Lifan Wang

We investigate the thermal emission and the extinction from dust associated with the nearby ( z = 0.0267) superluminous supernova (SLSN) 2018bsz. Our dataset combines daily cadence, simultaneous optical and near-infrared (NIR) coverage up to ∼100 days with late-time (+ 1.7 yr) mid-infrared (MIR) observations. At 230 days after the light-curve peak, the supernova (SN) is not detected in the optical, but shows a surprisingly strong NIR excess, with r − J > 3 mag and r − K _s > 5 mag. The temporal evolution of the infrared (IR) light curve allows us to assess whether the MIR emission originates from newly formed dust, either in the SN ejecta or in a postshock circumstellar shell, or instead from an IR echo produced by preexisting circumstellar or interstellar dust heated by the SN radiation. Our radiative-transfer modeling shows that the circumstellar and interstellar echo scenarios cannot simultaneously reproduce the observed IR luminosity and its temporal evolution, and we therefore disfavor these interpretations. In contrast, a scenario in which new dust forms in the SN ejecta at epochs ≳200 days provides a self-consistent explanation for the evolution of the SN flux. We can fit the spectral energy distribution well at +230 days with 5 × 10 ⁻⁴ M _⊙ of carbon dust, increasing over the following several hundred days to 10 ⁻² M _⊙ by +535 days. SN 2018bsz is the first SLSN showing evidence for dust formation within the SN ejecta, and appears to form 10 times more dust than normal core-collapse supernovae (SNe) at similar epochs. Together with their preference for low-mass, low-metallicity host galaxies, we suggest that superluminous SNe may be a significant contributor to dust formation in the early Universe.

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.

We present SEDONA-GesaRaT, a rapid code for supernova radiative transfer simulation developed based on the Monte Carlo radiative transfer code SEDONA. We use an atomic physics neural network, which has a convolutional neural network structure, to solve for the nonlocal thermodynamic equilibrium (NLTE) atomic physics level population calculation. SEDONA-GesaRaT is trained and validated on 119 1D type Ia supernova (SN Ia) radiative transfer simulation results. It has been applied to the 3D SN Ia explosion model N100 to perform a 3D NLTE radiative transfer calculation. The spatially resolved linear polarization data cubes of the N100 model are successfully retrieved with a high signal-to-noise ratio using the integral-based technique. The 3D LTE and NLTE radiative transfer calculations have been validated by comparisons to SEDONA program simulation results. The typical computation cost of a new 3D NLTE spectropolarimetry simulation using SEDONA-GesaRaT is only ∼3000 core-hours, which will be a factor of ∼100 faster relative to a SEDONA simulation. The large computational speed-up factor makes SEDONA-GesaRaT promising for future large-scale simulations that systematically study the internal structures of SNe. However, the ultimate advantages of SEDONA-GesaRaT will depend on the required computational cost of retraining to accommodate improvements in physical realism in radiative transfer and a wider range of SN models.

We present a GPU-accelerated transient detection pipeline developed for time-domain surveys with the Dark Energy Camera (DECam). It enables real-time-capable image processing, incorporating science-driven candidate filtering to support rapid transient identification in time-critical observing programs. The pipeline serves as the core transient discovery engine for multiple long-term DECam programs, including the GW-MMADS gravitational-wave follow-up campaign and the DESIRT survey for intermediate-redshift transients with DESI synergy. The pipeline ingests calibrated imaging products from the DECam Community Pipeline and performs image differencing using SFFT, a GPU-accelerated Fourier-domain image-subtraction method, coupled with convolutional neural network-based real–bogus classification, to produce science-ready transient alerts and light curves that are delivered to community brokers. We validate the pipeline using archival DECam data from the DESIRT survey. Our AI-based real–bogus classifier achieves a completeness of ∼99% of real transients while rejecting ∼96% of subtraction artifacts, and the workflow typically reduces the candidate load to a manageable level for survey operations. With GPU acceleration, the typical processing time per DECam exposure is ∼50 s from calibrated image processing to alert generation using a modest allocation of computing resources.

