Time variation of the correlation of the flux variations
between 6.7GHz methanol maser spectral components
in the massive star forming regions
Yukari Imaizumi , Kenta Fujisawa , Satoko Sawada-Satoh (Yamaguchi University) Yoshinori Yonekuna (Ibaraki University)

Background & Purpose？

6.7GHz methanol maser is one of the tracers for massive star forming region. Yet, the conditionof the maser excitation and the origin of flux variation have not been understood. To
investigate the formation process of massive stars with this maser, it is necessary tounderstand the properties of 6.7GHz methanol maser. In this study, we analyzed the spectra of
6.7GHz methanol maser for 16 sources that have been monitored for a long time at Ibarakistation. Based on the correlation of flux fluctuations in these sources, we discuss the excitationand variability model of 6.7GHz methanol maser.

Analytical method

How to choose
We have selected 16 sources from 452 sources of 6.7GHz methanol
maser that meet two conditions as follows.
(1) Long-term observations (over 250 days) from 2013 to 2019.
(2) There are 3 or more peaks with SNR of 80 or more.
Out of 16 sources, the results of 9 sources are presented. The rest 5
sources have spectra of seriously overlapping components ordisappeared during the observing period.
＊Details of MonR2 and W75N are below

Spectra and Light curve

In order to identify each component, the Gaussian function was fitted
to the peak on the spectrum using the method of least squares. We
create a light curve and a dispersion chart, from the identification ofmaser components. Then we calculate the correlation coefficient.

In this study, a correlation coefficientof 0.5 or less was judged to be nocorrelation. If there is a correlation
between components, they are
distributed on the deviation
distribution line of each component(see the lower left figure), and if
there is nocorrelation, they are
distributed in acircle around thecenter (see thelower right figure).

G9.621+0.196

G49.490-0.388(W51)

G30.757-0.048

G59.78+0.06

G33.641-0.228

G109.871+2.114(Cep A)

G35.197-0.743

G12.681-0.182

A＠G213.71-12.60 (Monoceros R2)

G81.87+0.78(W75N)

D

This maser observed from this source is considered to haveperiodic intensity fluctuations.
We here focus on component of B, C and D. In the past
research of MonR2 at Yamaguchi University, the spectral
components with three distinct velocities have reported to havea period of about 20 days, so they thought the threecomponents have the same excitation source.
In this study, in order to discuss the change in the correlation,
the variance of the correlation coefficient and the intensitychange was calculated by shifting the time by 250 days in the
period of 500 days.

BWe here focus on component C,D,F,G.
There are some peaks from velocity of 3km /s
to 6km/s and two peaks in a little away.
(7.278±0.016km/s & 9.434±0.006km/s )C

It was found that the change in correlation might not be
Two correlation trends are
explained by one excitation
sourcefound. In MJD57293-57793,
the 2nd correlation
trend(steeper) appears.
After that, the 1stcorrelation trend
disappears. The difference
between before and after
the change is as follows.
1. Maser spot
environment haschanged2. Has the same velocity
but radiates fromdifferent spots

Multiple components overlap and cannot bedecomposed.(like component A)
Regarding the source that could be analyzed,
the data that could not be fitted was judged
visually, and there was no data from thelight curve and correlation calculation.

CDF
G

C-D and C-F show continuous goodcorrelations (>0.5).But C-G does notshow significant correlation (<0.5)
during MJD57042-58292(<0.5).
From the fact that the velocity of G isfar from other maser components, Gis considered that influenced by anexcitation a source different fromother components in MJD57042-58292.

MJD56793-57793: A strong
correlation (>0.8) is seen.
MJD57793-58293: No
significant correlation (0.3).
This result shows that theexcitation source of the maser
differs depending on the time.

It was found that the change incorrelation might not be explainedby one excitation source

G9.621+0.196

Future work

The spectrum consist of several small components at~ -0.2 km/s andthe brighter component at~ 1.3 km/s.
We focus on components C and D, separated by 1.270 ± 0.02 km/s.

We consider the relationship between spatial distance and correlation by combining monitoring observation with asingle-dish and observation of spatial distribution by VLBI. Make sure that the highly correlated component pairs
are spatially close.

Discusses the model of properties and excitation mechanism of 6.7GHz methanol maser

SummaryCーD
cor 0.533

cor 0.294

cor 0.353

cor 0.601

When the time delay(τ) is 0,
no significant correlation. But
when the correlation function
was calculated, there was acorrelation with >0.5
at τ=-27days.It implies thatthe distance of C from B is5000AU(27days×light speed).
Than these excitation sources
are considered to be the same.

・16 sources were selected from the 6.7 GHz methanol maser sources monitored at Ibaraki University, and thespectra were separated to calculate the correlation coefficients and correlation functions of each flux density
over time.
・From the correlation coefficient and variance of the deviation, it was found that there is a source whose
correlation between components changes with time(MonR2, W75N) .
MonR2 B-D
Correlation (>0.5)→No correlation(<0.5)
MonR2 B-C
The slope of the variance changes
・ By the correlation change, it is difficult to assume that all components on the spectra observed by eachsource have intensity fluctuations due to a single excitation source.
・ It is necessary to combine VLBI observations with single-dish observations to see if there is a correlation
between correlation changes and spatial changes.