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Register a new userLight Echoes of Ancient Transients with the Blanco CTIO 4m Telescope
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For over a century, light echoes have been observed around variable stars and transients. The discovery of centuries-old light echoes from supernovae in the Large Magellanic Cloud has allowed the spectroscopic characterization of these events using modern instrumentation, even in the complete absence of any visual record of those events. Here we review the pivotal role the Blanco 4m telescope played in these discoveries.
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Light echoes, light from a variable source scattered off dust, have been observed for over a century. The recent discovery of light echoes around centuries-old supernovae in the Milky Way and the Large Magellanic Cloud have allowed the spectroscopic characterization of these events, even without contemporaneous photometry and spectroscopy using modern instrumentation. Here we review the recent scientific advances using light echoes of ancient and historic transients, and focus on our latest work on SN 1987As and Eta Carinaes light echoes.
Astronomical light echoes, the time-dependent light scattered by dust in the vicinity of varying objects, have been recognized for over a century. Initially, their utility was thought to be confined to mapping out the three-dimensional distribution of interstellar dust. Recently, the discovery of spectroscopically-useful light echoes around centuries-old supernovae in the Milky Way and the Large Magellanic Cloud has opened up new scientific opportunities to exploit light echoes. In this review, we describe the history of light echoes in the local Universe and cover the many new developments in both the observation of light echoes and the interpretation of the light scattered from them. Among other benefits, we highlight our new ability to spectroscopically classify outbursting objects, to view them from multiple perspectives, to obtain a spectroscopic time series of the outburst, and to establish accurate distances to the source event. We also describe the broader range of variable objects whose properties may be better understood from light echo observations. Finally, we discuss the prospects of new light echo techniques not yet realized in practice.
We use gravitational lensing of the cosmic microwave background (CMB) to measure the mass of the most distant blindly-selected sample of galaxy clusters on which a lensing measurement has been performed to date. In CMB data from the the Atacama Cosmology Telescope (ACT) and the Planck satellite, we detect the stacked lensing effect from 677 near-infrared-selected galaxy clusters from the Massive and Distant Clusters of WISE Survey (MaDCoWS), which have a mean redshift of $ langle z rangle = 1.08$. There are no current optical weak lensing measurements of clusters that match the distance and average mass of this sample. We detect the lensing signal with a significance of $4.2 sigma$. We model the signal with a halo model framework to find the mean mass of the population from which these clusters are drawn. Assuming that the clusters follow Navarro-Frenk-White density profiles, we infer a mean mass of $langle M_{500c}rangle = left(1.7 pm 0.4 right)times10^{14},mathrm{M}_odot$. We consider systematic uncertainties from cluster redshift errors, centering errors, and the shape of the NFW profile. These are all smaller than 30% of our reported uncertainty. This work highlights the potential of CMB lensing to enable cosmological constraints from the abundance of distant clusters populating ever larger volumes of the observable Universe, beyond the capabilities of optical weak lensing measurements.
We report the successful identification of the type of the supernova responsible for the supernova remnant SNR 0509-675 in the Large Magellanic Cloud (LMC) using Gemini spectra of surrounding light echoes. The ability to classify outbursts associated with centuries-old remnants provides a new window into several aspects of supernova research and is likely to be successful in providing new constraints on additional LMC supernovae as well as their historical counterparts in the Milky Way Galaxy (MWG). The combined spectrum of echo light from SNR 0509-675 shows broad emission and absorption lines consistent with a supernova (SN) spectrum. We create a spectral library consisting of 26 SNe Ia and 6 SN Ib/c that are time-integrated, dust-scattered by LMC dust, and reddened by the LMC and MWG. We fit these SN templates to the observed light echo spectrum using $chi^2$ minimization as well as correlation techniques, and we find that overluminous 91T-like SNe Ia with $dm15<0.9$ match the observed spectrum best.
We present orbital elements and mass sums for eighteen visual binary stars of spectral types B to K (five of which are new orbits) with periods ranging from 20 to more than 500 yr. For two double-line spectroscopic binaries with no previous orbits, the individual component masses, using combined astrometric and radial velocity data, have a formal uncertainty of ~0.1 MSun. Adopting published photometry, and trigonometric parallaxes, plus our own measurements, we place these objects on an H-R diagram, and discuss their evolutionary status. These objects are part of a survey to characterize the binary population of stars in the Southern Hemisphere, using the SOAR 4m telescope+HRCAM at CTIO. Orbital elements are computed using a newly developed Markov Chain Monte Carlo algorithm that delivers maximum likelihood estimates of the parameters, as well as posterior probability density functions that allow us to evaluate the uncertainty of our derived parameters in a robust way. For spectroscopic binaries, using our approach, it is possible to derive a self-consistent parallax for the system from the combined astrometric plus radial velocity data (orbital parallax), which compares well with the trigonometric parallaxes. We also present a mathematical formalism that allows a dimensionality reduction of the feature space from seven to three search parameters (or from ten to seven dimensions - including parallax - in the case of spectroscopic binaries with astrometric data), which makes it possible to explore a smaller number of parameters in each case, improving the computational efficiency of our Markov Chain Monte Carlo code.
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