Page 228 - Special Topic Session (STS) - Volume 1

STS426 Tanuka C.
note that SFRtrue values can never be directly deter-mined, i.e., these are
hypothetical values. One can use physical models to find model based SFR
values as representative of SFRtrue, denoted by SF R (say). Several authors
starting from Kennicut (1998) have used fluxes of electromagnetic waves of
various wave lengths related to star formation e.g. Balmer lines (viz. ,

̃
Kennicut 1998). In this work the author has estimated the integral in a
2
9
galaxy using the relation ( −1 ) = 0.945 × 10 ( ) , where D is
⊙

the distance and ( )is the integral flux in line corrected for Galactic

extinction. The SFR estimate, in the above relation, used that corresponds

to a time scale of ~10 Myr. This is the characteristic time scale for the glow of
most massive stars in a galaxy. Another estimate has been suggested by ?
̃
where the is found from the integral fluxes of far ultraviolet (FUV) lines
that correspond to a time scale of ~ 100 Myr. This estimate not only considers
massive stars but also includes intermediate solar type stars and hence is a
more trust worthy estimate. ? have estimated SFR based on the strength of
[OII] emission line. They found that the SFR is reduced in a cluster environment
compared to field galaxies for the similar concentration indices. Tekola et al.
(2011) have shown that tidal forces in groups and clusters of galaxies has a
strong correlation with SFR at a fixed stellar mass. Wuyts et al. (2011) have
suggested another estimate of on the basis of multiwavelength
̃
photometry starting from FUV to infrared and the estimated SFRFUV +IR >
100 −1 and out to z ~3. These spectroscopically (i.e., through flux of
⊙
̃
energies) estimated SFR (i.e., ) are later computed for huge datasets of
̃
galaxies by expressing in terms of magnitudes e.g., (?) or (?),

using a mathematical relation, which are photometric parameters hence easily
measurable quantities for a large number of galaxies. Finally these physical
model based values have been used to compare with our statistically
estimated SFR (SFRest) values. But the above wavelengths or magnitudes are
not the only indicators of SFR. There are many more parameters e.g neutral
hydrogen mass, environment (as mentioned above), which along with the
previous ones simultaneously affect the SFR. Hence an ideal model of
predicting the SFR of a galaxy is to include as many such parameters as
possible through a multivariate set up. The present work aims such challenge.
Moreover previous studies especially used the scatter diagrams of any two
parameters at a time while ignoring the effect of others and taking whole data
set at a time. These are not suitable in a multivariate set up where all the above
mentioned parameters have the simultaneous effect on star formation history
of galaxies. Also, there are various types of dwarf galaxies depending on color
and surface brightness (?). Therefore while tracing the star formation history
of any particular data set of galaxies we have to be aware of the fact that we
can depict a convincing theory only if we concentrate on homogeneous galaxy
groups, in some way identified. From the above point of view we have first

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