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Context.Type Ia supernovae (SNe Ia) are a key probe in modern cosmology, as they can be used to measure luminosity distances at gigaparsec scales. Models of their light curves are used to project heterogeneous observed data onto a common basis for analysis. Aims.The SALT model currently used for SN Ia cosmology describes SNe as having two sources of variability, accounted for by a color parameterc, and a “stretch” parameterx₁. We extend the model to include an additional parameter we labelx₂, to investigate the cosmological impact of currently unaddressed light-curve variability. Methods.We constructed a new SALT model, that we dub “SALT3+”. This model was trained by an improved version of theSALTshakercode, using training data combining a selection of the second data release of cosmological SNe Ia from the Zwicky Transient Facility and the existing SALT3 training compilation. Results.We find additional, coherent variability in supernova light curves beyond SALT3. Most of this variation can be described as phase-dependent variation ing − randr − icolor curves, correlated with a boost in the height of the secondary maximum ini-band. These behaviors correlate with spectral differences, particularly in line velocity. We find that fits with the existing SALT3 model tend to address this excess variation with the color parameter, leading to less informative measurements of supernova color. We find that neglecting the new parameter in light-curve fits leads to a trend in Hubble residuals withx₂of 0.039 ± 0.005 mag, representing a potential systematic uncertainty. However, we find no evidence of a bias in current cosmological measurements. Conclusions.We conclude that extended SN Ia light-curve models promise mild improvement in the accuracy of color measurements, and corresponding cosmological precision. However, models with more parameters are unlikely to substantially affect current cosmological results. 

Context. With a rapidly rising number of transients detected in astronomy, classification methods based on machine learning are increasingly being employed. Their goals are typically to obtain a definitive classification of transients, and for good performance they usually require the presence of a large set of observations. However, well-designed, targeted models can reach their classification goals with fewer computing resources. Aims. The aim of this study is to assist in the observational astronomy task of deciding whether a newly detected transient warrants follow-up observations. Methods. This paper presents SNGuess, a model designed to find young extragalactic nearby transients with high purity. SNGuess works with a set of features that can be efficiently calculated from astronomical alert data. Some of these features are static and associated with the alert metadata, while others must be calculated from the photometric observations contained in the alert. Most of the features are simple enough to be obtained or to be calculated already at the early stages in the lifetime of a transient after its detection. We calculate these features for a set of labeled public alert data obtained over a time span of 15 months from the Zwicky Transient Facility (ZTF). The core model of SNGuess consists of an ensemble of decision trees, which are trained via gradient boosting. Results. Approximately 88% of the candidates suggested by SNGuess from a set of alerts from ZTF spanning from April 2020 to August 2021 were found to be true relevant supernovae (SNe). For alerts with bright detections, this number ranges between 92% and 98%. Since April 2020, transients identified by SNGuess as potential young SNe in the ZTF alert stream are being published to the Transient Name Server (TNS) under the AMPEL_ZTF_NEW group identifier. SNGuess scores for any transient observed by ZTF can be accessed via a web service https://ampel.zeuthen.desy.de/api/live/docs . The source code of SNGuess is publicly available https://github.com/nmiranda/SNGuess . Conclusions. SNGuess is a lightweight, portable, and easily re-trainable model that can effectively suggest transients for follow-up. These properties make it a useful tool for optimizing follow-up observation strategies and for assisting humans in the process of selecting candidate transients. 

ABSTRACT Samples of young Type Ia supernovae have shown ‘early excess’ emission in a few cases. Similar excesses are predicted by some explosion and progenitor scenarios and hence can provide important clues regarding the origin of thermonuclear supernovae. They are, however, only predicted to last up to the first few days following explosion. It is therefore unclear whether such scenarios are intrinsically rare or whether the relatively small sample size simply reflects the difficulty in obtaining sufficiently early detections. To that end, we perform toy simulations covering a range of survey depths and cadences, and investigate the efficiency with which young Type Ia supernovae are recovered. As input for our simulations, we use models that broadly cover the range of predicted luminosities. Based on our simulations, we find that in a typical 3 d cadence survey, only ∼10 per cent of Type Ia supernovae would be detected early enough to rule out the presence of an excess. A 2 d cadence, however, should see this increase to ∼15 per cent. We find comparable results from more detailed simulations of the Zwicky Transient Facility surveys. Using the recovery efficiencies from these detailed simulations, we investigate the number of young Type Ia supernovae expected to be discovered assuming some fraction of the population comes from scenarios producing an excess at early times. Comparing the results of our simulations to observations, we find that the intrinsic fraction of Type Ia supernovae with early flux excesses is $$\sim 28^{+13}_{-11}{{\ \rm per\ cent}}$$. 

