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Resolving Sub-Kiloparsec Star-Forming Regions Within

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Resolving Sub-Kiloparsec Star-Forming Regions Within Clumpy Star Formation in Gravitationally Lensed Galaxies at 0.5 < z <1.0 Brian Molina Merino1,2, Greg Walth1, Shelley Wright3, Maren Cosens3 1Carnegie Observatories, 2San Francisco State University, 3University of California, San Diego Bridging The Gap Between Low and High Redshift Abstract Watershed? Star Formation Galaxies at high redshift (z > 1) contain HII regions that can be up to 100 times Plotting the radial profile of an image (like in figure 2) will show peaks which larger and many orders of magnitude brighter than the regions observed in the correspond to unique clumps. Watershed routines are designed to identify local M51 BX610 nearby universe (z ~ 0). This large discrepancy raises the question of whether or maxima in images and extent of each of the clumps (i.e. the boundary between z = 2.2103 z =0.002 not these regions are larger versions of their local analogs or if they are an peaks). Peaks significantly above the noise threshold (i.e. 5 sigma) are indication of an unidentified mode of star formation. While HII regions in the considered real features. nearby universe are well-studied, high-redshift HII regions are limited by sample sizes and angular resolution, as only the brightest and largest can be studied, even with Hubble Space Telescope’s (HST) resolution. In order to surpass the resolution Watershed to Clump Finding Algorithm: 8 kpc limit, we utilize massive galaxy clusters as gravitational lenses to resolve sub- kiloparsec scales of distant star-forming clumps. The sample of sources are • If you invert the profile to act as a potential and fill the potential with water, 18 kpc selected from Cluster Lensing and Supernova Survey with Hubble (CLASH), the isolated peaks will fill first which is a survey of 25 massive galaxy clusters imaged in 16 bands with HST ACS • Water spilling into neighboring regions maps the extent of the unique regions and WFC3. With the aid of gravitational lensing, we are able to measure the properties of star-forming clumps within a previously under sampled redshift range and compare them with local and distant (z > 1) star-forming galaxies. NASA Hubble Heritage Team Förster Schreiber et al. 2011 • Star-forming galaxies at local redshifts (z~0) have many clumps that are small and faint compared to galaxies at higher redshifts (z > 1) which have bright and large clumps Watershed Clump Detecting Algorithm • Are the large clumps just scaled up analogs of what we observe locally? Currently, our sample contains around 172 galaxies with some containing multiple • Is there an unidentified mode of star formation behind this discrepancy? clumps. To speed up the clump measuring procedure for all the galaxies selected in the CLASH sample, we automated the watershed process to run on all 16 bands of each galaxy cluster. Our algorithm does the following: Measuring Star-Forming Clumps in Gravitationally • Allows the user to choose which filters they would like to use Lensed Galaxies • Take in a region file saved in image coordinates Figure 3: (Left) A thumbnail of a clumpy galaxy in Abell 383 with a red line • Creates a unique directory for each cluster and subsequent region where all FITS region that crosses over the galaxy’s bulge and two clumps. (Right) A plot of the • Select galaxies with photometric redshifts between 0.5 < z < 1.0 files and tables will be saved counts versus position on the red line. The three peaks correspond to the bulge • Visually inspect each cluster for galaxies within the redshift range that contain • Creates a thumbnails of each region for each filter and two clumps within the galaxy. “clumpy” morphologies. • Run watershed algorithm • Run 2 dimensional watershed routine that was put together using scikit-image morphology package over every thumbnail • Verify existence of clumps Discussion • Generate segmentation maps of clumps • Creates 2 tables that document properties such as the number of clumps detected A preliminary analysis has been conducted on the clusters Abell 209, Abell 383, • Measure positions and sizes of clumps in each filter and pixel coordinates of each centroid and their corresponding sizes Abell 1423, MACS 1311, and MACS 1423 and we find the following: • Reconstruct images in the source plane to determine intrinsic sizes of clumps • Creates a plot (Figure 2) for each filter to visually check that the code is working • A population of large low SFR clumps at intermediate redshift, previously undiscovered by field surveys as expected • A population of large low SFR clumps at intermediate redshift, previously • Use photometry to extract star formation rates undiscovered by field surveys. • We have automated a watershed routine to run on full CLASH sample (25 clusters), results coming soon! • The next steps include incorporating spectroscopic redshifts to improve the accuracy of the star formation rates of individual clumps Figure 2: Output of watershed algorithm. The top-left panel displays the thumbnail Figure 1: RGB image of the massive galaxy cluster Abell 383 observed with that was given to the code. The top-right panel shows where the centers of the Figure 4: The above plot contains clumps between redshifts 0 < z < 5. Our data HST’s F606W, F814W, and F105W filters. The boxed galaxy has a photometric detected clumps are. The bottom-left panel is a segmentation map which shows points are represented as red diamonds. Despite being at intermediate redshift, redshift of 0.579 and is being gravitationally lensed by the foreground cluster which pixels belong to each clump. The bottom-right panel is the segmentation they are as large as high redshift clumps but not as bright. allowing us to see its large star forming regions. map overlaid over the original image..
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