High Energy Astrophysics of Galaxy Clusters

Mentors: Tim Paglione (CUNY/AMNH) and the AMNH Gamma-Rays and Compact Objects Group

Gamma-rays probe the most energetic processes in the universe. Most gamma-rays are created by light and matter interacting with cosmic rays, particles accelerated to nearly the speed of light usually by supernova explosions and the pulsars they leave behind. However, any strong shocks can accelerate cosmic rays, ranging from those found between interacting wind binaries to the very large-scale shocks in galaxy clusters launched by starburst super winds or jets from active galactic nuclei. Our group uses 15 years of data from the Fermi Gamma-Ray Space Telescope to stack signals from any and all potential sources of gamma-rays including pulsars, novae, hot stars, interstellar clouds, and a variety of interacting binaries and other exotica (even Jupiter!). Our current study targets galaxy clusters by cross-matching potential targets with the Planck Telescope’s catalog of those detected by the Sunyaev-Zeldovich Effect, which can help us quantify the energy budgets of galaxy clusters, the mass loading of winds and jets, and the impact of cosmic rays on cluster evolution and feedback in general.

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Mapping Motions in the Nearest Galaxies

Mentors: Julianne Dalcanton (CCA, University of Washington), Eric Koch (CfA; Harvard), David Hogg (CCA, NYU)

The project includes two possible phases, which can be done in any order. In Option A, the thesis student will apply standard software that models the motions of the galaxy as a series of rotating, circular rings that are allowed to “tilt” with respect to the observer. These “tilted ring models” are good first tools for characterizing the large scale motions of the gas, and can be used to derive “rotation curves” (i.e., how fast the galaxy is rotating as a function of radius), which have long been used to characterize the underlying dark matter distribution. Rotation curves have been calculated previously for these galaxies (see this detailed paper on NGC 6822, for example), but the high velocity resolution of the new data will better show departures from the mean motion, which can help diagnose internal gas flows and possible departures from equilibrium. This first phase can result in a paper that describes the new rotation curve measurements, shows the departures from simple models, and potentially fits the dark matter potential. Some overview slides of tilted ring modeling can be found here and here, and a slide deck with an overview of rotation curves derived from tilted ring modeling can be found here.

In Option B, the thesis student will develop new fitting tools that move beyond the tilted ring model, and that are better suited to the very complex motions that the new LGLBS data have revealed. Working with David Hogg (a world expert in fitting models to complex data), and Julianne Dalcanton & Eric Koch, the student will adapt and expand upon the work in Braun 1991 to model the galaxy motions as connected segments of cylinders, rather than axisymmetric rings. This will allow far more flexibility in the model, allowing it to handle the complexity revealed in the new high-resolution model. This tool is desperately needed to characterize the small-scale motions in the gas, which can then be connected with our knowledge of the evolving stars. This part of the project will involve learning about modern fitting tools and developing software skills. There are multiple paper possibilities including: (1) a paper describing the new tool; and (2) a paper describing the resulting fits to the LGLBS data. The resulting fits will also make it possible to measure the velocity dispersion of the gas, which would lead the student to be a co-author on subsequent analysis papers.

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Astrophysics Simulations

Mentors: Mordecai-Mark Mac Low (AMNH), Eric Anderssen (AMNH), Brooke Polak (Max Planck-IMPRS, AMNH), Sabrina Appel (Rutgers, AMNH), Linn Eriksson (Stony Brook, AMNH)

My group will be able to pitch three projects. We can provide more detail as is useful. First, with Eric Anderssen, a project in the formation of ultra-faint dwarf galaxies using star-by-star simulations of their lifetimes to reveal the scatter of stellar abundances. Second, with Brooke Polak and Sabrina Appel, a project on star cluster formation using the Torch framework combining N-body with adaptive-mesh magnetohydrodynamics to model the initial conditions for star cluster evolution. Finally, with Linn Eriksson, a project on the interactions of dust and gas in protoplanetary disks leading to planetesimal formation using single-grid and adaptive-mesh codes.

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Simulating Black-Hole Powered Transients

Mentor: Ore Gottlieb (CCA)

I am a theoretical and computational high-energy astrophysicist. I develop innovative approaches that integrate advanced large-scale numerical simulations and analytic techniques to uniquely connect the underlying physics to observations, through which I investigate one of the Universe’s most captivating objects – black holes. As gas falls in, black holes launch collimated outflows (jets) that reshape their environment and power multi-messenger signals: electromagnetic and gravitational waves, neutrinos, and cosmic rays. Each messenger supplies complementary information about the source, offering exceptional opportunities to constrain the mysterious physics of high-energy transient black hole-driven phenomena in a variety of astrophysical systems, including gamma-ray bursts, compact object mergers, supernovae, fast blue optical transients, and active galactic nuclei. In my research, I connect the physics at the heart of these astrophysical laboratories with observed multi-messenger signals of gravitational waves, electromagnetic and particle emission.

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Modeling the AGN Channel for Gravitational Wave Sources

Mentors: K. Saavik Ford (CUNY/AMNH/CCA) & Barry McKernan (CUNY/AMNH/CCA) and the TIDYNYC group​

There are multiple astrophysical channels for producing the binary black hole (BBH) mergers observed in gravitational waves (GWs) by the LIGO-Virgo-KAGRA (LVK) collaboration; the active galactic nucleus (AGN) channel (see e.g. here & here) suggests that many of the mergers could come from the interactions between a nuclear star cluster (NSC) and an AGN disk. Our group is producing McFACTS, the first publicly available, comprehensive, population synthesis code that models the interactions between NSCs and AGN disks, to provide detailed predictions of the distribution of parameters that may be observed by LVK for various AGN and NSC models, as well as predictions for future GW observatories such as LISA, DECIGO and others. We also would like to implement predictions for electromagnetic observables, a unique feature of the AGN channel, and consider how current and upcoming observations constrain our models of AGN and NSCs.