Abstract We construct a physically parameterized probabilistic autoencoder (PAE) to learn the intrinsic diversity of Type Ia supernovae (SNe Ia) from a sparse set of spectral time series. The PAE is a two-stage generative model, composed of an autoencoder that is interpreted probabilistically after training using a normalizing flow. We demonstrate that the PAE learns a low-dimensional latent space that captures the nonlinear range of features that exists within the population and can accurately model the spectral evolution of SNe Ia across the full range of wavelength and observation times directly from the data. By introducing a correlation penalty term and multistage training setup alongside our physically parameterized network, we show that intrinsic and extrinsic modes of variability can be separated during training, removing the need for the additional models to perform magnitude standardization. We then use our PAE in a number of downstream tasks on SNe Ia for increasingly precise cosmological analyses, including the automatic detection of SN outliers, the generation of samples consistent with the data distribution, and solving the inverse problem in the presence of noisy and incomplete data to constrain cosmological distance measurements. We find that the optimal number of intrinsic model parameters appears to be three, in line with previous studies, and show that we can standardize our test sample of SNe Ia with an rms of 0.091 ± 0.010 mag, which corresponds to 0.074 ± 0.010 mag if peculiar velocity contributions are removed. Trained models and codes are released at https://github.com/georgestein/suPAErnova. 

Abstract We apply the color–magnitude intercept calibration method (CMAGIC) to the Nearby Supernova Factory SNe Ia spectrophotometric data set. The currently existing CMAGIC parameters are the slope and intercept of a straight line fit to the linear region in the color–magnitude diagram, which occurs over a span of approximately 30 days after maximum brightness. We define a new parameter,ω_XY, the size of the “bump” feature near maximum brightness for arbitrary filtersXandY. We find a significant correlation between the slope of the linear region,β_XY, in the CMAGIC diagram andω_XY. These results may be used to our advantage, as they are less affected by extinction than parameters defined as a function of time. Additionally,ω_XYis computed independently of templates. We find that current empirical templates are successful at reproducing the features described in this work, particularly SALT3, which correctly exhibits the negative correlation between slope and “bump” size seen in our data. In 1D simulations, we show that the correlation between the size of the “bump” feature andβ_XYcan be understood as a result of chemical mixing due to large-scale Rayleigh–Taylor instabilities. 

Abstract We calibrate spectrophotometric optical spectra of 32 stars commonly used as standard stars, referenced to 14 stars already on the Hubble Space Telescope–based CALSPEC flux system. Observations of CALSPEC and non-CALSPEC stars were obtained with the SuperNova Integral Field Spectrograph over the wavelength range 3300–9400 Å as calibration for the Nearby Supernova Factory cosmology experiment. In total, this analysis used 4289 standard-star spectra taken on photometric nights. As a modern cosmology analysis, all presubmission methodological decisions were made with the flux scale and external comparison results blinded. The large number of spectra per star allows us to treat the wavelength-by-wavelength calibration for all nights simultaneously with a Bayesian hierarchical model, thereby enabling a consistent treatment of the Type Ia supernova cosmology analysis and the calibration on which it critically relies. We determine the typical per-observation repeatability (median 14 mmag for exposures ≳5 s), the Maunakea atmospheric transmission distribution (median dispersion of 7 mmag with uncertainty 1 mmag), and the scatter internal to our CALSPEC reference stars (median of 8 mmag). We also check our standards against literature filter photometry, finding generally good agreement over the full 12 mag range. Overall, the mean of our system is calibrated to the mean of CALSPEC at the level of ∼3 mmag. With our large number of observations, careful cross-checks, and 14 reference stars, our results are the best calibration yet achieved with an integral-field spectrograph, and among the best calibrated surveys. 

In order to identify the sources of the observed diffuse high-energy neutrino flux, it is crucial to discover their electromagnetic counterparts. To increase the sensitivity of detecting counterparts of transient or variable sources by telescopes with a limited field of view, IceCube began releasing alerts for single high-energy ( E ν  >  60 TeV) neutrino detections with sky localisation regions of order 1° radius in 2016. We used Pan-STARRS1 to follow-up five of these alerts during 2016–2017 to search for any optical transients that may be related to the neutrinos. Typically 10–20 faint ( m i P1  ≲ 22.5 mag) extragalactic transients are found within the Pan-STARRS1 footprints and are generally consistent with being unrelated field supernovae (SNe) and AGN. We looked for unusual properties of the detected transients, such as temporal coincidence of explosion epoch with the IceCube timestamp, or other peculiar light curve and physical properties. We found only one transient that had properties worthy of a specific follow-up. In the Pan-STARRS1 imaging for IceCube-160427A (probability to be of astrophysical origin of ∼50%), we found a SN PS16cgx, located at 10.0′ from the nominal IceCube direction. Spectroscopic observations of PS16cgx showed that it was an H-poor SN at redshift z  = 0.2895 ± 0.0001. The spectra and light curve resemble some high-energy Type Ic SNe, raising the possibility of a jet driven SN with an explosion epoch temporally coincident with the neutrino detection. However, distinguishing Type Ia and Type Ic SNe at this redshift is notoriously difficult. Based on all available data we conclude that the transient is more likely to be a Type Ia with relatively weak Si  II absorption and a fairly normal rest-frame r -band light curve. If, as predicted, there is no high-energy neutrino emission from Type Ia SNe, then PS16cgx must be a random coincidence, and unrelated to the IceCube-160427A. We find no other plausible optical transient for any of the five IceCube events observed down to a 5 σ limiting magnitude of m i P1  ≈ 22 mag, between 1 day and 25 days after detection.