Generating mock Euclid and Roman surveys of stellar halos of simulated nearby galaxies using the py-ananke pipeline

The py-ananke pipeline is a Python package designed to provide easy installation and distribution of the ananke software, as described in Sanderson et al. 2020. In this work, the team focused on cosmological simulations, such as the latte suite of FIRE simulations (Wetzel et al. 2016, Garrison-Kimmel et al. 2017 & Hopkins et al. 2018) which have limited resolution and cannot accurately represent fully resolved stellar populations with individual stars. To address this challenge, the authors of ananke developed a framework consisting of scripts and data that enabled the generation of synthetic GAIA star surveys (refer to Figure 1) from these simulated galaxies. The framework combines density estimations and IMF sampling techniques to create representative populations of mock stars.

An essential aspect of ananke is its integration with the EnLink/EnBiD C++ software (Sharma & Steinmetz 2006, Sharma & Johnston 2009) for computing phase space densities. These computed densities are then used as input for the Galaxia C++ software (Sharma et al. 2011), which generates synthetic surveys by incorporating user-supplied GAIA isochrones to produce the mock photometry.

The development of py-ananke aims to make this sophisticated framework accessible to a broader community. By providing a self-contained and easily installable Python package, we strive to facilitate the usage and adoption of ananke for generating mock star surveys from cosmological simulations, enabling the investigation of stellar halos around nearby galaxies.

As ananke represents a significant step towards establishing a standard of comparison between simulated and observed data, further refinements were necessary to promote wider adoption within the community. For instance, the backend C++ software required users to possess detailed knowledge in manually transferring files between pipeline components. Additionally, the included python scripts employed coding designs that necessitated manual rewriting of ananke's software to execute the pipeline successfully. Lastly, the framework for supplying isochrones tailored to other instruments, such as Euclid and Roman, was not yet prepared for this task.

To address these limitations and enhance the usability of py-ananke, we have undertaken extensive improvements and modifications. This includes streamlining the file management processes, enhancing the python scripts for seamless execution, and developing the capability to incorporate isochrones specific to Euclid and Roman instruments. These advancements contribute to establishing py-ananke as a versatile and user-friendly pipeline for generating mock surveys, thus promoting broader engagement.

To address the aforementioned limitations, the implementation of py-ananke was designed to provide solutions (refer to Figure 2). It introduces dedicated wrapper submodules, namely py-EnBiD-ananke and py-Galaxia-ananke, specifically developed to handle the installation and utilization of the C++ backend software, EnBiD, and a modified version of Galaxia called Galaxia-ananke. These submodules relieve users from the need to directly manage the C++ software while isolating the C++ wrapping process. This allows py-ananke to focus on processing inputs and outputs using pure Python. 

The implementation of py-ananke involves five classes, with only one - Ananke - being relevant to the end user.

• Universe objects store the particle data and various parameters provided to Ananke.
• Observer objects store the observing configuration, including the position in space.
• Densities objects utilize the particle data from the Universe class to compute and store phase space densities, employing py-EnBiD-ananke.
• Ananke objects serve as the user interface, connecting these three classes and the py-Galaxia-ananke classes to execute the pipeline.
• Additionally, while still in an experimental state, Extinction objects are utilized by Ananke to estimate and store extinctions in the outputs of py-Galaxia-ananke.

It is worth noting that Galaxia-ananke incorporates several pre-installed photometric systems, represented by sets of isochrones generated from the CMD web interface  (commonly referred to as Padova isochrones Marigo et al. 2017). Among the available options are HST, GAIA, Rubin, JWST, and, pertinent to this poster, Roman and Euclid.

These implementation enhancements in py-ananke successfully address the challenges associated with the original framework. By integrating the wrapper submodules, streamlining the execution process, and providing a diverse set of pre-installed photometric systems, py-ananke now offers a more user-friendly and efficient pipeline for generating mock surveys.

Py-ananke is compatible with Python versions above 3.7.12 and below 3.11. The project is publicly hosted on GitHub at https://github.com/athob/py-ananke, organized into three branches: main, stable, and develop. The main branch contains the latest released version, while the stable and develop branches host versions currently in development, with stable being the most recent stable version. To install py-ananke, you can use the following pip command, which pulls the latest version directly from the repository's main branch:

Alternatively, if you prefer, you may clone the repository to your local machine and then install py-ananke using the following pip command, which installs it from your local copy of the repository:

The repository includes a Jupyter notebook that demonstrates a simplified use case utilizing a dummy set of randomly generated particle data. You can access the notebook directly at https://github.com/athob/py-ananke/blob/main/jupyter/testing_ananke.ipynb. This notebook provides a step-by-step example to help you understand the functionality and usage of py-ananke in a straightforward manner.

