Properties of Interstellar Dust in the MBM 18-19 High-Latitude Cloud Complex

Properties of Interstellar Properties of Interstellar Dust in the MBM 18-19 High-Dust in the MBM 18-19 High-

Latitude Cloud ComplexLatitude Cloud Complex

Vernon H. Chaplin, Kristen A. Vernon H. Chaplin, Kristen A. Larson, and Perry A. GerakinesLarson, and Perry A. Gerakines

Stars Studied in the Stars Studied in the MBM 18-19 RegionMBM 18-19 Region

Interstellar CloudsInterstellar Clouds Made up of gas and small dust particlesMade up of gas and small dust particles Dust scatters and absorbs light from stars, Dust scatters and absorbs light from stars,

changing the brightness and color that we changing the brightness and color that we observeobserve

Interstellar dust clouds are the location of new Interstellar dust clouds are the location of new star formation in our galaxystar formation in our galaxy

We study cloud properties by looking at the effect We study cloud properties by looking at the effect dust grains have on starlightdust grains have on starlight

Studying StarsStudying Stars

Stars emit light at many different wavelengths Stars emit light at many different wavelengths across the electromagnetic spectrumacross the electromagnetic spectrum

We can measure the level of emission at We can measure the level of emission at individual wavelengthsindividual wavelengths– Systems of “passbands” designate specific Systems of “passbands” designate specific

wavelength rangeswavelength ranges Johnson Photometric System: Johnson Photometric System: U U {{B V RB V R}} {{I J H K L I J H K L

MM}} Magnitude System:Magnitude System:

– Originally created by either Ptolemy or Hipparchus to Originally created by either Ptolemy or Hipparchus to classify the apparent luminosities of starsclassify the apparent luminosities of stars

– Numerically small magnitude = bright starNumerically small magnitude = bright star– Numerically large magnitude = dim starNumerically large magnitude = dim star

Visual Infrared

Finding the distances to Finding the distances to starsstars

Apparent visual magnitude (Apparent visual magnitude (VV or or mmVV))– Measures how bright a star appears from earthMeasures how bright a star appears from earth

Absolute visual magnitude (Absolute visual magnitude (MMVV))– Measures the star’s actual luminosityMeasures the star’s actual luminosity– Defined as the apparent magnitude the star would Defined as the apparent magnitude the star would

have at a distance of 10 parsecshave at a distance of 10 parsecs– MMVV can be found from a star’s spectral typecan be found from a star’s spectral type

VV – – MMVV – – AAVV = 5 log = 5 log dd – 5 – 5 The distance to an interstellar dust cloud The distance to an interstellar dust cloud

such as MBM 18-19 can be estimated by such as MBM 18-19 can be estimated by plotting the reddening vs. distance for stars plotting the reddening vs. distance for stars in the direction of the cloudin the direction of the cloud

Effects of Dust on Effects of Dust on StarsStars Star color is the difference in a star’s magnitudes at Star color is the difference in a star’s magnitudes at

two different wavelengthstwo different wavelengths– E.g. E.g. BB – – VV

Dust changes the color of starlight by extinguishing Dust changes the color of starlight by extinguishing shorter wavelengths of light more effectivelyshorter wavelengths of light more effectively– This process is known as reddeningThis process is known as reddening

Color Excess: the change in a star’s apparent color Color Excess: the change in a star’s apparent color caused by the dustcaused by the dust– E.g. E.g. EEB-VB-V = (B-V) – (B-V) = (B-V) – (B-V)stdstd

– Color excesses can be used to estimate the total visual Color excesses can be used to estimate the total visual extinction (extinction (AAVV))

– Two estimates based on galactic averages are:Two estimates based on galactic averages are: AAVV ≈≈ 3.05 3.05 EEB-VB-V

AAVV ≈≈ 1.1 1.1 EEV-KV-K (we used this approximation in our calculations)(we used this approximation in our calculations)

Reddening vs. Reddening vs. DistanceDistance

0

0.1

0.2

0.3

0.4

0.5

0.6

0 100 200 300 400 500 600 700 800

Distance (parsecs)

E(B

-V)

Difficulties in Finding Difficulties in Finding the Distance to the the Distance to the CloudCloud Original graphs contained many near (less Original graphs contained many near (less

than 50-60 parsecs), highly reddened starsthan 50-60 parsecs), highly reddened stars– We know that there are no interstellar dust clouds We know that there are no interstellar dust clouds

this close to earth, so either our distance or this close to earth, so either our distance or reddening calculations had to be erroneousreddening calculations had to be erroneous

In addition to having spectral types, stars are In addition to having spectral types, stars are grouped into five luminosity classesgrouped into five luminosity classes– Supergiants (I, II), Giants (III), White Dwarfs (IV), Supergiants (I, II), Giants (III), White Dwarfs (IV),

