Strong lensing in the survey era (clusters, galaxies, microlensing)
Mentor: Giorgos Vernardos (CUNY/AMNH)
Gravitational lensing is a rare phenomenon that even Einstein did not think would be possible to observe. Yet, with our current advanced observatories, both in space and on the ground, we are able to obtain pristine and impressive images of such lenses. These new discoveries that are happening almost on a daily basis now, will increase the sample of approx. 1000 known lenses by about 100 times. This ‘zoo’ of lenses, apart from including very rare systems, for example quasars lensing other quasars, lensed supernovae, etc, will constitute a treasure trove of information regarding cosmology, dark matter, and galaxy evolution in general.
Cluster lenses in the Strong Lensing Database (SLED)
The Strong Lensing Database (SLED) is an invaluable resource that catalogues and enriches the information of all these lenses – it has been called the ‘facebook’ of lenses. It is hosted at AMNH and it is continuously being extended and improved. The current resource (https:/sled.amnh.org) supports galaxy-scale lenses. The project will consist of extending SLED to incorporate lens clusters and visualization tools, with particular emphasis on discoveries by Euclid.
Detecting dark matter with spectra
We will explore the concept of at least part of dark matter being in the form of planet-mass compact objects, e.g. primordial black holes, free-floating planets, etc. There are various ways to measure these but lensing is very promising because it can measure their abundance and mass at the same time. Alongside large optical surveys like Euclid and LSST, spectroscopic observations can unveil additional lensing “events” that would otherwise be missed. Based on data from the 4MOST spectroscopic survey, we will identify lensing events among millions of quasars and use them to measure the properties of compact dark matter.
Visualization and sonification
Images of gravitational lenses as well as simulations and other observables (e.g. time delays) can produce captivating and mesmerizing images and effects. This is barely explored in terms of communicating with the public and promoting the scientific discoveries behind lensing. This project will make use of GPUs, sound, and state-of-art lensing data and simulations to create online mobile apps and even hardware devices that can bring the lensing phenomena to the public in a tangible way.
The Rogue Worlds Citizen Science Project
Mentors: Jackie Faherty (AMNH, CUNY) and the Brown Dwarfs in New York City (BDNYC) Research Team
The Rogue Worlds Project is a newly funded NASA citizen-science initiative that will harness data from NASA’s SPHEREx all-sky spectroscopic mission. The project will activate volunteer researchers from around the world to search for young, free-floating planetary-mass objects in the solar neighborhood. These “rogue worlds”are objects only a few times the mass of Jupiter that travel through space without orbiting a sta. They are important laboratories for understanding how both stars and planets form.
Working through the Zooniverse platform, citizen scientists will examine specially constructed SPHEREx color and difference-image mosaics of nearby young stellar associations, identifying sources with the distinctive infrared signatures of cold brown dwarfs and super-Jupiters. Each selected source will trigger the extraction of its 0.7–5.0 μm SPHEREx spectrum. Machine-learning tools will then classify the candidates, with volunteers helping to evaluate and refine those classifications.
We are looking for an excited CUNY master’s students that can contribute directly to the project’s development over the next two years. Potential work includes constructing astronomical mosaics, building and testing the Zooniverse portal, extracting and analyzing SPHEREx spectra, developing machine-learning classification tools, and identifying high-priority targets for follow-up observations. The project combines large-scale survey astronomy, planetary and brown-dwarf science, software development, data visualization, machine learning, and public participation in the scientific discovery process.
Hydrodynamics of Transonic Astrophysical Objects
Mentor: Logan Prust (CCA)
Transonic gravitating bodies are found in many astrophysical contexts. While objects moving at subsonic or highly-supersonic speeds are generally well-understood, many models break down near the sound barrier. One such case is that of black holes moving through the interstellar medium, which do not have a rigid surface and readily accrete mass. However, the rate of accretion in the transonic regime is not adequately described by existing models. The drag force is also in need of further study due to the low-entropy accretion stream from behind the black hole, which may provide a thrust. On the other hand, some objects have rigid surfaces which collide with the gas, leading to a variety of shock waves as well as aerodynamic drag. In particular, a planet engulfed by its host star is expected to experience two distinct shock patterns depending on its depth within the star. In both cases, there are tangible quantities to be determined such as the drag force, accretion rate, shock structure, and dynamical friction. These can be probed via hydrodynamical “wind-tunnel” simulations. The numerical frameworks to simulate planetary engulfment (Prust & Bildsten 2024) and black holes (Prust et al. 2024) are already in place, so either of these projects would be ideal for a student interested in getting into the fields of astrophysical fluids or numerical modeling.
