Can machines uncover the universe’s secrets?

A faint dip in a star’s brightness can be easy to overlook. Software can search years of observations and bring that signal to an astronomer’s attention. That is a practical example of how machines turn measurements into opportunities for discovery. The harder task is establishing what the signal means.

Artificial intelligence adds new tools to astronomy, but discovery also depends on detectors, mathematical models and careful checks. Finding a pattern, explaining its cause and testing that explanation are distinct achievements. Machines already help with all three; that does not establish that one system can answer every question about the cosmos.

An actual discovery: A planet hidden in Kepler data

In 2017, a neural network helped researchers discover Kepler-90i in archived telescope observations. It searched for the small brightness changes associated with a planet crossing its star. Researchers then evaluated the candidates, including possible false positives. Machine learning made the search more productive, while the scientific conclusion still depended on assessing the evidence. ^[1]

The useful distinction is between a candidate worth investigating and a confirmed result. A model’s score needs to be understood in the context of its training data, its errors and the observations available. A confident output does not remove the need for verification.

The machinery behind a reliable result

Acquiring the evidence: Instruments must respond predictably to what they measure. Calibration, timing and environmental effects matter before any advanced analysis begins. The same separation of responsibilities appears in industrial sensors and actuators: acquiring a signal and acting on it are different jobs.

Processing the records: Big data and data analytics allow researchers to examine large collections, compare observations and identify unusual cases. Data quality and traceability remain essential. Adding more records does not automatically remove a systematic measurement error.

Challenging the explanation: A promising result should survive alternative explanations and tests with independent evidence. Keeping a record of preprocessing choices, model versions and rejected candidates helps other researchers repeat the analysis.

Gravitational waves: Automated science has more than one method

The 2015 observation of GW150914 combined sensitive instruments with several analysis methods. Detailed studies compared the measured signals with waveforms calculated from general relativity using matched filtering. Describing the first detection simply as an AI discovery would miss much of the measurement and analysis behind it. ^[3]

Signals from a distant merger can reveal properties of black holes. That research asks different questions from whether a machine could survive inside a black hole. The immediate challenge is to establish where a signal came from and whether instrumental effects could explain it.

Planck: Testing a history of the universe

The cosmic microwave background carries information from the early universe. Planck’s temperature and polarization measurements let researchers constrain cosmological models, including inflationary scenarios. Agreement with some predictions does not demonstrate every detail of inflation or uniquely establish its physical mechanism. ^[2]

These measurements address the universe’s history. Recreating the universe’s initial conditions is a separate engineering and physics question. A successful explanation of observed features should not be treated as complete knowledge of every stage of cosmic evolution.

What a simulated universe can teach us

IllustrisTNG models the formation and evolution of galaxies. Researchers compare its simulated populations with observations and investigate how results depend on resolution and physical assumptions. The calculations provide a way to test how different processes affect observable structures. Agreement and disagreement both help assess the model. ^[4]

A calculation of stellar evolution does not amount to building a physical star. Models also operate at chosen scales: a galaxy simulation need not solve every detail of quantum mechanics or quantum field theory. Its scope determines which questions it can address.

An industrial digital twin offers a useful comparison: the model earns trust through comparison with the system it represents. Running a cosmic simulation does not, by itself, show that the real universe is a mathematical simulation. A method for studying reality is not evidence about how reality is implemented.

Open questions: Dark matter and dark energy

Dark matter is investigated through effects such as galaxy motions and gravitational lensing. Mapping the inferred mass distribution does not identify the underlying material; its physical nature remains an open question. ^[5]

Dark energy names the unexplained component associated with the universe’s accelerating expansion. Its physical nature is also unsettled. Computers can help compare proposed explanations with measurements, but greater numerical precision alone cannot decide which explanation describes nature. ^[6]

Why more computing power is not a complete answer

The evidence may not distinguish the alternatives. If different explanations predict the same measured result, running an analysis faster does not automatically separate them. Predictions about missing data must remain distinguishable from actual observations.

The model itself needs testing. Quantum computers should be assessed against specific computational tasks and operating conditions. More computing capability is a different goal from finding an ultimate theory of the universe. The physical assumptions still have to withstand scrutiny.

The conclusion must fit the evidence. Sweeping claims can outrun what an experiment establishes. Misuse of quantum terminology is a useful warning: impressive vocabulary does not replace a testable explanation. A strong result makes its limitations visible.

A better partnership between researchers and machines

Software can rank candidates, carry out calculations and compare the consequences of different hypotheses. That support can improve human decision making about the next observation or experiment. Researchers define the question, assess the apparatus and challenge the interpretation. Creative reasoning matters when designing a test that could expose a mistaken assumption.

Machines can help uncover particular secrets of the universe. There is no established guarantee that they will settle every scientific or philosophical question. Progress comes from better measurements, stronger explanations and independent verification, supported by tools whose capabilities are understood.

References and further reading

[1] NASA — Machine learning and the Kepler-90i discovery (2017). Back to the text.

[2] Planck Collaboration — Planck 2018 results. X. Constraints on inflation. Back to the text.

[3] LIGO — GW150914 observations and signal analysis. Back to the text.

[4] IllustrisTNG — Scope and methods of cosmological simulations. Back to the text.

[5] NASA Science — Dark matter. Back to the text.

[6] NASA Science — Dark energy. Back to the text.

This article provides general educational information. Scientific findings should be read within the scope of the methods and models used. You may share this article with attribution to OtomasyonAVM. Editorial update: October 2026.