Jessica Craig
Gather.town id
GC10
Poster Title
Galaxies in Clusters and Groups Behind the Magellanic Clouds
Institution
Keele University
Abstract (short summary)
Environment plays a key role in influencing the evolution of galaxies, and clusters provide ‘laboratories’ in which to study galaxy interactions and the effect of environment on these. Meanwhile, the VISTA Magellanic Clouds Survey (VMC) provides a high-resolution near-infrared view covering the Magellanic Clouds and Magellanic Bridge. Due to its high resolution and sensitivity, the VMC also contains information about many background galaxies, most of which have not been captured by past surveys. Therefore, it is now possible to investigate galaxy clusters and groups that would previously have been obscured by foreground stars, providing us with new ‘laboratories’ in which to study galaxy evolution. We use VMC data in combination with data from optical and infrared surveys and new radio continuum images from the Australian SKA Pathfinder to study the evolution of galaxies in clusters and groups. To this end, we map galaxy clusters in the VMC survey using photometric colours and redshift estimates. This allows us to quantify the environments in which background galaxies reside and to study their properties by using near-infrared as a tracer of stellar mass and AGN dust and using radio lobes as a probe of the intracluster medium. We approach the problem of mapping background galaxies in areas of high stellar contamination using an automated method that we intend to be transferrable to other near-infrared surveys that present similar challenges to the VMC, such as those covering the galactic plane of the Milky Way.
Plain text (extended) Summary
The VISTA Magellanic Clouds Survey (VMC) is a high-resolution near-infrared survey, covering the Magellanic Clouds and Magellanic Bridge, but also contains information about many background galaxies. We locate and study galaxy clusters and groups in the VMC. We will use machine learning combined with multiwavelength observations to identify cluster members and study their properties.
The VMC uses the VISTA survey telescope and has a survey area of 170 square degrees, covering the Small and Large Magellanic Clouds, the Magellanic Bridge and part of the Magellanic Stream, with observations taken between November 2009 and October 2018. It uses the near-infrared filters Y, J and Ks, has a resolution of 0.8 arcseconds and a sensitivity of 20.3 to 21.9 Vega magnitude.
We search for background galaxies in this area because a variety of multiwavelength data is available, there is a clear line of sight to background galaxies outside the densest stellar regions, background galaxies provide a reference frame for stellar studies and discovering new galaxy clusters and expanding knowledge of known ones provides new opportunities for studying galaxy evolution. The VMC also has a high enough resolution to see morphological detail in background galaxies.
There are two known galaxy clusters behind the Magellanic Clouds with redshifts of about 0.037 and 0.065. We show a histogram showing redshift (x axis) and number of sources (y axis) for 6dfGS data (Jones et al. (2009)) cross-matched to sources classified as galaxies or probable galaxies in the VMC (Hambly et al. (2008)), showing only redshift less than or equal to 0.1. There are two peaks at redshifts just under 0.04 and just over 0.06, with about 160 sources at each peak. We show a map of VMC sources classed as galaxies or probable galaxies with redshift between 0.035 and 0.04 and between 0.063 and 0.067. Most sources with redshift between 0.035 and 0.04 are in the west of the Large Magellanic Cloud and most sources with redshift between 0.063 and 0.067 are in the Small Magellanic Cloud or the East of the Magellanic Bridge (the side closest to the SMC). We show a radio image from the Austalian SKA Pathfinder overlaid on a VMC image of some galaxies within the redshift about 0.037 cluster (Image Credit: Pennock et al. (2021)). One galaxy has a long radio `tail’, which we attribute to interaction with the intergalactic medium.
We will address the challenges of distinguishing between background galaxies and foreground stars and classifying cluster membership by using machine learning. This can consider many variables at once, potentially avoiding misclassification based on a single variable. Cluster members can be identified by colour, morphology and other properties. Once we have a sample of cluster members, others can be identified by association. We show a VMC image of a cluster candidate with a compact core, estimated redshift 0.8 (from a model based on Bruzual and Charlot (2008)) and a colour-colour diagram for sources in this cluster. Stars, cluster members and isolated galaxies appear separated, with sources with a greater than 50 percent probability of being stars (Hambly et al. (2008)) shown on the left.
Redshifts may be identified using existing photometric redshifts for VMC background galaxies (Bell et al. 2019, 2020), using existing photometric models or machine learning algorithms, using spectroscopic redshifts from surveys like 6dfGS (for low-redshift galaxies) and by taking our own spectroscopic observations.
We may use nearby clusters to study interactions with the intracluster medium and higher-redshift clusters to study quenching and cluster formation. An automated classification system may be adapted for other VISTA surveys and/or areas with high stellar contamination, such as the Milky Way galactic plane.
The VMC uses the VISTA survey telescope and has a survey area of 170 square degrees, covering the Small and Large Magellanic Clouds, the Magellanic Bridge and part of the Magellanic Stream, with observations taken between November 2009 and October 2018. It uses the near-infrared filters Y, J and Ks, has a resolution of 0.8 arcseconds and a sensitivity of 20.3 to 21.9 Vega magnitude.
We search for background galaxies in this area because a variety of multiwavelength data is available, there is a clear line of sight to background galaxies outside the densest stellar regions, background galaxies provide a reference frame for stellar studies and discovering new galaxy clusters and expanding knowledge of known ones provides new opportunities for studying galaxy evolution. The VMC also has a high enough resolution to see morphological detail in background galaxies.
There are two known galaxy clusters behind the Magellanic Clouds with redshifts of about 0.037 and 0.065. We show a histogram showing redshift (x axis) and number of sources (y axis) for 6dfGS data (Jones et al. (2009)) cross-matched to sources classified as galaxies or probable galaxies in the VMC (Hambly et al. (2008)), showing only redshift less than or equal to 0.1. There are two peaks at redshifts just under 0.04 and just over 0.06, with about 160 sources at each peak. We show a map of VMC sources classed as galaxies or probable galaxies with redshift between 0.035 and 0.04 and between 0.063 and 0.067. Most sources with redshift between 0.035 and 0.04 are in the west of the Large Magellanic Cloud and most sources with redshift between 0.063 and 0.067 are in the Small Magellanic Cloud or the East of the Magellanic Bridge (the side closest to the SMC). We show a radio image from the Austalian SKA Pathfinder overlaid on a VMC image of some galaxies within the redshift about 0.037 cluster (Image Credit: Pennock et al. (2021)). One galaxy has a long radio `tail’, which we attribute to interaction with the intergalactic medium.
We will address the challenges of distinguishing between background galaxies and foreground stars and classifying cluster membership by using machine learning. This can consider many variables at once, potentially avoiding misclassification based on a single variable. Cluster members can be identified by colour, morphology and other properties. Once we have a sample of cluster members, others can be identified by association. We show a VMC image of a cluster candidate with a compact core, estimated redshift 0.8 (from a model based on Bruzual and Charlot (2008)) and a colour-colour diagram for sources in this cluster. Stars, cluster members and isolated galaxies appear separated, with sources with a greater than 50 percent probability of being stars (Hambly et al. (2008)) shown on the left.
Redshifts may be identified using existing photometric redshifts for VMC background galaxies (Bell et al. 2019, 2020), using existing photometric models or machine learning algorithms, using spectroscopic redshifts from surveys like 6dfGS (for low-redshift galaxies) and by taking our own spectroscopic observations.
We may use nearby clusters to study interactions with the intracluster medium and higher-redshift clusters to study quenching and cluster formation. An automated classification system may be adapted for other VISTA surveys and/or areas with high stellar contamination, such as the Milky Way galactic plane.
URL
j.e.m.craig@keele.ac.uk
Poster file