We present Hubble Space Telescope imaging of a pre-explosion counterpart to SN 2019yvr obtained 2.6 years before its explosion as a type Ib supernova (SN Ib). Aligning to a post-explosion Gemini-S/GSAOI image, we demonstrate that there is a single source consistent with being the SN 2019yvr progenitor system, the second SN Ib progenitor candidate after iPTF13bvn. We also analyzed pre-explosion Spitzer/IRAC imaging, but we do not detect any counterparts at the SN location. SN 2019yvr was highly reddened, and comparing its spectra and photometry to those of other, less extinguished SNe Ib we derive $E(B-V)=0.51\substack{+0.27\\-0.16}$ mag for SN 2019yvr. Correcting photometry of the pre-explosion source for dust reddening, we determine that this source is consistent with a $\log(L/L_{\odot}) = 5.3 \pm 0.2$ and $T_{\mathrm{eff}} = 6800\substack{+400\\-200}$ K star. This relatively cool photospheric temperature implies a radius of 320$\substack{+30\\-50} R_{\odot}$, much larger than expectations for SN Ib progenitor stars with trace amounts of hydrogen but in agreement with previously identified SN IIb progenitor systems. The photometry of the system is also consistent with binary star models that undergo common envelope evolution, leading to a primary star hydrogen envelope mass that is mostly depleted but seemingly in conflict with the SN Ib classification of SN 2019yvr. SN 2019yvr had signatures of strong circumstellar interaction in late-time ($>$150 day) spectra and imaging, and so we consider eruptive mass loss and common envelope evolution scenarios that explain the SN Ib spectroscopic class, pre-explosion counterpart, and dense circumstellar material. We also hypothesize that the apparent inflation could be caused by a quasi-photosphere formed in an extended, low-density envelope or circumstellar matter around the primary star.
Context. Determining photometric redshifts (photo- z s) of extragalactic sources to a high accuracy is paramount to measure distances in wide-field cosmological experiments. With only photometric information at hand, photo- z s are prone to systematic uncertainties in the intervening extinction and the unknown underlying spectral-energy distribution of different astrophysical sources, leading to degeneracies in the modern machine learning algorithm that impacts the level of accuracy for photo- z estimates. Aims. Here, we aim to resolve these model degeneracies and obtain a clear separation between intrinsic physical properties of astrophysical sources and extrinsic systematics. Furthermore, we aim to have meaningful estimates of the full photo- z probability distribution, and their uncertainties. Methods. We performed a probabilistic photo- z determination using mixture density networks (MDN). The training data set is composed of optical ( g r i z photometric bands) point-spread-function and model magnitudes and extinction measurements from the SDSS-DR15 and WISE mid-infrared (3.4 μm and 4.6 μm) model magnitudes. We used infinite Gaussian mixture models to classify the objects in our data set as stars, galaxies, or quasars, and to determine the number of MDN components to achieve optimal performance. Results. The fraction of objects that are correctly split into the main classes of stars, galaxies, and quasars is 94%. Furthermore, our method improves the bias of photometric redshift estimation (i.e., the mean Δ z = ( z p − z s )/(1 + z s )) by one order of magnitude compared to the SDSS photo- z , and it decreases the fraction of 3 σ outliers (i.e., 3 × rms(Δ z ) < Δ z ). The relative, root-mean-square systematic uncertainty in our resulting photo- z s is down to 1.7% for benchmark samples of low-redshift galaxies ( z s < 0.5). Conclusions. We have demonstrated the feasibility of machine-learning-based methods that produce full probability distributions for photo- z estimates with a performance that is competitive with state-of-the art techniques. Our method can be applied to wide-field surveys where extinction can vary significantly across the sky and with sparse spectroscopic calibration samples. The code is publicly available.
Continuous intravenous epoprostenol improves exercise capacity, haemodynamics, and survival in severe primary pulmonary hypertension. Pulmonary hypertension can also be life-threatening in patients with connective tissue diseases. In a prospective open monocentre uncontrolled study, the effects of epoprostenol were evaluated in patients with severe pulmonary hypertension secondary to connective tissue diseases who were unresponsive to oral vasodilators (including calcium channel blockers) and continued to be in the New York Heart Association (NYHA) functional class III or IV despite conventional medical therapy. Seventeen patients received epoprostenol administered by a portable infusion pump associated with conventional therapy (oral anticoagulants, diuretics, supplemental oxygen). During the first six weeks of therapy, two (12%) patients died, of pulmonary oedema (n = 1) and severe sepsis (n = 1). In the fifteen remaining subjects, clinical and haemodynamic parameters improved significantly at six weeks. These patients were subsequently monitored for 80+/-48 (range 14-154) weeks after initiation of epoprostenol. Five (33%) patients died, of right heart failure (n = 2), severe sepsis (n = 2) or syncope (n = 1) and two patients were successfully transplanted 24 and 52 weeks after initiation of epoprostenol. Seven of the remaining eight patients had a persistent clinical improvement. Short-term epoprostenol therapy is effective in some patients with connective tissue diseases as demonstrated by better clinical status and haemodynamics at six weeks. However, this study reports several cases of early and late major complications including severe sepsis and pulmonary oedema. Additional information is needed to evaluate the benefit: risk ratio of long-term epoprostenol therapy in pulmonary hypertension secondary to connective tissue diseases.
