a researcher in the group of Alexandre Refregier at the Institute of Particle Physics and Astrophysics, explains: “Facebook uses its algorithms to find eyes, mouths or ears in images; we use ours to look for the tell-tale signs of dark matter and dark energy.” As dark matter cannot be seen directly in telescope images, physicists rely on the fact that all matter – including the dark variety – slightly bends the path of light rays arriving at the Earth from distant galaxies. This effect, known as “weak gravitational lensing”, distorts the images of those galaxies very subtly, much like far-away objects appear blurred on a hot day as light passes through layers of air at different temperatures.
Cosmologists can use that distortion to work backwards and create mass maps of the sky showing where dark matter is located. Next, they compare those dark matter maps to theoretical predictions in order to find which cosmological model most closely matches the data. Traditionally, this is done using human-designed statistics such as so-called correlation functions that describe how different parts of the maps are related to each other. Such statistics, however, are limited as to how well they can find complex patterns in the matter maps.
Neural networks teach themselves
“In our recent work, we have used a completely new methodology”, says Alexandre Refregier. “Instead of inventing the appropriate statistical analysis ourselves, we let computers do the job.” This is where Aurelien Lucchi and his colleagues from the Data Analytics Lab at the Department of Computer Science come in. Together with Janis Fluri, a PhD student in Refregier’s group and lead author of the study, they used machine learning algorithms called deep artificial neural networks and taught them to extract the largest possible amount of information from the dark matter maps.
In a first step, the scientists trained the neural networks by feeding them computer-generated data that simulates the universe. That way, they knew what the correct answer for a given cosmological parameter – for instance, the ratio between the total amount of dark matter and dark energy – should be for each simulated dark matter map. By repeatedly analysing the dark matter maps, the neural network taught itself to look for the right kind of features in them and to extract more and more of the desired information. In the Facebook analogy, it got better at distinguishing random oval shapes from eyes or mouths.
More accurate than human-made analysis
The results of that training were encouraging: the neural networks came up with values that were 30% more accurate than those obtained by traditional methods based on human-made statistical analysis. For cosmologists, that is a huge improvement as reaching the same accuracy by increasing the number of telescope images would require twice as much observation time – which is expensive.
Finally, the scientists used their fully trained neural network to analyse actual dark matter maps from the KiDS-450 dataset. “This is the first time such machine learning tools have been used in this context,” says Fluri, “and we found that the deep artificial neural network enables us to extract more information from the data than previous approaches. We believe that this usage of machine learning in cosmology will have many future applications.”
As a next step, he and his colleagues are planning to apply their method to bigger image sets such as the Dark Energy Survey. Also, more cosmological parameters and refinements such as details about the nature of dark energy will be fed to the neural networks.
Fluri J, Kacprzak T, Lucchi A, Refregier A, Amara A, Hofmann T, Schneider A: Cosmological constraints with deep learning from KiDS-450 weak lensing maps. Physical Review D. 100: 063514, doi: 10.1103/PhysRevD.100.063514