Reducing the Interfering Signals of Stars for Exoplanet Searches
Mentor: Ruth Angus (AMNH, CCA)
The search for a second Earth hinges on our ability to reduce the interfering signals of stars, that mask the presence of planets. In this project, we’ll work to model and mitigate that interference, bringing us ever closer to finding another planet just like our own.
Exploring the Universe with Spectroscopy
Mentor: Allyson Sheffield (CUNY/CCA)
Project 1: Galactic Archaeology: Origins of Stellar Streams
We will use spectroscopy from the SDSS-V Milky Way Mapper (MWM), along with Gaia data, to investigate the origins of stellar streams in the Milky Way halo. One possible project would study recently discovered streams with no known progenitor, using their motions and chemical compositions to determine whether they came from disrupted globular clusters or dwarf galaxies, and whether apparently separate streams might share a common origin. Current M.S. student Alejandra Rodriguez is using similar spectroscopic techniques to identify and characterize stars that have been stripped from Milky Way globular clusters.
Project 2: Searching for Technosignatures in Exoplanet Atmospheres
Here, we will use real and simulated JWST spectra to search for technosignatures (evidence of alien technology, such as industrial pollutants) in the atmospheres of exoplanets. Building on work with former M.S. student Carly Brown, this project would compare observations with simulated planetary atmospheres and explore which types of planets and observing conditions offer the best chance of detecting these signatures.
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.
Modeling star formation histories of the Local Group dwarfs with Semi-Analytic Modeling
Mentor: Mia Bovill (NASA HQ)
As we enter the golden age of surveys, we will have unprecedented data on nearby galaxies and their dwarf satellites. Encoded in the low mass satellite dwarf galaxies orbiting the Milky Way and Andromeda are not only the star formation histories of the individual dwarf galaxies, but also information about the merger histories of the two more massive galaxies. However, with only two systems for which we have detailed star formation histories for dwarf galaxies observations alone cannot separate out what is normal statistical variation and what is a signal of the mass or evolution of the Milky Way. In this project you will use the semi-analytic model Galacticus to build statistical samples of dwarf satellite systems around Milky Way and Andromeda analogs to answer two questions. First, are the differences we see in the star formation histories of the Milky Way and Andromeda dwarfs a signal of their different evolution or within the scatter? Second, what can the dwarf galaxies of our Local Group tell us about the minimum mass for star formation in the lowest mass galaxies ever formed.
Triaxial dark matter halos as cosmological probes.
Mentor: Ana Maria Delgado (CUNY)
What can the shapes of invisible dark matter structures tell us about the Universe? The structure of our Universe is dominated by dark matter. It forms the gravitational framework in which galaxies grow. Galaxy clusters are the largest gravitationally bound objects in the Universe, residing in enormous halos of dark matter. The literature has established that these halos are not usually spherical, but are instead stretched, flattened and otherwise triaxial, reflecting the history of the structure formation around them. Because cosmic structure grows differently in different cosmological models, simulations suggest that the shapes of cluster-sized halos may retain information about fundamental cosmological parameters. In this project, we will use state-of-the-art cosmological simulations and machine learning to test how well cosmology can be inferred from the triaxial shapes of cluster-sized dark matter halos. This work will investigate how the shapes of the Universe’s largest structures can advance our tests of cosmic evolution.
Stacking Gamma Rays To Discover New Source Populations
Mentors: Joshua Tan (CUNY/AMNH), Tim Paglione (CUNY/AMNH), Dave Zurek (AMNH) and the AMNH Gamma-Rays And Compact Emitters (GRACE) Group. Presenter: Owen Henry (CUNY Physics PhD Program)
The origin of cosmic rays (CRs) is a long-standing problem that astronomers have pondered for decades. Most attempts to answer questions about CRs rely on observing and interpreting astrophysical gamma-rays. Our group uses 18 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, dwarf galaxies, blazars, galaxy clusters, and a variety of interacting binaries and other exotica (even Jupiter!). Current ongoing projects of the group include CUNY Astro PhD student Owen Henry studying the signal from Galactic novae and CCNY Physics MS student Esha Singh studying dwarf galaxies. Projects are available for students interested in stacking astrophysical phenomena that could plausibly produce gamma rays, with a keen eye toward potential sources that have yet to be detected. In particular, projects on hot stars and AGN are waiting in the wings. Let’s see if your favorite astronomical object is sending gamma rays our way!
