<p>The age of an isolated star is difficult to determine. There are various properties that are age-dependent, but also depend in complex, non-linear ways on stellar mass, composition and each other. Examples include luminosity, rotation, magnetic activity and various chemical abundances.</p>
<p>On the other hand, the ages of stars that belong to <em>clusters</em> can be estimated with some accuracy and precision using for example the main sequence turn-off.</p>
<p>That means an ML method can be trained to estimate stellar ages using the features of stars in clusters of known age, circumventing the need for detailed evolutionary models of the age-dependence of those features (which are often uncertain or non-existent). The trained model can then estimate the ages of isolated stars. See for example <a href="https://arxiv.org/abs/2409.07523" rel="nofollow noreferrer">Weaver et al. (2024)</a> (on which I am a coauthor).</p>
<p>A similar example has just been published that uses a &quot;conditional normalising flow&quot; neural network to model the evolution of stellar rotation rates in order to provide an estimate of stellar age (<a href="https://ui.adsabs.harvard.edu/abs/2024arXiv241212244V/abstract" rel="nofollow noreferrer">Van-Lane et al. 2024</a>).</p>

CC BY-SA 4.0
2024-12-10T14:59:15.077Z
2024-12-25T15:17:36.273Z
2024-12-25T15:17:36.273Z
ProfRob
<p>Like the answer by ProfRob, there are many other instances of utilising AI and ML in astronomy, for example:</p>
<p>Detecting gravitational waves involves comparing observed signals with extensive libraries of theoretical waveforms. ML models, like convolutional neural networks (CNNs), can:</p>
<ol>
<li>Rapidly identify candidate signals from noisy data.</li>
<li>Reduce reliance on exhaustive template matching by approximating waveform calculations.</li>
</ol>
<p>Another mystery of the universe is dark matter and dark energy.</p>
<ol>
<li><p>We lack direct methods to identify and observe dark matter and dark energy, though they form a substantial component of our universe without us even getting a trace of them, and we have known of them just by imagining based on the results of theories.</p>
</li>
<li><p>We can learn from simulations and observational data (provided with the help of ML) to predict dark matter distribution. Also, modelling how it influences the expansion of the universe would just be awesome.</p>
</li>
</ol>
<p>Talking of petabytes, modern surveys like Euclid Mission, generate enormous datasets that even humans can't process manually. So, ML can be employed into:</p>
<p>Classifying these petabytes of data, into the types of celestial bodies (galaxies, quasars).</p>
<p>The main utilisations of ML are also highlighted in Gravitational Lensing and redshift estimations.</p>

CC BY-SA 4.0
2024-12-14T11:23:47.923Z
2024-12-14T11:23:47.923Z
sheyahi
<h1>A reminder of Machine Learning :</h1>
<p>ML is used to approximate a function, so we can use it to :</p>
<ul>
<li>Approximate a function in order to accelerate a calculation but with less quality</li>
<li>If we don't have a calculation for a certain task, we can show it some example and the model can recreate the task.</li>
</ul>
<h1>The question</h1>
<p>In Astronomy, is there some tasks that are still too complicated/slow to be done ?</p>
<p>We have petabytes of data, what can we do with these ?</p>

CC BY-SA 4.0
2024-12-14T15:00:20.553Z
It's really tough to predict what AI will be good at.  A lot of it is just trying and seeing if it works.  Even with a promising result, it tough to know it's doing what you think it's doing.
Greg Miller
<data-analysis>
What difficulties still exists in astronomy that could be solved (partially) with Machine Learning?
Arthur Delannoy
# What difficulties still exists in astronomy that could be solved (partially) with Machine Learning?

(Asked by: username_0 on 2024-12-10)

# A reminder of Machine Learning :

ML is used to approximate a function, so we can use it to :

* Approximate a function in order to accelerate a calculation but with less quality
* If we don't have a calculation for a certain task, we can show it some example and the model can recreate the task.

# The question

In Astronomy, is there some tasks that are still too complicated/slow to be done ?

We have petabytes of data, what can we do with these ?

**Tags:** data-analysis

> **Comments:**
> **username_1**: It's really tough to predict what AI will be good at.  A lot of it is just trying and seeing if it works.  Even with a promising result, it tough to know it's doing what you think it's doing.  

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## 2 answers

### Answer by username_2 on 2024-12-10. Score: 4

The age of an isolated star is difficult to determine. There are various properties that are age-dependent, but also depend in complex, non-linear ways on stellar mass, composition and each other. Examples include luminosity, rotation, magnetic activity and various chemical abundances.

On the other hand, the ages of stars that belong to *clusters* can be estimated with some accuracy and precision using for example the main sequence turn-off.

That means an ML method can be trained to estimate stellar ages using the features of stars in clusters of known age, circumventing the need for detailed evolutionary models of the age-dependence of those features (which are often uncertain or non-existent). The trained model can then estimate the ages of isolated stars. See for example [Weaver et al. (2024)](https://arxiv.org/abs/2409.07523) (on which I am a coauthor).

A similar example has just been published that uses a "conditional normalising flow" neural network to model the evolution of stellar rotation rates in order to provide an estimate of stellar age ([Van-Lane et al. 2024](https://ui.adsabs.harvard.edu/abs/2024arXiv241212244V/abstract)).


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### Answer by username_3 on 2024-12-14. Score: 2

Like the answer by username_2, there are many other instances of utilising AI and ML in astronomy, for example:

Detecting gravitational waves involves comparing observed signals with extensive libraries of theoretical waveforms. ML models, like convolutional neural networks (CNNs), can:

1. Rapidly identify candidate signals from noisy data.
2. Reduce reliance on exhaustive template matching by approximating waveform calculations.

Another mystery of the universe is dark matter and dark energy.

1. We lack direct methods to identify and observe dark matter and dark energy, though they form a substantial component of our universe without us even getting a trace of them, and we have known of them just by imagining based on the results of theories.
2. We can learn from simulations and observational data (provided with the help of ML) to predict dark matter distribution. Also, modelling how it influences the expansion of the universe would just be awesome.

Talking of petabytes, modern surveys like Euclid Mission, generate enormous datasets that even humans can't process manually. So, ML can be employed into:

Classifying these petabytes of data, into the types of celestial bodies (galaxies, quasars).

The main utilisations of ML are also highlighted in Gravitational Lensing and redshift estimations.


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