Dwarf/normal stars (V)Dwarf/normal stars (V) We realized that the anomalous stars in the We realized that the anomalous stars in the

graphs were probably giants, so we had graphs were probably giants, so we had severely underestimated their distancesseverely underestimated their distances– dd = 10^(( = 10^((V V – – MMVV – – AAVV + 5) / 5)+ 5) / 5)

The Effect of Assuming the The Effect of Assuming the Presence of Three Class III Presence of Three Class III Giants in one sub-region Giants in one sub-region studiedstudied

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0 50 100 150 200 250 300 350 400 450 500Distance (parsecs)

E(B

-V)

Map of Total Visual Map of Total Visual ExtinctionExtinction

The Ratio of Total to The Ratio of Total to Selective Visual Extinction Selective Visual Extinction (R(RVV))

RRVV = = AAVV / / EEB-VB-V Because Because AAVV is difficult to determine directly, is difficult to determine directly,

knowing an accurate value for knowing an accurate value for RRVV can be very can be very importantimportant

RRVV also gives us information about the also gives us information about the properties of dust grains in a cloudproperties of dust grains in a cloud– Bigger Bigger RRVV implies bigger dust particles implies bigger dust particles

One way to calculate One way to calculate RRVV is to independently is to independently calculate calculate AAVV and and EEB-VB-V using an approximation using an approximation such as such as AAVV ≈ 1.1 ≈ 1.1 EEV-KV-K

Another, potentially more precise method is to Another, potentially more precise method is to use extinction curvesuse extinction curves

Map of RMap of RVV

RRVV vs. Total Visual vs. Total Visual ExtinctionExtinction

RV is generally expected to increase in regions of high AV, as dust particles grow by accretion in the densest regions of the cloud. However, we observed only a slight correlation in MBM 18-19.

0

2

4

6

8

10

12

14

0 0.5 1 1.5 2 2.5 3

Av

Rv

Star Name: HD 24380

-3

-2

-1

0

1

2

3

4

0 0.5 1 1.5 2 2.5 3 3.5

1/λ

E(λ

-V)

/ E(B

-V)

Calculating RCalculating RVV from an from an Extinction CurveExtinction Curve

Unfortunately, we had too many free parameters and too few data points for most stars to perform an accurate curve fit, so we had to settle for our earlier estimated RV values.Y-intercept = -RV

E(λ -V) / E(B-V) = ε*λ-β - RV

Star FormationStar Formation

Interstellar clouds such as MBM 18-19 are the Interstellar clouds such as MBM 18-19 are the location of star and planet formation in our location of star and planet formation in our galaxygalaxy

In the early years of its life, a star is In the early years of its life, a star is surrounded by a disk of gas and dust surrounded by a disk of gas and dust particles which scatter and absorb photons particles which scatter and absorb photons and emit radiation in the infraredand emit radiation in the infrared

Interstellar dust affects the color of stars in a Interstellar dust affects the color of stars in a predictable waypredictable way– Therefore, we can detect young stars by identifying Therefore, we can detect young stars by identifying

stars which have abnormal reddeningstars which have abnormal reddening

Infrared Color-Color Infrared Color-Color DiagramDiagram

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

-0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

H-K

J-H

Standard Reddening Vector(J-H) / (H-K) = 1.60

Intrinsic Color-Color Curve

20 potentially newly forming stars were found

ConclusionsConclusions

Increasing the size of the data set from previous Increasing the size of the data set from previous studies has led us to a better understanding of studies has led us to a better understanding of the distance to the cloudthe distance to the cloud

Our maps of Our maps of AAVV and and RRVV show that these show that these parameters have their highest values along the parameters have their highest values along the brightest region of the infrared emission mapbrightest region of the infrared emission map

Unlike the nearby Taurus Dark Cloud Complex, Unlike the nearby Taurus Dark Cloud Complex, little correlation between little correlation between AAVV and and RRVV exists in MBM exists in MBM 18-1918-19– Different cloud structure and grain propertiesDifferent cloud structure and grain properties

MBM 18-19 is an active star-forming regionMBM 18-19 is an active star-forming region

Future WorkFuture Work

Further observations are required to fill in Further observations are required to fill in missing pieces from our studymissing pieces from our study

Data regarding the polarization of starlight by Data regarding the polarization of starlight by the dust will help us to further understand the dust will help us to further understand dust propertiesdust properties

Further photometric data is also needed for Further photometric data is also needed for precise calculations of precise calculations of RRVV in individual lines in individual lines of sightof sight

MBM 18-19 is an ideal region for the study of MBM 18-19 is an ideal region for the study of nearby new star formationnearby new star formation

AcknowledgementsAcknowledgements

We acknowledge support from the National We acknowledge support from the National Science Foundation (NSF)-Research Science Foundation (NSF)-Research Experiences for Undergraduates (REU)-site Experiences for Undergraduates (REU)-site award to the University of Alabama at award to the University of Alabama at Birmingham (UAB) under Grant No. DMR-Birmingham (UAB) under Grant No. DMR-0243640 0243640