Cross the Misty Mountains through Moria: New frontiers of the cosmic web
Mentor: Charlotte Welker (CUNY/CCA)
On scales much larger than stars, solar systems and even galaxies, the Universe is shaped in a highly structured network resembling a spider web: the cosmic web. This network of filaments serve as highways for dark matter, cosmic gas and galaxies and constrain their properties, such as their spin, shape or ability to form stars. However, the diversity of cosmic filaments and their ability to evolve over time remain a mostly uncharted territory.
In the Gotham Web Lab, we produce and use a variety of datasets ( high-resolution cosmological simulations, telescope observations ) and techniques (hydrodynamic modeling, HPC computing, AI) to better understand the diversity of cosmic filaments, develop models of their evolution, understand how they shape populations of galaxies….and how galaxies shape them back, for instance through feedback associated with central black hole activity.
In our group, you will be able to choose between a variety of projects including for instance modeling the merger of two large filaments connecting to a cluster of galaxies and hunting for signatures in observations of galaxies, using Graph Neural Networks to reconstruct the evolutionary stage of filament from the population of galaxies within and around it, understanding how the faintest, most elusive filaments affect the tiniest galaxies (dwarf galaxies) detected by millions in new surveys like EUCLID and LSST, exploring the impact of AGN feedback in galaxies hosted by a cosmic filament on its long-term evolution or building a high resolution simulation of a merger of two faint filaments hosting dwarf galaxies.
You will join a highly collaborative team including a postdoc, two PhD students and a number of undergraduate students. You will also be given the opportunity to contribute to cross-disciplinary engineering/astronomy projects we plan to offer to undergraduate at City Tech in the coming two years, including among others building a mini radio-telescope and building a mechanical simulation of a galaxy merger.
Exoplanet Projects
Mentors: Genaro Suarez (AMNH) and the Brown Dwarfs in New York City (BDNYC) Research Team
Project 1: Understanding the Conditions that Allow Sand Clouds to Form in Exoplanet Atmospheres
Exoplanets exhibit fascinating and complex atmospheres where a variety of clouds form under different physical and chemical conditions. Among the most common are silicate clouds, which are essentially made of sand grains. These clouds can dramatically shape how exoplanet atmospheres look and behave, yet we still do not fully understand the conditions that determine when they form or disappear. In this project, the student will investigate this question by deriving fundamental physical properties, including temperature, radius, luminosity, surface gravity, mass, and age, for a sample of exoplanet analogs and exploring how these properties relate to the presence or absence of sand clouds. The project will combine observations from multiple telescopes, including the James Webb Space Telescope, with models that predict how these objects should look and evolve over time. The student will also use dedicated software developed by our research group and have the opportunity to contribute to improving tools for exoplanet data analysis. The student will join the dynamic Brown Dwarfs in New York City (BDNYC) research group, a world-leading team in the study and characterization of brown dwarfs and exoplanet atmospheres.
Project 2: Searching for New Exoplanet Analogs with Archival JWST Images
Brown dwarfs are fascinating objects that bridge the gap between the lowest-mass stars and giant planets. They are believed to form like stars, but their atmospheres resemble those of giant planets, making them ideal laboratories for understanding both stars and planets. Our Galaxy could host as many brown dwarfs as stars, but because brown dwarfs are extremely faint and emit most of their radiation in the infrared, many remain undiscovered. In this project, the student will use archival images obtained with the James Webb Space Telescope (JWST) to search for new brown dwarfs by analyzing the colors and brightnesses of hundreds or thousands of objects. The resulting sample will provide the basis for future observing proposals, giving the student the opportunity to lead follow-up observations using multiple telescopes. The student will join the dynamic Brown Dwarfs in New York City (BDNYC) research group, a world-leading team in the study and characterization of brown dwarfs and exoplanet atmospheres.
Modeling the AGN Channel for Gravitational Wave Sources
Mentors: Saavik Ford & Barry McKernan (CUNY/AMNH/CCA)
Active galactic nuclei (AGN) are powered by the accretion of disks of gas onto supermassive black holes in the centers of galaxies. AGN disks are expected to contain a dense population of embedded objects, including stars and stellar mass black holes. This embedded population is a promising source of binary black hole mergers, detectable in gravitational waves with LIGO-Virgo-Kagra (the so-called AGN channel). AGN should be the most efficient channel to merge binary black holes to intermediate black hole (IMBH>100Msun) mass.
The recent public, open-source code McFACTS is the only complete AGN channel simulation code; with development led by Profs Ford & McKernan assisted by CUNY MS & PhD students. We are interested in working with a new MS student to develop modules within McFACTS. Options include:
1. Details of binaries (start with initial fraction, improve eccentricity/dynamics models)
2. Extreme mass Inspiral Events (do properly) & link to QPEs/TDEs
3. Overall meta-study of Disk properties vs hierarchical merger ratio
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