Wolfgang E. Kerzendorf

Type Ic broad-line supernovae (SNe Ic-BL) are often associated with energetic explosions that display a prompt outburst of high-energy emission. Since their progenitor lost the H and He envelopes before the explosion exposing the C/O core, their explosion dynamics and geometry can be seen in an unobscured and undistorted way. We present imaging polarimetry and spectropolarimetry of the Type Ic-BL SN 2026gzf obtained 4.6 and 16.5 days after the X-ray shock breakout, which was recorded by the Einstein Probe satellite as EP260321a, showing it to be one of the softest and intrinsically dimmest extragalactic fast X-ray transients. The persistent low continuum polarization indicates that the outer layer of SN 2026gzf is mostly spherical, suggesting the explosion did not significantly disrupt the progenitor envelope. At day 16.5, the calcium near-infrared triplet displays a peak polarization above 1.5%. The geometry of the associated line opacity is also compatible with an axisymmetric configuration. The spatial distribution of such oxygen-burning ashes thus indicates the presence of a symmetry axis of the excitation structure within the nearly spherical ejecta. The Ca , II triplet polarization is dominated by a primary component spanning ∼25,000–40,000 km s ⁻¹ , while a possible high-velocity polarization substructure of modest statistical significance may indicate a complex, nonaxisymmetric excitation geometry in the outer ejecta. By implementing a three-dimensional Monte Carlo calculation, we infer that a viewing angle of ∼40 ^∘ from the symmetry axis of the excitation structure could plausibly reproduce the observed spectral and polarization profiles of the Ca II triplet.

The absence of helium features in optical spectra is one of the classification criteria for Type Ic supernovae (SNe Ic). However, it is highly debated whether helium is truly absent in ejecta or spectroscopically undetectable in the optical region. The near-infrared (NIR) region contains cleaner He lines that are less blended with other common ions in SN Ic ejecta. We perform full spectral modeling on the near-peak-light optical and NIR spectra of the SN Ic 2014L to quantitatively constrain helium and other outer-ejecta properties, using the radiative transfer code tardis . We employ a deep learning emulator for SN Ic spectra that serves as a fast surrogate for tardis simulations. We then integrate the emulator within the Bayesian inference framework to infer the ejecta properties. The emulator achieves a mean fractional error of 1% between the emulated and tardis fluxes across all wavelengths and all samples in the test dataset. We constrain 0.018–0.020 M _⊙ (16%–84% posterior percentile) of He above the photosphere near peak light in SN 2014L, inferred from the observed spectra covering 3500–24000 Å. A Bayesian statistical test shows that the observed spectra are inconsistent with no helium. Furthermore, the posterior favors a power-law density exponent of −7.04 to −6.88 (16%–84% credible interval), consistent with theoretical calculations of radiation-dominated explosions. This work demonstrates that Bayesian radiative-transfer inference over a wide wavelength range provides a powerful path toward systematic constraints on He in SNe Ic.

The ability to distinctly and properly collate an individual researcher’s publications is crucial for ensuring appropriate recognition, guiding the allocation of research funding, and informing hiring decisions. However, accurately grouping and linking a researcher’s entire body of work with their individual identity is challenging because of widespread name ambiguity across the growing literature. Algorithmic author name disambiguation (AND) provides a scalable approach to disambiguating author identities, yet existing methods have limitations. Many modern AND methods rely on comprehensive metadata features such as email, venue, or affiliation. Despite advancements in digitally indexing publications, metadata is often unavailable or inconsistent in large digital libraries (e.g., SAO/NASA Astrophysics Data System). We introduce the Neural Author Name Disambiguator (NAND), a method that effectively disambiguates author identities in large digital libraries despite limited metadata availability. We formulate the AND task as a similarity learning problem by employing a Siamese neural network to disambiguate author names across publications relying solely on widely available publication metadata—author names, titles, and abstracts. We construct the Large-Scale Physics Open Researcher and Contributor ID (ORCID)-Linked dataset to evaluate the NAND method by cross-matching NASA/ADS publications ORCiD. By leveraging foundation models to embed metadata into features, the NAND achieves up to 94% accuracy in pairwise disambiguation and over 95% F1 in clustering publications into their correct researcher identities. We release the testing dataset as a benchmark for physics and astronomy, providing realistic evaluation conditions for future disambiguation methods. The NAND algorithm demonstrates effective disambiguation with minimal metadata, offering a scalable solution for name ambiguity in large-scale digital libraries.