Because there are a wealth of physical processes involved in the interactions between NSCs and AGN, and some of those processes remain substantially uncertain, McFACTS handles the uncertainties by implementing many optional modules to simulate different physical assumptions. The ‘bare bones’ version will be released publicly by the end of September 2024, but many effects (that could be important!) will not be included in the first release. We are seeking an MS student to work on implementing at least one new or variant physical effect, as an update to the code for later release. In conjunction with this, the student will be able to use McFACTS simulate observables and determine the impact of the newly added physics by comparing with output of prior versions; in this way we hope to learn more about AGN through their impact on GW observed BBH mergers.

There are multiple possible options for new modules, but all of them involve learning some new physics, coding skills for working on a large public code (both writing and maintenance), and learning to write at least one paper on their new module and results. Depending on choice of project (driven by student preferences) and project outcomes, the student may be able to work on more than one module over the course of their MS career. The student will also be learning to work with other members of the development team, especially their cohort-mate, CUNY MS student Shawn Ray, who started with us at the beginning of the summer, and will be expected to help onboard new team members who may join later this year (undergraduates) or next (future MS students).

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Empowering Collaborative Data Management for Astronomers

Mentor: Kelle Cruz (CUNY/AMNH/Flatiron)

Astronomy is a data-intensive field, with astronomers collecting vast amounts of data from telescopes and other instruments. This data is used to study a wide range of astronomical phenomena, from the formation of stars and galaxies to the evolution of the universe. Many astronomers use compilations of very wide tables to keep track of their data instead of using databases. Databases are organized collections of data that can be easily searched and queried but they can be difficult for a typical astronomer to build. The goal of this project is to develop new database infrastructure, dubbed the AstroDB Toolkit, to make it easier for astronomers to build, share, and collaboratively maintain databases of astronomical data. This project involves: designing and implementing new database schemas, developing tools and techniques for ingesting data, working with astronomers to identify their needs and requirements for databases. This project is ideal for a student with a strong interest in software development and data management and has experience using Github. Prior experience with databases is not necessary.

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Machine Learning Galaxy Formation 

Mentor: Ariyeh Maller (CUNY/CCA)

How galaxies form and grow is a difficult question in astrophysics.  We often turn to running large volume cosmological simulations to model galaxy formation. But when we do we often still don’t understand why galaxies end up the way that they do, despite knowing all of the input physics.  

In the first project, we will attempt to use machine learning to uncover the physics that determine various galaxy properties. Machine learning is an extremely powerful tool for uncovering relationships in data, but is often difficult to interpret. We will use a combination of techniques, including feature ranking and symbolic regression,  to try and uncover simple connections between galaxy properties.

In the second project, we will attempt to determine the likelihood of a galaxy formation model, comparing it to all known constraints.  This won’t actually be possible in the 2 year time frame, but we can make a start of it by aiming for a large number of constraints at redshift 0. This project will focus on statistical inference and understanding observational data.

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Exoplanet Watch: Building a Community Collaboration to Characterize Exoplanet Transits

Mentor: Joshua Tan (CUNY/AMNH), Rob Zellem (NASA), and Kyle Pearson (NASA)

Since the observation of the transit of HD 209458 b in 1999, thousands of transiting exoplanets have been discovered with thousands more candidates yet to be confirmed. In many cases, the stars that these exoplanets are orbiting are bright enough to be observed efficiently with very small telescopes. With consistent year-over-year improvements in off-the-shelf imaging equipment, we find ourselves in a unique period of history where it is possible for almost anyone in the world to detect a transiting planet around another star.

Student observers at CUNY have been participating in programs to follow-up on transit detections since 2017, mostly in collaboration with a private observatory in Colorado. In the meantime, Dr. Rob Zellem and Dr. Kyle Pearson launched Exoplanet Watch to encourage people of all levels and abilities to participate in humanity’s ongoing discovery of planets around other stars. As part of this project, the team has developed a versatile, open-source code named EXOTIC (EXOplanet Transit Interpretation Code) which is intended to be a user-friendly data reduction pipeline aimed to produce high-quality transit light curves from any kind of data source. Since then, Exoplanet Watch has partnered closely with the American Association of Variable Star Observers (AAVSO), Microobservatory, and the TESS Follow-up Observing Program (TFOP).

There are a wide range of projects for the enterprising Master’s student. Aside from aiding and organizing remote observing campaigns, we hope to add new software features to EXOTIC to improve auxiliary science (stellar variability remains a feature in the aggregate data that is not yet part of the pipeline), promote community organizing to help improve Exoplanet Watch’s data collection and outreach, and improve EXOTIC’s user interface and documentation. Having access to Exoplanet Watch’s firehose of data ensures plenty of opportunities to publish unique scientific results either as a lead author or a co-author. Self-motivated students who are excited about transit science and community collaboration are encouraged to discuss possible project plans and consider joining this dynamic team.

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Sunspot Predictions Using Time Series and Dynamical Systems

Mentor: Milena Cuellar (CUNY)

In May 2024 a G5 geomagnetic storm on the Sun was dramatic enough to allow aurora to be visible from New York City (if you could find dark enough skies). While the basic idea of the solar dynamo and the 11-year timeframe for the changing of the Sun’s magnetic field gives a simple model for the phenomenon, detailed predictions have eluded theorists since consistent records of the solar cycle were first kept starting in 1750. The student will explore new mathematical techniques in chaos for making these kinds of predictions along with AI as a literature review tool in nonlinear dynamics.