A Mid-Infrared Search for Variable Stars in the Milky Way Galaxy

1. A Mid-Infrared Search for Variable Stars in the Milky Way Galaxy Brian H. Brooks1, 2, Robert A. Benjamin2,3 & the GLIMPSE Team 1Old Dominion University , Norfolk , VA, United States, 2University of Wisconsin-Madison, Madison , WI, United States , 3University of Wisconsin-Whitewater, Whitewater , WI, United States  Galactic Location of Variable Star Candidates References [1] A GLIMPSE of the Milky Way http://www.alienearths.org/glimpse/ [2] James Binney and Michael Merrified ,GALACTIC ASTRONOMY, Princeton University Press (1998) [3] Sparke and Gallagher, Galaxies in the Universe: An Introduction, 2nd ed, Cambridge University Press (2007) Fig 1a: Coverage of Spitzer/GLIMPSE observations in 2004-2005 (red) and Spitzer/Deep GLIMPSE in 2012-2013 (white) superimposed on an optical panorama of the inner Galaxy by Axel Mellinger. Fig 1b: Overlap region between Deep GLIMPSE survey (shown with white boundary) and GLIMPSE/MIPGAL image. Color contours show areas with an excess of variable stars candidates. This is probably due to overlap areas in the GLIMPSE survey. In these areas, flux uncertainties are lower and our ability to extract variable star candidates improves. Introduction Visual inspection/confirmationhttp://www.astro.wisc.edu/~benjamin/Var/tensig_pg0.html Project Overview Fig 1 above shows the outlines of two mid-infrared surveys of the Milky Way Galaxy. Data for both of these survey was gathered using NASA’s Spitzer Space Telescope. Onboard Spitzer, the IRAC (Infrared Array Camera) instrument allowed for a GLIMPSE (Galactic Legacy Infrared Midplane Extraordinaire) survey of the Milky Way Galaxy. The map shows coverage from two overlapped projects GLIMPSE and Deep GLIMPSE. Between these two projects, 27,703,930 sources in 150 square degrees of sky were matched. Sources were detected in four IRAC bands from GLIMPSE (2004-2005) with bands of 3.6, 4.5, 5.8 and 8.0 microns. DEEP GLIMPSE (2012-2013) was carried out with only bands 3.6 and 4.5 microns after the beginning of the Warm Spitzer mission. As well as being separated in time, these two surveys differed in observing strategy with GLIMPSE consisting of 2 x 1.2 second exposures and Deep GLIMPSE consisting of 3 x 10.4 second exposures (in HDR mode). Much of the overlap between the two surveys was ranked as Priority 3 observations but were fortunately scheduled. Of the total number of matched sources, 100,098 sources, 0.36% of the total were observed to vary by more than 2sigma in the same direction in all the bands that were observed in common. We characterize the sources with a variability parameter: vpar=(m1-m2-moff)/sqrt{s12+s22}$, where m1,2 are the magnitudes at epoch 1 or 2, s is the uncertainty, and moff is a position and magnitude-dependent offset value between different projects. A table of the number of variable star candidates and their SIMBAD matches is given below. Fig 2: GLIMPSE Fig 3: DEEP GLIMPSE Fig 4 Histogram of the number of variable stars per mid-infrared magnitude showing the effect of cutting at different variability parameters. We see that the variables are characterized by two peaks: a bright peak consisting principally of evolved AGB stars, and a fainter peak that has a much more heterogenous collection of sources. The relative stengths of these peaks changes as a function of Galactic direction, with the bright peak being more dominant in the inner Galaxy directions. This distribution is related to the intrinsic probability of variability as shown in Figure 5. Displayed above on the left is (Fig 2) a star from the GLIMPSE survey and to the right (Fig 3) the same star from the DEEP GLIMPSE survey. These two images were combined using DS9 imaging software to create blinking animated GIFs. All variable sources were found by a program that the GLIMPSE team developed. This program compared matched sources and identified variations in the apparent magnitudes. Blinking GIF’s were created to test that the 10-sigma cuts were variables or not. The 10-sigma cut contains a total of 1060 variable stars, which I verified by visual inspection. SIMBAD matches to Variable Star Candidates Variable Star Demographics Environment Classification Fig 6: Infrared Dark Cloud (IRDC) Fig 7: Star Forming Region (SFR) Fig 8: Near Star Forming Region (nearSFR) Fig 9: Field Star Above are some examples of the environment classification criteria of our 1060 10-sigma variables. An environment could either be an IRDC (Infrared Dark Cloud), nearSFR (near Star Forming Region), SFR( Star Forming Region), or Field Star. An infrared dark cloud is a dense region of giant molecular cloud that is cold. These regions are associated with the early stages of star forming in the Milky Way. Each images is labeled with my environment classification. IRDCs were identified by the presence of a green cluster of stars as in Fig 6.  Fig 7 shows a SFR characterized by the glow from chemical compounds called Polycyclic Aromatic Hydrocarbons (PAHs). These compounds are excited by ultraviolet radiation given off by massive stars causing the PAHs to glow in the 8.0 micron band. This glow lets us know if there was recent star formation. Any star that was not in a SFR but in the 2 acrmin frame of a SFR was classified as a “near” star forming region which is shown in Fig 8. Lastly if, SFR, nearSFR, or IRDC were not present then the environment was a Field Star as shown in Fig 9. The 10-sigma list was also uploaded to SIMBAD and separated by bright and faint stars. In total, we recovered 352 SIMBAD sources based on our 10-sigma variable stars. We have recovered proper motion stars, Be stars, dark clouds, circumstellar masers, emission line stars and Wolf-Rayet stars. The total number of classification for all variability levels are given in the table above. Fig 5 The probability of variability as a function of observed mid-infrared magnitude for different amplitude variations. We see that the probability of a source being variable increases by more than an order of magnitude for mid-infrared magnitudes greater than m[3.6]=9. This increase is presumably due to the larger fraction of AGB sources at these magnitudes, but the case for the asymptotic behavior of variability at faint magnitudes is still under investigation. Acknowledgements: This work was partially supported by the National Science Foundation’s REU program through NSF Award AST-1004881 to the University of Wisconsin-Madison and Spitzer Guest Investigator Program