Type Ia supernovae (SNe Ia) are powered by the radioactive decay of isotopes such as ⁵⁶Ni and ⁵⁶Co, making their γ-ray spectra useful probes of the explosion mechanism and ejecta structure. Accurate interpretation of γ-ray observables, including line ratios and continuum fluxes, requires a detailed understanding of the microphysical processes that shape the spectra. One such process is positronium formation during electron–positron annihilation, which can redistribute flux from the 511 keV line into the surrounding continuum. To assess the impact of positronium on the emergent spectra, we developed a new open-source module, tardis-He, for time-dependent three-dimensional γ-ray transport, integrated into the radiative transfer code tardis. The code simulates γ-ray spectra and light curves from one-dimensional supernova ejecta models and allows for flexible incorporation of decay chains and opacity treatments. Using tardis-He, we explore the effect of positronium formation by varying the positronium fraction from 0% to 100%, and assuming an extreme case where 75% of positronium decays result in three-photon emission. We find that full positronium formation can reduce the 511 keV line flux by ≈70% and modestly enhance energy deposition by up to 2% at around 100 days postexplosion, compared to models without positronium. These results demonstrate that, while the effect is not dominant, positronium formation introduces measurable changes to γ-ray observables. Future observations with missions such as the Compton Spectrometer and Imager may offer constraints on positronium formation in SNe Ia and help refine models of their radioactive energy transport.

We introduce a new 1D stellar spectral synthesis Python code called stardis. stardis is a modular, open-source radiative transfer code that is capable of spectral synthesis from near-UV to IR for FGK stars. We describe the structure, inputs, features, underlying physics, and assumptions of stardis, as well as the radiative transfer scheme implemented. To validate our code, we show spectral comparisons between stardis and korg with the same input atmospheric structure models, and we also compare qualitatively to phoenix for solar models. We find that stardis generally agrees with korg for solar models on the few-percent level or better, though the codes can diverge in the UV, with more extreme differences in cooler stars. stardis can be found at https://github.com/tardis-sn/stardis, and documentation can be found at https://tardis-sn.github.io/stardis/.

One of the main challenges in modeling massive stars to the onset of core collapse is the computational bottleneck of nucleosynthesis during advanced burning stages. The number of isotopes formed requires solving a large set of fully coupled stiff ordinary differential equations, making the simulations computationally intensive and prone to numerical instability. To overcome this barrier, we design a nuclear neural network (NNN) framework with multiple hidden layers to emulate nucleosynthesis calculations and conduct a proof of concept to evaluate its performance. The NNN takes the temperature, density, and composition of a burning region as input and predicts the resulting isotopic abundances along with the energy generation and loss rates. We generate training sets for initial conditions corresponding to oxygen core depletion and beyond using large nuclear reaction networks, and compare the predictions of the NNNs to results from a commonly used small net. We find that the NNNs improve the accuracy of the electron fraction by 280%–660%, the average atomic and mass numbers by 150%–360%, and the nuclear energy generation by 250%–750%, consistently outperforming the small network across all time steps. They also achieve significantly better predictions of neutrino losses on relatively short timescales, with improvements ranging from 100% to 1,000,000%. While further work is needed to enhance their accuracy and applicability to different stellar conditions, integrating NNN-trained models into stellar evolution codes is promising for facilitating the large-scale generation of core-collapse supernova progenitors with higher physical fidelity.

Stripped-envelope supernovae (SESNe) represent a significant fraction of core-collapse supernovae, arising from massive stars that have shed their hydrogen and, in some cases, helium envelopes. The origins and explosion mechanisms of SESNe remain a topic of active investigation. In this work, we employ radiative-transfer simulations to model the light curves and spectra of a set of explosions of single, solar-metallicity, massive Wolf–Rayet stars with ejecta masses ranging from 4 to 11 M _⊙, which were computed from a turbulence-aided and neutrino-driven explosion mechanism. We analyze these synthetic observables to explore the impact of varying ejecta mass and helium content on observable features. We find that the light curve shape of these progenitors with high ejecta masses is consistent with observed SESNe with broad light curves but not the peak luminosities. The commonly used analytic formula based on rising bolometric light curves overestimates the ejecta mass of these high-initial-mass progenitor explosions by a factor of up to 2.6. In contrast, the calibrated method by Haynie et al., which relies on late-time decay tails, reduces uncertainties to an average of 20% within the calibrated ejecta mass range. Spectroscopically, the He i 1.083 μm line remains prominent even in models with as little as 0.02 M _⊙ of helium. However, the strength of the optical He i lines is not directly proportional to the helium mass but instead depends on a complex interplay of factors such as the ⁵⁶Ni distribution, composition, and radiation field. Thus, producing realistic helium features requires detailed radiative transfer simulations for each new hydrodynamic model.

