Periodic Reporting for period 1 - GalaxyMergers (Galaxy mergers in the era of large surveys)
Periodo di rendicontazione: 2023-07-03 al 2025-11-02
Galaxies are the largest coherent gravitationally bound objects in the Universe – enormous islands of up to hundreds of billions of stars, accompanied by huge clouds of gas and dust and, likely, an even larger mass of dark matter. The galaxies that we observe in the Universe around us show a wide range of properties – sizes, shapes, populations of stars and internal structures. Upon seeing this variety, the question of “how did all these galaxies ended up looking like they do?” must come up on everyone’s mind. It turns out that the current state of galaxies is a result of an evolution that has been going on over most of the age of the Universe. The key to understanding galaxies today is thus in understanding their history and the history of the Universe as whole, the cosmology.
In the most widely accepted cosmological model, the so-called ΛCDM (Λ Cold Dark Matter) model, the growth of large structures in the Universe is dominated by the gravitational effects of the dark matter. This hypothetical substance has been postulated in the first half of the 20th century to explain unexpectedly high velocities of movement of various objects –stars in galaxies, as well as galaxies in galaxy clusters. While there are still open questions, the ΛCDM has demonstrated a tremendous ability to reproduce observable features in the current Universe.
In the ΛCDM model, large galaxies are formed by merging of smaller galaxies, protogalaxies, and diffuse accretion of the surrounding matter. Thus, galaxy interactions play a crucial role in the life of every galaxy. Galaxy mergers ensure the mass growth, enhance or suppress star formation, possibly trigger active galactic nuclei (AGNs), and many more. Knowing the merger history of the observed galaxies is crucial to understanding their role in the evolution of the individual galaxies as well as of the whole universe.
Galaxy mergers are slow processes, happening over millions of years and so the information about the merger history of any individual galaxy must be indirectly derived from its present-day state. Luckily, a significant portion of elliptical and lenticular galaxies show a unique type of fine structure known as stellar shells, which are created in mergers and can be used to date the last significant merger that the galaxy has undergone.
Previously, we developed methods to extract such information from individual galaxies. The aim of this project is to expand the applicability of such methods to vastly larger samples. To this end, we are developing a set of self-contained tools to identify and analyze shell galaxies in images of large sky surveys, so that the estimates of merger times can be automatically obtained. We gradually apply those tools to existing data from current surveys and prepare for their application to the unprecedentedly large data set that will be produced by the Large Survey of Space and Time (LSST) at the Vera C. Rubin Observatory, which is becoming operational as the project is concluding.
Through this project, we transform shell galaxies from a position of curiosity to that of utility, allowing statistical applications using the merger data on the hundreds of shell galaxies that can be found in the existing data and later on the thousands of galaxies eventually observed by the LSST project – a huge qualitative leap from the handful of galaxies with known merger histories available before the project.
In the most widely accepted cosmological model, the so-called ΛCDM (Λ Cold Dark Matter) model, the growth of large structures in the Universe is dominated by the gravitational effects of the dark matter. This hypothetical substance has been postulated in the first half of the 20th century to explain unexpectedly high velocities of movement of various objects –stars in galaxies, as well as galaxies in galaxy clusters. While there are still open questions, the ΛCDM has demonstrated a tremendous ability to reproduce observable features in the current Universe.
In the ΛCDM model, large galaxies are formed by merging of smaller galaxies, protogalaxies, and diffuse accretion of the surrounding matter. Thus, galaxy interactions play a crucial role in the life of every galaxy. Galaxy mergers ensure the mass growth, enhance or suppress star formation, possibly trigger active galactic nuclei (AGNs), and many more. Knowing the merger history of the observed galaxies is crucial to understanding their role in the evolution of the individual galaxies as well as of the whole universe.
Galaxy mergers are slow processes, happening over millions of years and so the information about the merger history of any individual galaxy must be indirectly derived from its present-day state. Luckily, a significant portion of elliptical and lenticular galaxies show a unique type of fine structure known as stellar shells, which are created in mergers and can be used to date the last significant merger that the galaxy has undergone.
Previously, we developed methods to extract such information from individual galaxies. The aim of this project is to expand the applicability of such methods to vastly larger samples. To this end, we are developing a set of self-contained tools to identify and analyze shell galaxies in images of large sky surveys, so that the estimates of merger times can be automatically obtained. We gradually apply those tools to existing data from current surveys and prepare for their application to the unprecedentedly large data set that will be produced by the Large Survey of Space and Time (LSST) at the Vera C. Rubin Observatory, which is becoming operational as the project is concluding.
