A machine-learning approach for data mining in astronomy

Evgeny Smirnov, PhD, 4xxi Software Ltd, smirik@gmail.com
A machine-learning approach
for data mining in astronomy

“A blue jay standing on

a large basket of rainbow macarons.”
Source: Benjamin Hilton

“A robot couple fine dining

with Eiffel Tower in the background.”

“A photo of a Corgi dog riding a bike in Times Square.

It is wearing sunglasses and a beach hat.”

Types of ML
• Supervised learning: example inputs and
desired outputs are provided; the goal is to
create a map that binds inputs to outputs.

• Unsupervised learning: no examples are
provided, the goal is to discover hidden
patterns.

• Reinforcement learning: the same as
supervised learning but instead of a
training set there is an environment that
provides the rewards based on the actions

Neural network approach to improve the estimation

of the cosmological parameter

DOI: 10.1017/S1743921314013672

Reducing estimation errors more than twice

DOI: 10.1088/0004-637X/803/2/50

Resonance identification that is 100 times faster

DOI: 10.1093/mnras/stx999

NEA categorisation by machine-learning methods

DOI: 10.3847/1538-3881/aa88be

An attempt to categorise asteroid groups based on ML

DOI: 10.1093/mnras/stz1795

The ML approach to family classification problem (vs HCM)

DOI: 10.1093/mnras/staa1463

A quick method to estimate the stability of a planetary system

DOI: 10.3847/2041-8205/832/2/L22

MMR identification using ML
Recall 98,38 %
Precision 91,01 %
Accuracy 99,97 %

ML companion
Original idea: Carruba et al. 2021, MNRAS,

10.1093/mnras/stab914
Automatic libration identi
fi
cation: Smirnov & Dovgalev, 2018,
SSR, 10.1134/S0038094618040056
https://github.com/smirik/resonances

The Problem
Pure libration 2 Possibly, pure libration –2
Transient resonances 1 Possibly, transient –1
No libration 0

MLfication
Time series
{-2, -1, 0, 1, 2}
categorisation

A machine-learning approach for data mining in astronomy

A machine-learning approach for data mining in astronomy

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