arXiv subjects

Brian Healy

Publications and source records attributed to Brian Healy.

6 recordsLinked to original sources

arXiv2024

Searching for gravitational wave optical counterparts with the Zwicky Transient Facility: summary of O4a

During the first half of the fourth observing run (O4a) of the International Gravitational Wave Network (IGWN), the Zwicky Transient Facility (ZTF) conducted a systematic search for kilonova (KN) counterparts to binary neutron star (BNS) and neutron star-black hole (NSBH) merger candidates. Here, we present a comprehensive study of the five high-significance (FAR < 1 per year) BNS and NSBH candidates in O4a. Our follow-up campaigns relied on both target-of-opportunity observations (ToO) and re-weighting of the nominal survey schedule to maximize coverage. We describe the toolkit we have been developing, Fritz, an instance of SkyPortal, instrumental in coordinating and managing our telescope scheduling, candidate vetting, and follow-up observations through a user-friendly interface. ZTF covered a total of 2841 deg$^2$ within the skymaps of the high-significance GW events, reaching a median depth of g~20.2 mag. We circulated 15 candidates, but found no viable KN counterpart to any of the GW events. Based on the ZTF non-detections of the high-significance events in O4a, we used a Bayesian approach, nimbus, to quantify the posterior probability of KN model parameters that are consistent with our non-detections. Our analysis favors KNe with initial absolute magnitude fainter than -16 mag. The joint posterior probability of a GW170817-like KN associated with all our O4a follow-ups was 64%. Additionally, we use a survey simulation software, simsurvey, to determine that our combined filtered efficiency to detect a GW170817-like KN is 36%, when considering the 5 confirmed astrophysical events in O3 (1 BNS and 4 NSBH), along with our O4a follow-ups. Following Kasliwal et al. (2020), we derived joint constraints on the underlying KN luminosity function based on our O3 and O4a follow-ups, determining that no more than 76% of KNe fading at 1 mag/day can peak at a magnitude brighter than -17.5 mag.

astro-ph.HE↗

arXiv2023

An Optically-Discovered Outburst from XTE J1859+226

Using the Zwicky Transient Facility, in 2021 February we identified the first known outburst of the Black Hole X-ray Transient XTE J1859+226 since its discovery in 1999. The outburst was visible at X-ray, UV, and optical wavelengths for less than 20 days, substantially shorter than its 320-day full outburst in 1999, and the observed peak luminosity was two orders of magnitude lower. Its peak bolometric luminosity was only $2\times 10^{35}$ erg s$^{-1}$, implying an Eddington fraction of about $3\times10^{-4}$. The source remained in the hard spectral state throughout the outburst. From optical spectroscopy measurements we estimate an outer disk radius of 10$^{11}$ cm. The low observed X-ray luminosity is not sufficient to irradiate the entire disk, but we observe a surprising exponential decline in the X-ray lightcurve. These observations highlight the potential of optical and infrared (O/IR) synoptic surveys to discover low-luminosity activity from X-ray transients.

astro-ph.HE↗

arXiv2023

Chemical Distribution of the Dynamical Ejecta in the Neutron Star Merger GW170817

GW170817 and its associated electromagnetic counterpart AT2017gfo continue to be a treasure trove as observations and modeling continue. Recent precision astrometry of AT2017gfo with the Hubble Space Telescope combined with previous constraints from Very Long Baseline Interferometry (VLBI) constraints narrowed down the inclination angle to 19-25 deg (90\% confidence). This paper explores how the inclusion of precise inclination information can reveal new insights about the ejecta properties, in particular, about the composition of the dynamical ejecta of AT2017gfo. Our analysis relies on updated kilonova modeling, which includes state-of-the-art heating rates, thermalization efficiencies, and opacities and is parameterized by $\bar{Y}_{\rm e,dyn}$, the average electron fraction of the dynamical ejecta component. Using this model, we incorporate the latest inclination angle constraint of AT2017gfo into a light curve fitting framework to derive updated parameter estimates. Our results suggest that the viewing angle of the observer is pointed towards the lanthanide-poor ($Y_{\rm e,dyn}\gtrsim0.25$), squeezed polar dynamical ejecta component, which can explain the early blue emission observed in the light curve of AT2017gfo. In contrast to a recent claim of spherical ejecta powering AT2017gfo, our study indicates that the composition of the dynamical ejecta has a strong angular dependence, with a lanthanide-rich ($Y_{\rm e,dyn}\lesssim0.25$), tidal component distributed around the merger plane with a half-opening angle of $35^\circ$. The inclination angle constraint reduces $\bar{Y}_{\rm e,dyn}$ from $0.24$ to $0.22$, with values $0.17\lesssim Y_{\rm e, dyn} \lesssim0.41$ enabling the robust production of $r$-process elements up to the $3^{\rm rd}$ peak in the tidal dynamical ejecta.

astro-ph.HE↗

arXiv2023

Bayesian model selection for GRB 211211A through multi-wavelength analyses

Although GRB 211211A is one of the closest gamma-ray bursts (GRBs), its classification is challenging because of its partially inconclusive electromagnetic signatures. In this paper, we investigate four different astrophysical scenarios as possible progenitors for GRB~211211A: a binary neutron-star merger, a black-hole--neutron-star merger, a core-collapse supernova, and an r-process enriched core collapse of a rapidly rotating massive star (a collapsar). We perform a large set of Bayesian multi-wavelength analyses based on different models describing these scenarios and priors to investigate which astrophysical scenarios and processes might be related to GRB~211211A. Our analysis supports previous studies in which the presence of an additional component, likely related to $r$-process nucleosynthesis, is required to explain the observed light curves of GRB~211211A, as it can not solely be explained as a GRB afterglow. Fixing the distance to about $350~\rm Mpc$, namely the distance of the possible host galaxy SDSS J140910.47+275320.8, we find a statistical preference for a binary neutron-star merger scenario.

astro-ph.HE↗

arXiv2020

PCA for Implied Volatility Surfaces

Principal component analysis (PCA) is a useful tool when trying to construct factor models from historical asset returns. For the implied volatilities of U.S. equities there is a PCA-based model with a principal eigenportfolio whose return time series lies close to that of an overarching market factor. The authors show that this market factor is the index resulting from the daily compounding of a weighted average of implied-volatility returns, with weights based on the options' open interest (OI) and Vega. The authors also analyze the singular vectors derived from the tensor structure of the implied volatilities of S&P500 constituents, and find evidence indicating that some type of OI and Vega-weighted index should be one of at least two significant factors in this market.

q-fin.ST↗

arXiv2019

Data science is science's second chance to get causal inference right: A classification of data science tasks

Causal inference from observational data is the goal of many data analyses in the health and social sciences. However, academic statistics has often frowned upon data analyses with a causal objective. The introduction of the term "data science" provides a historic opportunity to redefine data analysis in such a way that it naturally accommodates causal inference from observational data. Like others before, we organize the scientific contributions of data science into three classes of tasks: Description, prediction, and counterfactual prediction (which includes causal inference). An explicit classification of data science tasks is necessary to discuss the data, assumptions, and analytics required to successfully accomplish each task. We argue that a failure to adequately describe the role of subject-matter expert knowledge in data analysis is a source of widespread misunderstandings about data science. Specifically, causal analyses typically require not only good data and algorithms, but also domain expert knowledge. We discuss the implications for the use of data science to guide decision-making in the real world and to train data scientists.

stat.ML↗