Engineering topics

Pereyra, M.

Publications and source records attributed to Pereyra, M..

2 records

DOE OSTI2021

Constraints on the Electromagnetic Counterpart of the Neutron-star–Black-hole Merger GW200115

We report the results of our follow-up campaign for the neutron-star—black-hole (NSBH) merger GW200115 detected during the O3 run of the Advanced LIGO and Advanced Virgo detectors. We obtained wide-field observations with the Deca-Degree Optical Transient Imager covering ~20% of the total probability area down to a limiting magnitude of w = 20.5 AB at ~23 hr after the merger. Our search for counterparts returns a single candidate (AT2020aeo), likely not associated with the merger. In total, only 25 sources of interest were identified by the community and later discarded as unrelated to the GW event. We compare our upper limits with the emission predicted by state-of-the-art kilonova simulations and disfavor high-mass ejecta (>0.1 M⊙), indicating that the spin of the system is not particularly high. By combining our optical limits with gamma-ray constraints from Swift and Fermi, we disfavor the presence of a standard short-duration burst for viewing angles ≲15° from the jet axis. Our conclusions are, however, limited by the large localization region of this GW event, and accurate prompt positions remain crucial to improving the efficiency of follow-up efforts.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2019

Sparse Bayesian mass mapping with uncertainties: local credible intervals

ABSTRACT Until recently, mass-mapping techniques for weak gravitational lensing convergence reconstruction have lacked a principled statistical framework upon which to quantify reconstruction uncertainties, without making strong assumptions of Gaussianity. In previous work, we presented a sparse hierarchical Bayesian formalism for convergence reconstruction that addresses this shortcoming. Here, we draw on the concept of local credible intervals (cf. Bayesian error bars) as an extension of the uncertainty quantification techniques previously detailed. These uncertainty quantification techniques are benchmarked against those recovered via Px-MALA – a state-of-the-art proximal Markov chain Monte Carlo (MCMC) algorithm. We find that, typically, our recovered uncertainties are everywhere conservative (never underestimate the uncertainty, yet the approximation error is bounded above), of similar magnitude and highly correlated with those recovered via Px-MALA. Moreover, we demonstrate an increase in computational efficiency of $\mathcal {O}(10^6)$ when using our sparse Bayesian approach over MCMC techniques. This computational saving is critical for the application of Bayesian uncertainty quantification to large-scale stage IV surveys such as LSST and Euclid.

Price, M. A.↗