The merger of two compact objects of which at least one is a neutron star is signalled by transient electromagnetic emission in a kilonova (KN). This event is accompanied by gravitational waves and possibly other radiation messengers such as neutrinos or cosmic rays. The electromagnetic emission arises from the radioactive decay of heavy r -process elements synthesized in the material ejected during and after the merger. In this paper we show that the analysis of KNe light curves can provide cosmological distance measurements and constrain the properties of the ejecta. In this respect, MAAT, the new Integral Field Unit in the OSIRIS spectrograph on the 10.4 m Gran Telescopio CANARIAS (GTC), is well suited for the study of KNe by performing absolute spectro-photometry over the entire 3600 − 10 000 Å spectral range. Here, we study the most representative cases regarding the scientific interest of KNe from binary neutron stars, and we evaluate the observational prospects and performance of MAAT on the GTC to do the following: (a) study the impact of the equation of state on the KN light curve, and determine to what extent bounds on neutron star (NS) radii or compactness deriving from KN peak magnitudes can be identified and (b) measure the Hubble constant, H 0 , with precision improved by up to 40%, when both gravitational wave data and photometric-light curves are used. In this context we discuss how the equation of state, the viewing angle, and the distance affect the precision and estimated value of H 0 .
We present the largest and most homogeneous collection of near-infrared (NIR) spectra of Type Ia supernovae (SNe Ia): 339 spectra of 98 individual SNe obtained as part of the Carnegie Supernova Project-II. These spectra, obtained with the FIRE spectrograph on the 6.5 m Magellan Baade telescope, have a spectral range of 0.8--2.5 $μ$m. Using this sample, we explore the NIR spectral diversity of SNe Ia and construct a template of spectral time series as a function of the light-curve-shape parameter, color stretch $s_{BV}$. Principal component analysis is applied to characterize the diversity of the spectral features and reduce data dimensionality to a smaller subspace. Gaussian process regression is then used to model the subspace dependence on phase and light-curve shape and the associated uncertainty. Our template is able to predict spectral variations that are correlated with $s_{BV}$, such as the hallmark NIR features: Mg II at early times and the $H$-band break after peak. Using this template reduces the systematic uncertainties in K-corrections by ~90% compared to those from the Hsiao template. These uncertainties, defined as the mean K-correction differences computed with the color-matched template and observed spectra, are on the level of $4\times10^{-4}$ mag on average. This template can serve as the baseline spectral energy distribution for light-curve fitters and can identify peculiar spectral features that might point to compelling physics. The results presented here will substantially improve future SN~Ia cosmological experiments, for both nearby and distant samples.
Abstract The nearby type II supernova, SN 2023ixf in M101 exhibits signatures of early time interaction with circumstellar material in the first week postexplosion. This material may be the consequence of prior mass loss suffered by the progenitor, which possibly manifested in the form of a detectable presupernova outburst. We present an analysis of long-baseline preexplosion photometric data in the g , w , r , i , z , and y filters from Pan-STARRS as part of the Young Supernova Experiment, spanning ∼5000 days. We find no significant detections in the Pan-STARRS preexplosion light curves. We train a multilayer perceptron neural network to classify presupernova outbursts. We find no evidence of eruptive presupernova activity to a limiting absolute magnitude of −7 mag. The limiting magnitudes from the full set of gwrizy (average absolute magnitude ≈ −8 mag) data are consistent with previous preexplosion studies. We use deep photometry from the literature to constrain the progenitor of SN 2023ixf, finding that these data are consistent with a dusty red supergiant progenitor with luminosity logL/L⊙ ≈ 5.12 and temperature ≈ 3950 K, corresponding to a mass of 14–20 M ⊙ .
A bright ($m_{\rm F150W,AB}$=24 mag), $z=1.95$ supernova (SN) candidate was discovered in JWST/NIRCam imaging acquired on 2023 November 17. The SN is quintuply-imaged as a result of strong gravitational lensing by a foreground galaxy cluster, detected in three locations, and remarkably is the second lensed SN found in the same host galaxy. The previous lensed SN was called "Requiem", and therefore the new SN is named "Encore". This makes the MACS J0138.0$-$2155 cluster the first known system to produce more than one multiply-imaged SN. Moreover, both SN Requiem and SN Encore are Type Ia SNe (SNe Ia), making this the most distant case of a galaxy hosting two SNe Ia. Using parametric host fitting, we determine the probability of detecting two SNe Ia in this host galaxy over a $\sim10$ year window to be $\approx3\%$. These observations have the potential to yield a Hubble Constant ($H_0$) measurement with $\sim10\%$ precision, only the third lensed SN capable of such a result, using the three visible images of the SN. Both SN Requiem and SN Encore have a fourth image that is expected to appear within a few years of $\sim2030$, providing an unprecedented baseline for time-delay cosmography.
