Closely-Spaced Object Classification Using MuyGPyS

Accurately detecting rendezvous and proximity operations (RPO) is crucial for understanding how objects are behaving in the space domain. However, detecting closely-spaced objects (CSO) is challenging for ground-based optical space domain awareness (SDA) algorithms as two objects close together along the line-of-sight can appear blended as a single object within the point-spread function (PSF) of the optical system. Traditional machine learning methods can be useful for differentiating between singular objects and closely-spaced objects, but many methods require large training sample sizes or high signal-to-noise conditions. The quality and quantity of realistic data make probabilistic classification methods a superior approach, as they are better suited to handle these data inadequacies. We present CSO classification results using the Gaussian process python package, MuyGPyS, and examine classification accuracy as a function of angular separation and magnitude difference between the simulated satellites. This orbit-independent analysis is done on highly accurate simulated SDA images that emulate realistic ground-based commercial-of-the-shelf (COTS) optical sensor observations of CSOs. We find that MuyGPyS outperforms traditional machine learning methods, especially under more challenging circumstances.Comment: Accepted to the 2023 Advanced Maui Optical and Space Surveillance Technologies Conference (AMOS

Disentangling the Black Hole Mass Spectrum with Photometric Microlensing Surveys

From the formation mechanisms of stars and compact objects to nuclear physics, modern astronomy frequently leverages surveys to understand populations of objects to answer fundamental questions. The population of dark and isolated compact objects in the Galaxy contains critical information related to many of these topics, but is only practically accessible via gravitational microlensing. However, photometric microlensing observables are degenerate for different types of lenses, and one can seldom classify an event as involving either a compact object or stellar lens on its own. To address this difficulty, we apply a Bayesian framework that treats lens type probabilistically and jointly with a lens population model. This method allows lens population characteristics to be inferred despite intrinsic uncertainty in the lens-class of any single event. We investigate this method's effectiveness on a simulated ground-based photometric survey in the context of characterizing a hypothetical population of primordial black holes (PBHs) with an average mass of $30 M_{\odot}$. On simulated data, our method outperforms current black hole (BH) lens identification pipelines and characterizes different subpopulations of lenses while jointly constraining the PBH contribution to dark matter to ${\approx}25$\%. Key to robust inference, our method can marginalize over population model uncertainty. We find the lower mass cutoff for stellar origin BHs, a key observable in understanding the BH mass gap, particularly difficult to infer in our simulations. This work lays the foundation for cutting-edge PBH abundance constraints to be extracted from current photometric microlensing surveys.Comment: 31 pages, 18 figures, submitted to AA

A Reanalysis of Public Galactic Bulge Gravitational Microlensing Events from OGLE-III and IV

Modern surveys of gravitational microlensing events have progressed to detecting thousands per year. Surveys are capable of probing Galactic structure, stellar evolution, lens populations, black hole physics, and the nature of dark matter. One of the key avenues for doing this is studying the microlensing Einstein radius crossing time distribution ($t_E$). However, systematics in individual light curves as well as over-simplistic modeling can lead to biased results. To address this, we developed a model to simultaneously handle the microlensing parallax due to Earth's motion, systematic instrumental effects, and unlensed stellar variability with a Gaussian Process model. We used light curves for nearly 10,000 OGLE-III and IV Milky Way bulge microlensing events and fit each with our model. We also developed a forward model approach to infer the timescale distribution by forward modeling from the data rather than using point estimates from individual events. We find that modeling the variability in the baseline removes a source of significant bias in individual events, and previous analyses over-estimated the number of long timescale ($t_E>100$ days) events due to their over simplistic models ignoring parallax effects and stellar variability. We use our fits to identify hundreds of events that are likely black holes.Comment: Submitted version, in review, 33 pages, 18 figures, MCMC posterior samples available by publisher after acceptanc

The Complete Calibration of the Color–Redshift Relation (C3R2) Survey: Analysis and Data Release 2

The Complete Calibration of the Color-Redshift Relation (C3R2) survey is a multi-institution, multi-instrument survey that aims to map the empirical relation of galaxy color to redshift to i ~ 24.5 (AB), thereby providing a firm foundation for weak lensing cosmology with the Stage IV dark energy missions Euclid and WFIRST. Here we present 3171 new spectroscopic redshifts obtained in the 2016B and 2017A semesters with a combination of DEIMOS, LRIS, and MOSFIRE on the Keck telescopes.13 The observations come from all of the Keck partners: Caltech, NASA, the University of Hawaii, and the University of California. Combined with the 1283 redshifts published in DR1, the C3R2 survey has now obtained and published 4454 high-quality galaxy redshifts. We discuss updates to the survey design and provide a catalog of photometric and spectroscopic data. Initial tests of the calibration method performance are given, indicating that the sample, once completed and combined with extensive data collected by other spectroscopic surveys, should allow us to meet the cosmology requirements for Euclid, and make significant headway toward solving the problem for WFIRST. We use the full spectroscopic sample to demonstrate that galaxy brightness is weakly correlated with redshift once a galaxy is localized in the Euclid or WFIRST color space, with potentially important implications for the spectroscopy needed to calibrate redshifts for faint WFIRST and LSST sources