The resulting output comprises various characteristics of the generated synthetic stars, directly derived from their parent particles, such as age and chemical abundances. Other properties are also based on those of the parent particles, but differ between individual synthetic stars. Those include physical properties such as luminosity, mass, and temperature, as well as phase-space properties like position and velocity. Additionally, the catalog includes GAIA photometry and the corresponding extinctions for further analysis and comparison.

Euclid and Roman will revolutionize our understanding of nearby galaxies by delivering resolved star maps for hundreds of galaxies within the local universe. These maps will offer a depth and clarity comparable to the current observations available for the Andromeda galaxy (M31). With their impressive angular resolution, Euclid and Roman will disentangle stellar blends throughout the nearby universe, specifically within a distance of less than 10 Mpc. However, to fully capitalize on the immense potential of these telescopes, it is crucial that we are equipped with robust observing plans and scalable analysis software capable of addressing the corresponding challenges.

To develop the necessary infrastructure for effectively utilizing Euclid and Roman, it is vital to rely on simulated observations of nearby galaxies as a critical design reference. Py-ananke plays a pivotal role in this regard, as it enables the processing of individual simulated galaxies at specific distances and orientations to generate mock star lists for a simulated Euclid or Roman field of view. For this poster, we have selected the m12f simulated latte galaxy from the FIRE2 suite of cosmological simulations presented by Garrison-Kimmel et al. 2017. By carefully placing and orienting the simulated data and observer, we have constructed a projected field of view that emulates the Southern Pinwheel galaxy (M83, see Figure 3). This choice represents a typical example of a nearby face-on galaxy that Euclid and Roman will have the ability to map at the level of individual stars.

Figure 4.1 and 4.2 showcase the mock catalogs produced by py-ananke, simulating the M83 galaxy as viewed through the Euclid VIS & NISP and Roman WFI facilities, respectively. The simulated color-magnitude diagrams focus on various regions within the field of view, each highlighted by the footprint of the corresponding camera used by the facility. This remarkable capability of py-ananke to generate accurate photometric and astrometric catalogs of synthetic stars derived from cosmological simulations positions it at a crucial juncture for facilitating more direct comparisons between simulations and observations.

The availability of such calibrated tools is instrumental for the scientific community to effectively prepare for the upcoming Euclid/Roman era. These tools enable investigations in key science areas that will be addressed by these facilities, including the determination of dark matter halo masses, census and properties of dwarf galaxies, characterization of stellar streams, galactic archaeology, refinement of stellar evolution models, investigation of star formation histories, and exploration of merger histories.

Py-ananke plays a crucial role in the success of the RINGS (Roman Infrared Nearby Galaxies Survey, PI: B. Williams) project, an approved Roman Large Wide Field Science Program in response to the NASA ROSES solicitation "D.14 Nancy Grace Roman Space Telescope Research and Support Participation Opportunities" (number NNH22ZDA001N-ROMAN) aimed at developing advanced software tools for simulating and analyzing Roman surveys of nearby galaxies.

RINGS is committed to maximizing the scientific output of the Roman mission and optimizing the utilization of its limited observing time. By providing the necessary tools to the community, RINGS aims to unlock the full potential of Roman's capabilities and revolutionize our understanding of nearby galaxies. Building upon the achievements of the WINGS project, RINGS focuses on harnessing the remarkable capabilities of the Roman telescope, such as its wide field coverage, exceptional sensitivity to faint stars and compact sources, and near-infrared bandpasses. The project aims to empower the scientific community with powerful software tools that enable the generation of simulated Roman imaging data based on numerical simulations. By simulating Roman observations, researchers can validate and refine model predictions pertaining to star formation, galaxy structure, and the distribution of dark matter. Additionally, these tools will facilitate the optimization of observing strategies, allowing scientists to efficiently plan Roman observations of resolved stellar populations in crowded and low surface brightness regions.