"Kulingana na taarifa ya gazeti la The New York Times, ""Ujasusi bandia unachunguza mambo ya giza katika ulimwengu, kuelewa jinsi ulimwengu wetu ulivyotokea kuwa kile ulivyo leo na ni nini kitakachokuwa hatima yake ya mwisho ni moja ya changamoto kubwa katika sayansi.""" Maonyesho ya nyota nyingi katika usiku wa wazi yanatupa wazo la ukubwa wa tatizo hilo, lakini hiyo ni sehemu tu ya hadithi. "Kitu kingine kinachotatanisha zaidi ni kile ambacho hatuwezi kuona, angalau si moja kwa moja: ""dark matter"" na ""dark energy.""" Kwa kuwa nyenzo nyeusi inaunganisha ulimwengu na nishati nyeusi inaifanya kupanuka kwa kasi, wataalamu wa anga wanahitaji kujua ni kiasi gani cha nyenzo hizo mbili ziko nje ili kuboresha mifano yao. Katika ETH Zurich, wanasayansi kutoka Idara ya Fizikia na Idara ya Sayansi ya Kompyuta sasa wameungana ili kuboresha mbinu za kawaida za kukadiria maudhui ya nyenzo nyeusi ya ulimwengu kupitia akili bandia. Algorithms za kujifunza mashine za hali ya juu zinatumiwa kwa uchambuzi wa data ya cosmological ambayo ina mengi ya kawaida na zile zinazotumiwa kwa utambuzi wa uso na Facebook na vyombo vingine vya kijamii. "Kama ilivyoelezwa katika jarida la kisayansi la Physical Review: ""Facebook hutumia algorithms yake kupata macho, vinywa au masikio katika picha; sisi hutumia yetu kutafuta ishara za habari za nyenzo nyeusi na nishati nyeusi."" - Tomasz Kaczmarek, Mtafiti wa kikundi cha Alexandre Refreguet, Institute of Particle Physics and Astrophysics, ""Facebook hutumia algorithms yake kupata macho, vinywa au masikio katika picha; sisi hutumia yetu kutafuta ishara za habari za nyenzo nyeusi na nishati nyeusi.""" Madhara haya, yanayojulikana kama "weak gravitational lensing," yanapotosha picha za galaksi hizo kwa njia ndogo sana, kama vile vitu vilivyo mbali vinavyoonekana kuwa vimefichwa siku ya joto wakati mwanga unapopita kupitia tabaka za hewa kwa joto tofauti. Wataalamu wa anga wanaweza kutumia udanganyifu huo kufanya kazi nyuma na kuunda ramani za anga zinazoonyesha mahali ambapo vitu vyeusi viko. Kisha, wanalinganisha ramani hizo za vitu vyeusi na utabiri wa nadharia ili kugundua ni mfano gani wa ulimwengu unaolingana zaidi na data. Kwa kawaida, hii hufanywa kwa kutumia takwimu zilizobuniwa na binadamu kama vile kazi zinazoitwa uhusiano ambazo zinaelezea jinsi sehemu tofauti za ramani zinavyohusiana. Hata hivyo, takwimu hizo hazina uwezo wa kugundua mifumo tata katika ramani za vitu. Mitandao ya neva hujifundisha yenyewe <unk>Katika kazi yetu ya hivi karibuni, tumetumia mbinu mpya kabisa<unk>, anasema Alexandre Refregier. "Badala ya kuunda uchambuzi sahihi wa takwimu wenyewe, tunaruhusu kompyuta kufanya kazi. ""Hapa ndipo Aurelien Lucchi na wenzake kutoka Maabara ya Uchambuzi wa Takwimu katika Idara ya Sayansi ya Kompyuta wanaingia." Pamoja na Janis Fluri, mwanafunzi wa PhD katika kikundi cha Refugio na mwandishi mkuu wa utafiti, walitumia algorithms za kujifunza mashine zinazoitwa mitandao ya kina ya neva ya bandia na kuwafundisha kuchimba kiasi kikubwa cha habari kutoka kwa ramani za nyenzo nyeusi. Katika hatua ya kwanza, wanasayansi walifundisha mitandao ya neva kwa kuipatia data iliyotengenezwa na kompyuta ambayo huiga ulimwengu. Kwa njia hiyo, walijua ni nini jibu sahihi kwa parameter fulani ya cosmological - kwa mfano, uwiano kati ya kiasi cha jumla cha nyenzo nyeusi na nishati nyeusi - inapaswa kuwa kwa kila ramani ya nyenzo nyeusi iliyoiga. Kwa kuchanganua mara kwa mara ramani za nyenzo nyeusi, mtandao wa neva ulijifunza kutafuta aina sahihi ya vipengele ndani yao na kutoa habari zaidi na zaidi. Katika mfano wa Facebook, iliboresha kutofautisha maumbo ya mviringo ya nasibu kutoka kwa macho au vinywa. Matokeo ya mafunzo hayo yalikuwa yenye kutia moyo: mitandao ya neva ilitoa maadili ambayo yalikuwa sahihi zaidi ya 30% kuliko yale yaliyopatikana na mbinu za jadi kulingana na uchambuzi wa takwimu uliofanywa na binadamu. Kwa wataalamu wa anga, hiyo ni uboreshaji mkubwa kama kufikia usahihi sawa kwa kuongeza idadi ya picha za darubini ingehitaji mara mbili zaidi ya muda wa uchunguzi - ambayo ni ghali. Kwa hiyo, wanasayansi walitumia mtandao wao wa neva uliozoezwa kikamilifu kuchanganua ramani halisi za nyenzo nyeusi kutoka kwa seti ya data ya KiDOS-450. "Hii ni mara ya kwanza kama zana za kujifunza mashine zimetumiwa katika muktadha huu", anasema Fluri, "na tuligundua kwamba mtandao wa kina wa neva za bandia hutuwezesha kupata habari zaidi kutoka kwa data kuliko mbinu za awali. "Tunaamini kwamba matumizi ya kujifunza mashine katika cosmology itakuwa na matumizi mengi ya baadaye. ""Kama hatua inayofuata, yeye na wenzake ni mipango ya kutumia mbinu yao kwa seti kubwa ya picha kama vile Dark Energy Survey." Pia, vipimo zaidi vya cosmological na usafishaji kama vile maelezo juu ya asili ya nishati nyeusi itapelekwa kwa mitandao ya neva. Fluri J, Kaczczak T, Lucchi A, Refugier A, Amara A, Hofmann T, Schneider A: Vizuizi vya Cosmological na kujifunza kina kutoka kwa ramani za KiDS-450 za lensing dhaifu. Utafiti wa Kifasiri D. 100: 063514 D. 10113 (PhysRevD. 100: 063514)