In recent years, millimeter wavelength observations of protoplanetary disks around young stars have revealed substructures. These structures include asymmetries, gaps, and rings that could be indicative of phenomena creating gas pressure maxima which trap large dust grains, aiding their growth and promoting planet formation. The congregation and growth of large dust grains is thought to be a critical step in the grain growth that allows planets to form in the relatively short time frame before the circumstellar disk dissipates. We present new, high-resolution Sub-Millimeter Array observations (at a wavelength of 1.3 mm) of the HD 34700 system. The multiple Herbig Ae system HD 34700 is comprised of a close central binary and a distant companion, and has a bright disk around its central binary inferred through its strong infrared and millimeter excess. Recent scattered light observations showed that the smaller dust grains form a spiral arm structure with a large central cavity and an azimuthal discontinuity. Our SMA observations show an azimuthal asymmetry in the dust continuum which is indicative of a dust trap: a strong concentration of larger dust grains toward a likely pressure maximum in the gas. The trap is located at approximately 167 au from the central binary and with an azimuthal extent of 24 degrees. This is confirmed by our detection of CO gas centered on the binary location and consistent with a standard Keplerian disk. The large dust asymmetry could be produced by a planet producing a vortex at the cavity’s edge, or by the dynamical interactions of the central binary. Finally, we also detect a previously-unknown small dust disk around the distant companion HD 34700B, with a radius of approximately 43 au.
Gravitational microlensing is a powerful tool to study invisible objects, such as black holes, in the Milky Way. By monitoring highly populated areas like the Galactic bulge region, one can observe a variety of microlensing events due to brown dwarfs, main-sequence stars, white dwarfs, neutron stars, and black holes. We model the microlensing event rates with source stars in the Galactic bulge region using standard spatial and velocity distributions of stars in the Galactic bulge and disk regions. We observe that if black holes have an extended Salpeter-like mass function (as indicated by the recent LIGO binary-black hole gravitational wave events) and a similar velocity and spatial structure to stars, the population leads to a distinct increase in the microlensing event rate with Einstein crossing time on the order of 100 days. By looking toward the Galactic bulge region and observing on the order of 108 stars, we could potentially observe this excess of microlensing events. The Large Synoptic Survey Telescope (LSST) holds the potential to make these observations, though the success of observing microlensing events depends on the cadence of the telescope. We evaluate the efficacy of potential LSST cadences as either a trigger or a measurer of the light curves of black hole microlensing.
The Theory of General Relativity (GR) is very well-tested on local Solar System scales, but tests on the largest cosmological scales have been limited by the volume and precision of existing galaxy surveys. This situation is expected to change in the coming decade with the advent of several new spectroscopic redshifts surveys like DESI and Euclid. In this project, we aim to test the nature of gravity on these scales by using cosmological simulations to construct mock galaxy catalogs that mimic surveys as closely as possible. In particular, we focus on ΛCDM and three variants of the f(R) model of modified gravity: F6, F5, F4, each of which enhance the strength of gravity relative to GR with increasing intensity. Because of the inherent nonlinearity of the f(R) model, we use large-scale numerical simulations which self-consistently evolve dark matter particles according to these modified equations of motion. Previous simulations have predicted a higher abundance of massive halos and stronger clustering in the f(R) model relative to GR; however, it is unclear as to how much these differences persist in the galaxy distribution. We transform each of the halo catalogs using the Halo Occupation Distribution model, which determines the likelihood of a halo having a certain number of galaxies based upon its mass. Automating this process allows us to compare the differences in the redshift-space clustering between f(R) and GR using galaxies as tracers. Finally, we trim these galaxy catalogues even further by applying survey realism, ensuring that the galaxy distribution in the two cosmologies is identical to the observer.
With the discovery of over 4000 exoplanets, we are now able to conduct detailed studies of planet demographics. Interestingly, a possible dichotomy has developed, where hot Jupiters on longer orbital periods (between 5 and 15 days) tend to be more massive on average compared to shorter period systems. It is not clear whether this trend is produced by a detection bias or whether it results from some aspect of the planet's formation and evolutionary history. NASA's Transiting Exoplanet Survey Satellite (TESS) provides an opportunity to probe this question by increasing the known population of long-period hot Jupiters. In this work, we focus on the confirmation and characterization of TESS Objects of Interest, TOI-558b and 559b, two giant planet candidates within this period range. We globally modeled the photometric data from TESS along with precise radial velocity measurements, and find that both planets are quite massive (3-6 MJ) and have highly eccentric (0.1-0.3), long period orbits (7 and 14.6 days). Finally, we include the two systems in an analysis of all known giant planets with orbital periods less than 15 days, with a particular focus on their mass-period distribution.
The Harvard-Smithsonian Center for Astrophysics manages the comprehensive paper database Astrophysics Data System (ADS), which hosts nearly 15 million records, each with detailed citation and impact metrics. The Astronomy Image Explorer, hosted by AAS, is a database of images and figures published in peer-reviewed astronomy journals. In these records, the use of color in figures began in the mid-1990s and has become generally conventional since then. Using glue, a Python library that explores relationships within and between related datasets, we generated color distribution histograms of figures in AIE for ADS-listed journal articles in color figure-heavy fields of astronomy, such as stellar formation and galactic evolution. We correlated the color distributions with impact metrics from ADS. We predict results that certain RGB distributions improve article comprehensibility up to a certain threshold.
