While a hindrance to statistical and computational models of inference, missing knowledge can be exploited by organisms in their natural environments. The recognition heuristic utilizes missing knowledge to make accurate inferences about the real world. A consequence of applying this heuristic is a counterintuitive less-is-more effect where less knowledge is better than more for inferential accuracy. Theoretical arguments and experimental evidence supporting the less-is-more effect are given.

Most approaches to modeling rational inference do not take inio account that in the real world, organisms make inferences under limited time and knowledge. In this tradition, the mind is treated as a calculating demon equipped with unlimited time, knowledge, and computational might. We propose a family of satisficing algorithms based on a simple psychological mechanism: one-reason decision making. These fast and frugal algorithms violate fundamental tenets of classical rationality, for example, they neither look up nor integrate all information. By computer simulation, we held a competition between the satisficing Take The Best algorithm and various more "optimal" decision procedures. The Take The Best algorithm matched or outperformed all competitors in inferential speed and accuracy. Most interesting was the flnding that the best algorithms in the competition, those which used a form of one-reason decision making, exhibited a startling "less-is-more" effect: they performed better with missing knowledge than with complete knowledge. We discuss the less-is-more effect and present evidence of it in human reasoning. This counter-intuitive effect demonstrates that the mind can satisfice and seize upon regularities in the environment to the extent that it can exploit even the absence of knowledge as knowledge.

Type Ia supernovae (SNe Ia) that are multiply imaged by gravitational lensing can extend the SN Ia Hubble diagram to very high redshifts (z ≳ 2), probe potential SN Ia evolution, and deliver high-precision constraints on H 0, w, and Ωm via time delays. However, only one, iPTF16geu, has been found to date, and many more are needed to achieve these goals. To increase the multiply imaged SN Ia discovery rate, we present a simple algorithm for identifying gravitationally lensed SN Ia candidates in cadenced, wide-field optical imaging surveys. The technique is to look for supernovae that appear to be hosted by elliptical galaxies, but that have absolute magnitudes implied by the apparent hosts' photometric redshifts that are far brighter than the absolute magnitudes of normal SNe Ia (the brightest type of supernovae found in elliptical galaxies). Importantly, this purely photometric method does not require the ability to resolve the lensed images for discovery. Active galactic nuclei, the primary sources of contamination that affect the method, can be controlled using catalog cross-matches and color cuts. Highly magnified core-collapse SNe will also be discovered as a byproduct of the method. Using a Monte Carlo simulation, we forecast that the Large Synoptic Survey Telescope can discover up to 500 multiply imaged SNe Ia using this technique in a 10 year z-band search, more than an order of magnitude improvement over previous estimates. We also predict that the Zwicky Transient Facility should find up to 10 multiply imaged SNe Ia using this technique in a 3 year R-band search - despite the fact that this survey will not resolve a single system.

Supernovae that are strongly gravitationally lensed (gLSNe) by elliptical galaxies are powerful probes of astrophysics and cosmology that will be discovered systematically by wide-field, high-cadence imaging surveys such as the Zwicky Transient Facility (ZTF) and the Large Synoptic Survey Telescope (LSST). Here we use pixel-level simulations that include observing strategy, target selection, supernova properties, and dust to forecast the rates and properties of gLSNe that ZTF and LSST will find. Applying the resolution-insensitive discovery strategy of Goldstein et al., we forecast that ZTF (LSST) can discover 0.02 (0.79) 91bg-like, 0.17 (5.92) 91T-like, 1.22 (47.84) Type Ia, 2.76 (88.51) Type IIP, 0.31 (12.78) Type IIL, and 0.36 (15.43) Type Ib/c gLSNe per year, with uncertainties dominated by uncertainties in the supernova rate. We also forecast that the surveys can discover at least 3.75 (209.32) Type IIn gLSNe per year, for a total of at least 8.60 (380.60) gLSNe per year under fiducial observing strategies. ZTF gLSNe have a median z s = 0.9, z l = 0.35, , Δt max = 10 days, min(θ) = 0.″25, and N img = 4. LSST gLSNe are less compact and less magnified, with a median z s = 1.0, z l = 0.4, , Δt max = 25 days, min(θ) = 0.″6, and N img = 2. We develop a model of the supernova-host galaxy connection and find that the vast majority of gLSN host galaxies will be multiply imaged, enabling detailed constraints on lens models with sufficiently deep high-resolution imaging taken after the supernova has faded. We release the results of our simulations as catalogs at http://portal.nersc.gov/project/astro250/glsne/.