Large scientific institutions, such as the Space Telescope Science Institute, track the usage of their facilities to understand the needs of the research community. Astrophysicists incorporate facility usage data into their scientific publications, embedding this information in plain text. Traditional automatic search queries prove unreliable for accurate tracking due to the misidentification of facility names in plain text. As automatic search queries fail, researchers are required to manually classify publications for facility usage, which consumes valuable research time. In this work, we introduce a machine learning classification framework for the automatic identification of facility usage of observation sections in astrophysics publications. Our framework identifies sentences containing telescope mission keywords (e.g., Kepler and TESS) in each publication. Subsequently, the identified sentences are transformed using term frequency–inverse document frequency and classified with a support vector machine. The classification framework leverages the context surrounding the identified telescope mission keywords to provide relevant information to the classifier. The framework successfully classifies the usage of MAST-hosted missions with a 92.9% accuracy. Furthermore, our framework demonstrates robustness when compared to other approaches, considering common metrics and computational complexity. The framework’s interpretability makes it adaptable for use across observatories and other scientific facilities worldwide.

Large scientific institutions, such as the Space Telescope Science Institute, track the usage of their facilities to understand the needs of the research community. Astrophysicists incorporate facility usage data into their scientific publications, embedding this information in plain-text. Traditional automatic search queries prove unreliable for accurate tracking due to the misidentification of facility names in plain-text. As automatic search queries fail, researchers are required to manually classify publications for facility usage, which consumes valuable research time. In this work, we introduce a machine learning classification framework for the automatic identification of facility usage of observation sections in astrophysics publications. Our framework identifies sentences containing telescope mission keywords (e.g., Kepler and TESS) in each publication. Subsequently, the identified sentences are transformed using Term Frequency-Inverse Document Frequency and classified with a Support Vector Machine. The classification framework leverages the context surrounding the identified telescope mission keywords to provide relevant information to the classifier. The framework successfully classifies usage of MAST hosted missions with a 92.9% accuracy. Furthermore, our framework demonstrates robustness when compared to other approaches, considering common metrics and computational complexity. The framework's interpretability makes it adaptable for use across observatories and other scientific facilities worldwide.

We present an analysis of ground-based and JWST observations of SN 2022pul, a peculiar “03fg-like” (or “super-Chandrasekhar”) Type Ia supernova (SN Ia), in the nebular phase at 338 days postexplosion. Our combined spectrum continuously covers 0.4–14μm and includes the first mid-infrared spectrum of a 03fg-like SN Ia. Compared to normal SN Ia 2021aefx, SN 2022pul exhibits a lower mean ionization state, asymmetric emission-line profiles, stronger emission from the intermediate-mass elements (IMEs) argon and calcium, weaker emission from iron-group elements (IGEs), and the first unambiguous detection of neon in a SN Ia. A strong, broad, centrally peaked [Neii] line at 12.81μm was previously predicted as a hallmark of “violent merger” SN Ia models, where dynamical interaction between two sub-M_Chwhite dwarfs (WDs) causes disruption of the lower-mass WD and detonation of the other. The violent merger scenario was already a leading hypothesis for 03fg-like SNe Ia; in SN 2022pul it can explain the large-scale ejecta asymmetries seen between the IMEs and IGEs and the central location of narrow oxygen and broad neon. We modify extant models to add clumping of the ejecta to reproduce the optical iron emission better, and add mass in the innermost region (−1) to account for the observed narrow [Oi]λλ6300, 6364 emission. A violent WD–WD merger explains many of the observations of SN 2022pul, and our results favor this model interpretation for the subclass of 03fg-like SNe Ia.