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Abstract Primordial black holes (PBHs), theorized to have originated in the early Universe, are speculated to be a viable form of dark matter. If they exist, they should be detectable through photometric and astrometric signals resulting from gravitational microlensing of stars in the Milky Way. Population Synthesis for Compact-object Lensing Events, orPopSyCLE, is a simulation code that enables users to simulate microlensing surveys, and is the first of its kind to include both photometric and astrometric microlensing effects, which are important for potential PBH detection and characterization. To estimate the number of observable PBH microlensing events, we modifyPopSyCLEto include a dark matter halo consisting of PBHs. We detail our PBH population model, and demonstrate ourPopSyCLE+ PBH results through simulations of the Optical Gravitational Lensing Experiment-IV (OGLE-IV) and Nancy Grace Roman Space Telescope (Roman) microlensing surveys. We provide a proof-of-concept analysis for adding PBHs intoPopSyCLE, and thus include many simplifying assumptions, such asf_DM, the fraction of dark matter composed of PBHs, and ${\overline{m}}_{\mathrm{PBH}}$, mean PBH mass. Assuming ${\overline{m}}_{\mathrm{PBH}}=30$M_⊙, we find ∼3.6f_DMtimes as many PBH microlensing events than stellar evolved black hole events, a PBH average peak Einstein crossing time of ∼91.5 days, estimate on order of 10²f_DMPBH events within the 8 yr OGLE-IV results, and estimate Roman to detect ∼1000f_DMPBH microlensing events throughout its planned microlensing survey. 

Abstract Studies of star formation in various galaxy cluster mergers have reached apparently contradictory conclusions regarding whether mergers stimulate star formation, quench it, or have no effect. Because the mergers studied span a range of time since pericenter (TSP), it is possible that the apparent effect on star formation is a function of the TSP. We use a sample of 12 bimodal mergers to assess the star formation as a function of TSP. We measure the equivalent width of the Hαemission line in ∼100 member galaxies in each merger, classify galaxies as emitters or nonemitters, and then classify emitters as star-forming galaxies (SFGs) or active galactic nucleus (AGN) based on the [Nii]λ6583 line. We quantify the distribution of SFG and AGN relative to nonemitters along the spatial axis defined by the subcluster separation. The SFG and AGN fractions vary from merger to merger but show no trend with TSP. The spatial distribution of SFG is consistent with that of nonemitters in eight mergers, but show significant avoidance of the system center in the remaining four mergers, including the three with the lowest TSP. If there is a connection between star formation activity and TSP, probing it further will require more precise TSP estimates and more mergers with TSP in the range of 0–400 Myr. 

Abstract 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 30M_⊙. 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 ≈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. 

Abstract Modern surveys of gravitational microlensing events have progressed to detecting thousands per year, and 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 the microlensing Einstein radius crossing time ( t E ) distribution. However, systematics in individual light curves as well as oversimplistic 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 t E 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 the previous analyses overestimated the number of t E > 100 day events due to their oversimplistic model ignoring parallax effects. We use our fits to identify the hundreds filling a regime in the microlensing parameter space that are 50% pure of black holes. Finally, we have released the largest-ever catalog of Markov Chain Monte Carlo parameter estimates for microlensing events. 

Abstract High-quality Extragalactic Legacy-field Monitoring (HELM) is a long-term observing program that photometrically monitors several well-studied extragalactic legacy fields with the Dark Energy Camera (DECam) imager on the CTIO 4 m Blanco telescope. Since 2019 February, HELM has been monitoring regions within COSMOS, XMM-LSS, CDF-S, S-CVZ, ELAIS-S1, and SDSS Stripe 82 with few-day cadences in the (u)gri(z) bands, over a collective sky area of ∼38 deg². The main science goal of HELM is to provide high-quality optical light curves for a large sample of active galactic nuclei (AGNs), and to build decades-long time baselines when combining past and future optical light curves in these legacy fields. These optical images and light curves will facilitate the measurements of AGN reverberation mapping lags, as well as studies of AGN variability and its dependencies on accretion properties. In addition, the time-resolved and coadded DECam photometry will enable a broad range of science applications from galaxy evolution to time-domain science. We describe the design and implementation of the program and present the first data release that includes source catalogs and the first ∼3.5 yr of light curves during 2019A–2022A.