Py-ananke has recently entered its beta phase, and we are diligently working towards its submission to the Journal of Open Source Software. We have an exciting roadmap of upcoming updates planned, which we aim to implement prior to or following the submission. Here are some of the planned updates in no particular order:

• Improving Extinction: The extinction feature is currently in an experimental state, and we have identified areas for significant improvement. Firstly, while the user can supply their own extinction coefficients Aλ/A0 for any photometric system, only GAIA currently has default coefficients. Future updates will expand the range of default extinction coefficients for different systems. Secondly, the estimation of dust column density maps per particle currently requires user input. Our plan is to incorporate a treatment that directly computes dust column densities from the simulated metal-enriched gas provided by the user.
• Implementing Error Modelling: The original ananke framework (Sanderson et al. 2020) featured error modelling as a significant component. In future updates, we will introduce a framework that allows for the incorporation of simple error models into the pipeline, enhancing the robustness of the generated mock surveys.
• Interfacing with Isochrone Databases: Py-ananke currently includes pre-loaded isochrones for a chosen photometric system (some of which are listed in the introduction section). Our plan is to implement a direct interface with established isochrone databases such as Padova or MIST, enabling users to download available photometric systems on-the-fly. Additionally, we aim to develop a framework that allows py-ananke to output photometry in a range of commonly used calibration standards.
• Additional Modularization: While EnBiD serves as the density estimation routine of choice, we plan to expand the options by adding more choices such as EnLink. Furthermore, we intend to diversify the selection of kernel functions for density estimation and sampling mock stars in phase space, making it possible to utilize anisotropic kernel functions. Additionally, we will enhance the flexibility of py-ananke by incorporating a wider range of initial mass functions (IMFs) and allowing mass sampling based on present mass functions, particularly for generating mock stars in globular clusters.
• Quality of Life Updates: We are dedicated to enhancing the user experience and overall usability of py-ananke. To that end, we will be implementing various quality of life updates, refining the software interface, improving documentation, and streamlining the overall workflow.

These upcoming updates signify our commitment to continuously improve py-ananke and address the evolving needs of the community. We encourage users to stay engaged with the project, provide feedback, and contribute to its development as we work towards a more comprehensive and user-friendly tool for generating mock surveys.

The entire py-ananke software code is released under the open-source GPL3.0 licence, ensuring its accessibility and transparency. You can readily access the code in the main py-ananke GitHub repository, as well as its submodules repositories. We encourage users to utilize the Github issues feature to report any bugs encountered or suggest new ideas for improvement. Contributions to the project are highly valued and greatly appreciated. To contribute, we kindly request that you make your changes in a separate branch or fork of the repository. Once your contributions are ready, you can submit a pull request to merge them into the develop branch.

By leveraging the capabilities of py-ananke and the forthcoming Euclid and Roman telescopes, we anticipate groundbreaking advancements in our understanding of nearby galaxies and their stellar halos. The unprecedented resolution and depth provided by these instruments will allow us to unravel the intricate structures and dynamics within stellar halos, shedding light on their formation and evolution processes. The combination of accurate photometric and astrometric catalogs generated by py-ananke with the observational data from Euclid and Roman will pave the way for comprehensive analyses, enabling detailed studies of dark matter substructure, tidal interactions, and the origin of streams and overdensities. Furthermore, these investigations will contribute to our knowledge of galactic assembly histories, the interplay between dwarf galaxies and their host systems, and the broader field of galactic archaeology. Ultimately, this synergy between advanced simulations, state-of-the-art telescopes, and robust analysis tools will open new avenues for exploring the edge of galaxies and unraveling the complex dynamics and formation processes within stellar halos.

We extend our sincere gratitude to Robyn Sanderson (UPenn), Andrew Eden (FloridaTech), Nondh Panithanpaisal (UPenn/CarnegieObs), and Nicolas Garavito-Camargo (FlatironCCA) for their invaluable contributions and support during the development of this package. Their expertise, guidance, and collaboration have been instrumental in shaping the vision and advancement of this project. We also appreciate the valuable feedback and suggestions provided by the wider community, including the extended Galaxy Dynamics @ UPenn group and the participants of the "anankethon" workshop, which have significantly contributed to the refinement and enhancement of the package.

Figure credits:

• Figure 1 by Sanderson et al. 2020
  Acknowledgment: R. Sanderson (University of Pennsylvania)
• Figure 3 by CTIO/NOIRLab/DOE/NSF/AURA
  Acknowledgment: M. Soraisam (University of Illinois)
  Image processing: Travis Rector (University of Alaska Anchorage), Mahdi Zamani & Davide de Martin
• Figure 5 upper by ESA, CC BY-SA IGO 3.0, CC BY-SA 3.0 igo
• Figure 5 lower by NASA (WFIRST Project and Dominic Benford) - Adapted from https://www.nasa.gov/press-release/nasa-to-make-announcement-about-wfirst-space-telescope-mission

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