Application of machine learning techniques for analysing molecular emission lines

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10 - 14 MARCH 2025
Ka olina Plaki ina1, Ma ia Ki sano a1, And ej Os o skii2, Alina Gimalie a2, S e lana Salii2
1Ins i u e o As onomy o he Russian Academy o Sciences
2Ins i u e o Na u al Sciences and Ma hema ics, U al Fede al Uni e si y
Machine Lea ning o Molecula
Emission Analysis
Con ac me: plaki ina.k @ou look.com
Mo i a ion and goals
How do we classi y housands o molecula
emission sou ces e icien ly?
h ps://www.eso.o g/public/images/eso1606e/
By applying machine lea ning echniques, we aim o explo e whe he an algo i hm can effec i ely
iden i y diffe en ypes o sou ces wi hin he da ase based on ea u es om he MALT90 ca alog.
Da ase MALT90
J. M. Ra hbo ne e al. 2016
We e ined only hose wi h  les 40% co e ge
Clus e isa ion was pe o med wi h in eg a ed in ensi ies o 5 molecula lines + FLUX a 870µm
A — p o os ella clumps (753 objec s)
H — ex ended HII egions (688),
U — unce ain (673),
Q — IR quiescen clumps (616),
P — PDR egions (345),
C — compac HII egions (171)
Resul s Ech do ep esen s n objec om MALT90
Silhoue e sco e > 0.5 —> clus e is ion ws success ul
Resul s (HDBSCAN + -SNE)
CCH
N2H+
HCN
HNC
HCO+
ATLASGAL 870 𝜇m
00.1 0.4
0.2 0.3
Fea u e impo ance
Numbe o objec s
Classi ica ion
Classi ica ion
A — p o os ella clumps (753 obj)
H — ex ended HII egions (688),
U — unce ain (673),
Q — IR quiescen clumps (616),
P — PDR egions (345),
C — compac HII egions (171)

Resul s (HDBSCAN + -SNE)
Numbe o objec s
Classi ica ion
Classi ica ion
1) The e  e wo s ble clus e s:
•S  - o ming egions + ex ended H II egions:
A (51–54%) nd H (47–50%)
• D k in  ed clumps + s  - o ming egions:
Q (26–27%) nd A (19–20%).
3) The p esence o 870 µm dus emission d  does no
significn ly ffec he clus e ing esul s, no does he
exclusion o U-clssified (unce in) objec s.
2) The key e u es used o clus e ing we e he in eg  ed
in ensi ies o he C₂H nd N₂H⁺ lines.
Con ac me: plaki ina.k @ou look.com