12 research outputs found
Flight of the Bumblebee: the Early Excess Flux of Type Ia Supernova 2023bee revealed by , and Young Supernova Experiment Observations
We present high-cadence ultraviolet through near-infrared observations of the
Type Ia supernova (SN Ia) 2023bee in NGC~2708 ( Mpc), finding
excess flux in the first days after explosion relative to the expected
power-law rise from an expanding fireball. This deviation from typical behavior
for SNe Ia is particularly obvious in our 10-minute cadence light curve
and UV data. Compared to a few other normal SNe Ia with detected early
excess flux, the excess flux in SN 2023bee is redder in the UV and less
luminous. We present optical spectra of SN 2023bee, including two spectra
during the period where the flux excess is dominant. At this time, the spectra
are similar to those of other SNe Ia but with weaker Si II, C II and Ca II
absorption lines, perhaps because the excess flux creates a stronger continuum.
We compare the data to several theoretical models that have been proposed to
explain the early flux excess in SNe Ia. Interaction with either a nearby
companion star or close-in circumstellar material is expected to produce a
faster evolution than seen in the data. Radioactive material in the outer
layers of the ejecta, either from a double detonation explosion or simply an
explosion with a Ni clump near the surface, can not fully reproduce the
evolution either, likely due to the sensitivity of early UV observable to the
treatment of the outer part of ejecta in simulation. We conclude that no
current model can adequately explain the full set of observations. We find that
a relatively large fraction of nearby, bright SNe Ia with high-cadence
observations have some amount of excess flux within a few days of explosion.
Considering potential asymmetric emission, the physical cause of this excess
flux may be ubiquitous in normal SNe Ia.Comment: 21 pages, 12 figures. Accepted by the astrophysical journa
SN 2022oqm: A Multi-peaked Calcium-rich Transient from a White Dwarf Binary Progenitor System
We present the photometric and spectroscopic evolution of SN 2022oqm, a
nearby multi-peaked hydrogen- and helium-weak calcium-rich transient (CaRT). SN
2022oqm was detected 19.9 kpc from its host galaxy, the face-on spiral galaxy
NGC 5875. Extensive spectroscopic coverage reveals a hot (T >= 40,000 K)
continuum and carbon features observed ~1 day after discovery, SN Ic-like
photospheric-phase spectra, and strong forbidden calcium emission starting 38
days after discovery. SN 2022oqm has a relatively high peak luminosity (MB =
-17 mag) for CaRTs, making it an outlier in the population. We determine that
three power sources are necessary to explain SN 2022oqm's light curve, with
each power source corresponding to a distinct peak in its light curve. The
first peak of the light curve is powered by an expanding blackbody with a power
law luminosity, consistent with shock cooling by circumstellar material.
Subsequent peaks are powered by a double radioactive decay model, consistent
with two separate sources of photons diffusing through an optically thick
ejecta. From the optical light curve, we derive an ejecta mass and 56Ni mass of
~0.89 solar masses and ~0.09 solar masses, respectively. Detailed spectroscopic
modeling reveals ejecta that is dominated by intermediate-mass elements, with
signs that Fe-peak elements have been well-mixed. We discuss several physical
origins for SN 2022oqm and favor a white dwarf progenitor model. The inferred
ejecta mass points to a surprisingly massive white dwarf, challenging models of
CaRT progenitors.Comment: 33 pages, 17 figures, 5 tables, Submitted to Ap
Flight of the bumblebee : the early excess flux of Type Ia supernova 2023bee revealed by TESS, Swift, and Young Supernova Experiment observations
We present high-cadence ultraviolet through near-infrared observations of the Type Ia supernova (SN Ia) 2023bee at D = 32 ± 3 Mpc, finding excess flux in the first days after explosion, particularly in our 10 minutes cadence TESS light curve and Swift UV data. Compared to a few other normal SNe Ia with early excess flux, the excess flux in SN 2023bee is redder in the UV and less luminous. We present optical spectra of SN 2023bee, including two spectra during the period where the flux excess is dominant. At this time, the spectra are similar to those of other SNe Ia but with weaker Si ii, C ii, and Ca ii absorption lines, perhaps because the excess flux creates a stronger continuum. We compare the data to several theoretical models on the origin of early excess flux in SNe Ia. Interaction with either the companion star or close-in circumstellar material is expected to produce a faster evolution than observed. Radioactive material in the outer layers of the ejecta, either from double detonation explosion or from a 56Ni clump near the surface, cannot fully reproduce the evolution either, likely due to the sensitivity of early UV observable to the treatment of the outer part of ejecta in simulation. We conclude that no current model can adequately explain the full set of observations. We find that a relatively large fraction of nearby, bright SNe Ia with high-cadence observations have some amount of excess flux within a few days of explosion. Considering potential asymmetric emission, the physical cause of this excess flux may be ubiquitous in normal SNe Ia
Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States
Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks
The United States COVID-19 Forecast Hub dataset
Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages
SN2023ixf in Messier 101: the twilight years of the progenitor as seen by Pan-STARRS
The nearby type II supernova, SN2023ixf in M101 exhibits signatures of early-time interaction with circumstellar material in the first week post-explosion. This material may be the consequence of prior mass loss suffered by the progenitor which possibly manifested in the form of a detectable pre-supernova outburst. We present an analysis of the long-baseline pre-explosion photometric data in , , , , and filters from Pan-STARRS as part of the Young Supernova Experiment, spanning 5,000 days. We find no significant detections in the Pan-STARRS pre-explosion light curve. We train a multilayer perceptron neural network to classify pre-supernova outbursts. We find no evidence of eruptive pre-supernova activity to a limiting absolute magnitude of . The limiting magnitudes from the full set of (average absolute magnitude -8) data are consistent with previous pre-explosion studies. We use deep photometry from the literature to constrain the progenitor of SN2023ixf, finding that these data are consistent with a dusty red supergiant (RSG) progenitor with luminosity 5.12 and temperature 3950K, corresponding to a mass of 14-20 M$_\odot
An open repository of real-time COVID-19 indicators.
The COVID-19 pandemic presented enormous data challenges in the United States. Policy makers, epidemiological modelers, and health researchers all require up-to-date data on the pandemic and relevant public behavior, ideally at fine spatial and temporal resolution. The COVIDcast API is our attempt to fill this need: Operational since April 2020, it provides open access to both traditional public health surveillance signals (cases, deaths, and hospitalizations) and many auxiliary indicators of COVID-19 activity, such as signals extracted from deidentified medical claims data, massive online surveys, cell phone mobility data, and internet search trends. These are available at a fine geographic resolution (mostly at the county level) and are updated daily. The COVIDcast API also tracks all revisions to historical data, allowing modelers to account for the frequent revisions and backfill that are common for many public health data sources. All of the data are available in a common format through the API and accompanying R and Python software packages. This paper describes the data sources and signals, and provides examples demonstrating that the auxiliary signals in the COVIDcast API present information relevant to tracking COVID activity, augmenting traditional public health reporting and empowering research and decision-making