E+A galaxies are post-starburst galaxies that have recently undergone quenching of their star formation and now lie in the “green valley” transition zone, making them a valuable source for studying the evolution of galaxies. Using data from DR15 of the Sloan Digital Sky Survey, we analyzed a sample of 4,435 galaxies from the MaNGA (Mapping of Nearby Galaxies at APO) catalogue. We identified 52 E+A galaxies using their optical spectra, based on their spectral shape, u-r color, lack of Hα emission, and hydrogen Balmer absorption. We manually measured the equivalent widths of the Balmer absorption lines using PyRAF to overcome inconsistencies in the spectral synthesis modeling of these galaxies that underestimated their line strengths. Interestingly, we found that 27 of the 52 E+As are within 3 degrees projected distance from the center of the Coma Cluster and of comparable redshift to the cluster. The large number of E+As in and around the Coma Cluster hints at the influence of a dense galaxy environment on the formation of E+A galaxies, as has been suggested by previous authors, and the potential value of E+A galaxies as a diagnostic tool to study the formation of clusters. This work was supported by grant #AST-1852355 from the National Science Foundation to the CUNY College of Staten Island and the American Museum of Natural History.
The National Science Olympiad is the United States’ largest K-12 science competition, reaching over 250,000 students at nearly 8,00 schools in all 50 states. Competitors participate in a variety of events designed to prepare students for STEM careers by exploring topics ranging from constructing maximally efficient bridges to implementing advanced machine learning algorithms to analyzing real astronomical data using JS9. Since 2004, the Astronomy event has been a staple of the National competition and supervised at hundreds of college campuses annually. This upcoming year, the event will probe an understanding of fundamental stellar evolution principles in the context of galaxy formation and evolution. Competitors will answer questions related to the concepts underlying modern theories of star and galaxy formation and evolution, such as the warm-hot intergalactic medium, Beta Cephei pulsation mechanisms, and the nuances of the Lambda-CDM model; apply quantitative relations to solve theoretical problems or draw insights from real astronomical data; and demonstrate a masterful awareness of recent research surrounding 16 deep space objects, including M87, 3C 273, and JKCS 041. In addition to welcoming feedback from the community, we invite any interested community member to assist in the development of educational resources or Astronomy event materials for students and coaches by contacting Donna Young (firstname.lastname@example.org), Tad Komacek (email@example.com), or Asher Noel (firstname.lastname@example.org). Additionally, we encourage community members to volunteer at one of Science Olympiad’s 450 annual tournaments by contacting tournament directors to inquire about supervising an event. Supervisors benefit the younger generation by cultivating a passion for either Astronomy or the broader universe of STEM.
We developed a new analytical method to identify potential missed planets in multi-planet systems found via transit surveys such as those conducted by Kepler and TESS. Our method depends on quantifying a system’s dynamical packing in terms of the dynamical spacing Δ, the number of mutual Hill radii between adjacent planets (“planet pair”). The method determines if a planet pair within a multi-planet system is dynamically unpacked and thus capable of hosting an additional intermediate planet. If a planet pair is found to be unpacked, our method constrains the potential planet’s mass and location. Our method was tested using three well-characterized multi-body systems: the Galilean satellites, the solar system, and TRAPPIST-1. The analysis was run with three previously proposed values for minimum Δ required for planet pair orbital stability (Δ = 10, 12.3, and 21.7). The method was then applied to the Kepler primary mission multi-candidate systems, first via direct calculations and then via Monte Carlo (MC) analysis. Direct calculations show that as many as 560 planet pairs in Kepler multi-candidate systems could contain additional planets (Δ = 12.3). The MC analysis shows that 164 of these pairs have a probability ≥ 0.90 of being unpacked. Furthermore, according to calculated median mass efficiencies, 28.2% of these potential planets could be Earths and Sub-Earths. If these planets exist, the masses and semimajor axes predicted here could facilitate detection by characterizing expected detection signals. Ultimately, understanding the dynamical packing of multi-planet systems could help contribute to our understanding of their architectures.
Asteroseismology, the study of stellar pulsations, provides great insight into a star’s physical parameters, and with different computational methods, we are able to estimate the unknown characteristics of stars (like chemistry or age). In this project, we present a web-based interface with the ability to predict the physical properties of solar-like stars from stellar observations with the use of machine learning. With perturbation techniques and a Classification and Regressions Trees Algorithm (CART) on 350,000 theoretical stellar models to learn the matching between unknown and known properties of the star and predict its unknown parameters with a trained regression model. Initially developed with a database, the architecture of the interface consists of two separate frame-works: a back-end model and a dynamic front-end web application. With an additionally implemented, asynchronous front-end execution, email delivery server, and online database for storage of data and results, the interface successfully handles incoming and existing users and data. Finally, the mean predictions of 16 Cygni A, 16 Cygni B, and KIC 12258514 resulted in a 3.11% difference to original predictions, and the average prediction time was 6.27 ±1.06 seconds per star. This novel engineering project is potentially another great tool for stellar research which we plan to deploy simultaneously with the submission of a future publication that extends the back-end algorithm to handle more kinds of stars.