Time delays between the multiple images of strongly gravitationally lensed Type Ia supernovae (glSNe Ia) have the potential to deliver precise cosmological constraints, but the effects of microlensing on time delay extraction have not been studied in detail. Here we quantify the effect of microlensing on the glSN Ia yield of the Large Synoptic Survey Telescope (LSST) and the effect of microlensing on the precision and accuracy of time delays that can be extracted from LSST glSNe Ia. Microlensing has a negligible effect on the LSST glSN Ia yield, but it can be increased by a factor of ∼2 over previous predictions to 930 systems using a novel photometric identification technique based on spectral template fitting. Crucially, the microlensing of glSNe Ia is achromatic until three rest-frame weeks after the explosion, making the early-time color curves microlensing-insensitive time delay indicators. By fitting simulated flux and color observations of microlensed glSNe Ia with their underlying, unlensed spectral templates, we forecast the distribution of absolute time delay error due to microlensing for LSST, which is unbiased at the sub-percent level and peaked at 1% for color curve observations in the achromatic phase, while for light-curve observations it is comparable to state-of-the-art mass modeling uncertainties (4%). About 70% of LSST glSN Ia images should be discovered during the achromatic phase, indicating that microlensing time delay uncertainties can be minimized if prompt multicolor follow-up observations are obtained. Accounting for microlensing, the 1-2 day time delay on the recently discovered glSN Ia iPTF16geu can be measured to 40% precision, limiting its cosmological utility.

Single-object imaging and spectroscopy on telescopes with apertures ranging from ~4 m to 40 m have the potential to greatly enhance the cosmological constraints that can be obtained from LSST. Two major cosmological probes will benefit greatly from LSST follow-up: accurate spectrophotometry for nearby and distant Type Ia supernovae will expand the cosmological distance lever arm by unlocking the constraining power of high-z supernovae; and cosmology with time delays of strongly-lensed supernovae and quasars will require additional high-cadence imaging to supplement LSST, adaptive optics imaging or spectroscopy for accurate lens and source positions, and IFU or slit spectroscopy to measure detailed properties of lens systems. We highlight the scientific impact of these two science drivers, and discuss how additional resources will benefit them. For both science cases, LSST will deliver a large sample of objects over both the wide and deep fields in the LSST survey, but additional data to characterize both individual systems and overall systematics will be key to ensuring robust cosmological inference to high redshifts. Community access to large amounts of natural-seeing imaging on ~2-4 m telescopes, adaptive optics imaging and spectroscopy on 8-40 m telescopes, and high-throughput single-target spectroscopy on 4-40 m telescopes will be necessary for LSST time domain cosmology to reach its full potential. In two companion white papers we present the additional gains for LSST cosmology that will come from deep and from wide-field multi-object spectroscopy.

The first two months of the third Advanced LIGO and Virgo observing run (2019 April-May) showed that distant gravitational-wave (GW) events can now be readily detected. Three candidate mergers containing neutron stars (NS) were reported in a span of 15 days, all likely located more than 100 Mpc away. However, distant events such as the three new NS mergers are likely to be coarsely localized, which highlights the importance of facilities and scheduling systems that enable deep observations over hundreds to thousands of square degrees to detect the electromagnetic counterparts. On 2019 May 10 02:59:39.292 UT the GW candidate S190510g was discovered and initially classified as a binary neutron star (BNS) merger with 98% probability. The GW event was localized within an area of 3462 deg2, later refined to 1166 deg2 (90%) at a distance of 227 92 Mpc. We triggered Target-of-Opportunity observations with the Dark Energy Camera (DECam), a wide-field optical imager mounted at the prime focus of the 4 m Blanco Telescope at Cerro Tololo Inter-American Observatory in Chile. This Letter describes our DECam observations and our real-time analysis results, focusing in particular on the design and implementation of the observing strategy. Within 24 hr of the merger time, we observed 65% of the total enclosed probability of the final skymap with an observing efficiency of 94%. We identified and publicly announced 13 candidate counterparts. S190510g was reclassified 1.7 days after the merger, after our observations were completed, with a "BNS merger" probability reduced from 98% to 42% in favor of a "terrestrial classification.