Evolution of Star Formation of Dwarf Galaxies within Extragalactic Cluster Substructures

Transcription

1 Evolution of Star Formation of Dwarf Galaxies within Extragalactic Cluster Substructures Haylee Archer 1 Mentors: Victor Pankratius 2, Cody Rude 2, Michael Gowanlock 2 1 University of North Dakota 2 MIT Haystack Observatory

2 Project Goals Detect galaxy clusters and substructure using VariantDBSCAN Vary clustering parameters that each lead to alternative clustering models of galaxy clusters Detect different shapes and densities to determine substructure within clusters Understand the star formation history of dwarf galaxies Dwarf elliptical (de) galaxies are the most numerous type of galaxy in the universe Dwarf galaxies are highly susceptible to external influences due to their low mass Environmental effects need to be identified Can fall into the cluster either in groups or in isolation

3 Background: DBSCAN Density-Based Spatial Clustering of Applications with Noise Given a set of points in some space, it groups together points that are closely packed together, marking as outliers points that lie alone in low-density regions One of the most common clustering algorithms and highly cited in scientific literature

4 Background: DBSCAN - How it works Points are classified as core points, reachable points, and outliers A point p is a core point if at least MinPts points are within distance ε of it (including p). Those points are said to be directly reachable from p A point q is reachable from p if there is a path where each point is directly reachable from p All points not reachable from any other point are outliers Image credit: Wikimedia Commons user Chire

5 Background: Galaxy Clusters Galaxies are not randomly distributed throughout the universe Grouped in gravitationally bound clusters Contain anywhere from hundreds to thousands of galaxies Smaller structures known as groups Roughly less than 50 galaxies Most common structures of galaxies Coma Cluster Image credit: Copyright , Dean C. Rowe

6 Background: Star Formation Stars are formed in the collapse of hydrogen gas Hotter, blue stars have shorter lifespan than smaller, red stars Blue colors generally indicate recent star formation Color can then be used to detect star forming populations

7 Detecting Clusters Generated Cluster with Substructure Creating mock clusters and substructure Generate background galaxies NFW profile to determine radial distribution of galaxies in clusters and groups Depends on redshift, right ascension and declination range, and background density Dec (degrees) RA (degrees)

8 Detecting Clusters Eps: 0.06 Eps: 0.09 Number of Clusters MinPts Eps: 0.08 MinPts: 21 Determining DBSCAN parameters (Eps, MinPts) to accurately detect clusters and substructure and the correct number of galaxies in them Number of Clusters Eps: 0.03 MinPts: 11 Number of Galaxies

9 Dec (degrees) Detecting Clusters: Separating Substructure Detected Cluster with Generated Cluster and Substructure Detected Substructure with Generated Cluster and Substructure RA (degrees)

10 GMBCG Clusters, Detected Clusters, and Matches SDSS Matching GMBCG Cluster Catalog Clusters (DR7 Data) Dec (degrees) Detected Clusters (DR12 Data) Matched Clusters RA (degrees)

11 NED Matching Redshift Histogram for Matched NED clusters NASA/IPAC Extragalactic Database Selected clusters found with 100+ galaxies in the thumbprint of the SDSS DR12 survey in redshift range z= Matched with NED and checked to see if detected clusters were in same redshift range Number of Clusters Redshift

12 SDSS Matching: Cluster Detection Detected Cluster and Substructure Center of the cluster Cluster galaxies Dec (degrees) Substructure galaxies Background galaxies RA (degrees)

13 Conclusion We can successfully detect SDSS clusters and their substructure using VariantDBSCAN We ve started to look at the colors of the galaxies in our detected clusters and substructure Comparing color of galaxies in substructure with isolated galaxies in the same radial bin Corrections - K corrections, magnitude cuts

14 Future Work Comparing detected clusters to clusters with known substructure Richness Optical richness is the primary mass proxy for clusters Allows us to determine the size of our clusters KiDS data Deeper survey Higher redshift Fewer studies done

15 My wonderful mentors - Cody, Mike, and Victor Phil, Vincent, and Mary Everybody at Haystack My fellow REU students NSF Acknowledgements

16 References Gowanlock, M., et al. 2016, Proc. of the 30th IEEE IPDPS, 760 Kennicutt, R. C. 1998, ARA&A, 36, 189 McKee, C. F., & Ostriker, E. C. 2007, ARA&A, 45, 565 Rozo, E., Rykoff, E. S., Koester, B. P., et al. 2009b, ApJ, 703, 601 Rude, C., 2015, Ph.D. thesis, University of North Dakota

17 K corrections When observing through filters, the galaxy s redshift affects the color seen Magnitude cuts Completeness limits The number of galaxies is expected to increase as a power law with decreasing luminosity As the data becomes incomplete at faint magnitudes, some galaxies are not detected due to their low luminosity Number of Galaxies Number of Galaxies Corrections U-band Magnitude Histogram Completeness limit Color Magnitude (u-band) R-band Magnitude Histogram Completeness limit Color Magnitude (r-band)

18 SDSS Matching: Cluster Redshift Redshift Histogram of Cluster Galaxies Found all galaxies within 16 arcseconds of the detected cluster center Matched galaxies with detected cluster galaxies Histogram of redshift of galaxies to determine redshift of cluster Number of Galaxies Redshift