Through this project, we transform shell galaxies from a position of curiosity to that of utility, allowing statistical applications using the merger data on the hundreds of shell galaxies that can be found in the existing data and later on the thousands of galaxies eventually observed by the LSST project – a huge qualitative leap from the handful of galaxies with known merger histories available before the project.
The idea of using shell galaxies as a kind of mass-surveillance tool for observing the long-term development of galaxies is remarkably simple, but as it usually happens, its practical execution requires the solutions to many technical details. First, we need to identify shell galaxies and to measure the positions of shells in these galaxies. At this stage, the most efficient method is still visual inspection – to facilitate that, we have developed visualization tools that allow fast visual inspection of galaxies by generating composite images with multiple scales and residuals, facilitating the identification of shells simultaneously in the inner and outer parts of the galaxy.
Before the shell structures can be interpreted in any way, it is necessary to obtain quantitative information about the gravitational potential of the galaxy in which those shells are formed. This is obviously determined from the mass distribution in the galaxy, which in turn is derived from its light profile. We have developed methods to reliably measure the light profiles of galaxies from sky surveys, to convert this light into stellar mass and then to link this visible part with the full mass of the galaxy, including the invisible dark matter.
When the positions of the shells and the potential in which they were created are known, they can be interpreted in terms of the time elapsed since the beginning of the merger that created the shells. For this purpose, we have developed an algorithm for modelling shell evolution and integrated it into the tool chain. We validated the algorithm using shell galaxies produced in cosmological simulations to make sure that the implementation is realistic. Using such an algorithm is obviously orders of magnitudes faster than executing full self-consistent simulations of galactic mergers as it is thus the final link needed for mass analysis of samples of shell galaxies.
Before the shell structures can be interpreted in any way, it is necessary to obtain quantitative information about the gravitational potential of the galaxy in which those shells are formed. This is obviously determined from the mass distribution in the galaxy, which in turn is derived from its light profile. We have developed methods to reliably measure the light profiles of galaxies from sky surveys, to convert this light into stellar mass and then to link this visible part with the full mass of the galaxy, including the invisible dark matter.
When the positions of the shells and the potential in which they were created are known, they can be interpreted in terms of the time elapsed since the beginning of the merger that created the shells. For this purpose, we have developed an algorithm for modelling shell evolution and integrated it into the tool chain. We validated the algorithm using shell galaxies produced in cosmological simulations to make sure that the implementation is realistic. Using such an algorithm is obviously orders of magnitudes faster than executing full self-consistent simulations of galactic mergers as it is thus the final link needed for mass analysis of samples of shell galaxies.
The essential result of the project is the integrated chain of tools (tfimage for shell detection and measurement, photomass for the determination of gravitational potential of the host galaxy and shevoll for the modelling of shell evolution in this potential and comparison with the detected shell positions) which has been made publicly available as free and open-source software. Before the project, various groups of scientists were using various single-purpose tools, the application of which for even a single galaxy required a significant amount of input and manual data management. This integrated tool chain allows for fast and efficient workflow and forms the first step in the complete automation of the process which will be necessary for the processing of the overwhelming amounts of data soon to be generated by the LSST project. These tools, however, are not just software implementations, but also include novel formulae developed specifically to insure robust results when the tools are applied to the outputs of large-scale sky surveys.
Armed with our tools, we have also produced and made available catalogs of processed data. The catalog of photometry and stellar mass estimates of nearby galaxies enables full reproducibility and allows users to apply custom stellar-mass calibrations to Legacy Surveys images. Further we have gone through 40 thousand galaxies from the 2MASS Redshift Survey (2MRS) whose sky positions overlap with the Legacy Surveys footprint and selected the 345 brightest galaxies with the classification of their tidal shell structure. From these, we compiled a pilot sample of 11 shell-hosting prolate rotators – a special class of galaxies of particular interest from the point of view of stellar evolution – and applied the merger-dating procedure on them as the first example of a sample-wide application of the fully developed chain of methods. These are only the first examples of the potentially broad impact of the methods and tools developed within the project.
Armed with our tools, we have also produced and made available catalogs of processed data. The catalog of photometry and stellar mass estimates of nearby galaxies enables full reproducibility and allows users to apply custom stellar-mass calibrations to Legacy Surveys images. Further we have gone through 40 thousand galaxies from the 2MASS Redshift Survey (2MRS) whose sky positions overlap with the Legacy Surveys footprint and selected the 345 brightest galaxies with the classification of their tidal shell structure. From these, we compiled a pilot sample of 11 shell-hosting prolate rotators – a special class of galaxies of particular interest from the point of view of stellar evolution – and applied the merger-dating procedure on them as the first example of a sample-wide application of the fully developed chain of methods. These are only the first examples of the potentially broad impact of the methods and tools developed within the project.