Type Ia supernovae (SNe Ia) are key tools for addressing major cosmological questions like the Hubble tension and dark energy. Modern surveys are predominantly photometry based, making photometric classification of SNe Ia essential for precision cosmology. We evaluate whether functional principal component analysis (FPCA) scores derived from light curves, combined with ensemble learning, can reliably distinguish SNe Ia from other transients using a subset of PLAsTiCC sources with well-sampled light curves in a fully photometric approach. FPCA offers a data-driven, flexible characterization without strict model-based assumptions. Light curves are fitted by minimizing residuals with penalty terms from clean samples, ensuring robustness to outliers and poor band sampling. The first two FPCA scores and peak magnitudes in five Legacy Survey of Space and Time (LSST) bands serve as classification features. We implement two binary classifiers: an ensemble boosting model (CatBoost) and a statistical probabilistic method based on Euclidean distances. CatBoost slightly outperforms the statistical method, achieving 96.5% accuracy and 96.6% precision. Both accuracy and precision remain satisfactory (>90%) for photometric redshift uncertainties up to σ = 0.05. On the spectroscopic DES Y5 sample, both methods maintain ∼90% accuracy and 95% precision, demonstrating excellent cross-survey generalization. Applied to DECam DDF and DESIRT transients, predictions strongly agree, and their intersection yields a high-confidence SN Ia sample for cosmological analyses. This FPCA-based framework provides a powerful, flexible tool for transient classification in upcoming surveys such as LSST and Roman.

We present multiband photometric and spectroscopic observations of supernova (SN) 2024abvb, which exhibits early-time prominent photoionized narrow emission lines of C II superposed on a blue continuum. The absence of Balmer features indicates that the SN exploded within hydrogen-poor circumstellar matter (CSM). Together with the lack of explicit evidence of helium signatures, we tentatively identify SN 2024abvb as a Type Icn SN (SN Icn). After correcting for extinction, we estimate an r -band peak absolute magnitude of −19.7 ± 0.1, placing SN 2024abvb in the luminous regime of SNe Icn. We adopted a hybrid model that accounts for both the energy released by the ejecta-CSM interaction and the radioactive decay of nickel synthesized in the SN ejecta to fit the light curve of SN 2024abvb. The best-fit model to the multiband light curves within the first ∼40 days after explosion suggests the CSM, radioactive nickel, and ejecta masses to be 0.2 8 − 0.03 + 0.02 M ⊙ , ≤ 3.8 × 10 ⁻² M _⊙ , and 0.1 2 − 0.02 + 0.06 M ⊙ , respectively. Such a low ejecta mass indicates that the progenitor star of SN 2024abvb experienced a significant mass-stripping process, consistent with the hydrogen-poor and helium-poor spectral features. SN 2024abvb provides important insights into the physical origins of the rare subclass of SNe Icn.

Training physics-informed neural networks (PINNs) on stiff, time-dependent PDEs remains a fundamental challenge due to optimization instabilities and gradient pathologies. Through a series of rigorous ablation studies and Neural Tangent Kernel (NTK) analysis, we identify that the exact enforcement of initial conditions (ICs) is a decisive factor in stabilizing the training landscape. We present the first systematic ablation of two core strategies: hard initial-condition constrained transformation and self-adaptive loss weighting. Our findings demonstrate that embedding ICs directly into the network architecture provides an implicit time-marching effect, effectively reducing spectral bias and enabling the solution of highly stiff benchmarks, including sharp transitions and high-frequency coupled systems. This work provides a scalable framework for developing reliable and physically-consistent neural solvers for complex mechanical systems.

We present complete spectropolarimetric coverage of the Type II supernova (SN) 2023ixf, ranging from 1 to 120 days after explosion. Polarimetry was obtained with the Kast double spectrograph on the Shane 3 m telescope at Lick Observatory. As the ejecta interact with circumstellar material (CSM) during the first week, the intrinsic polarization of SN 2023ixf is initially high at ≲1%, dropping steeply within days down to ∼0.4% when the ejecta sweep up the optically thick CSM. The continuum polarization stays low at ∼0.2% thereafter, until it rises again to ∼0.6% as the ejecta transition to the nebular phase. We model this evolution using a combination of archival and newly computed two-dimensional polarized radiative transfer models. In this context, we interpret the early-time polarization as arising from an aspherical CSM with a pole-to-equator density contrast of ≳3. We propose that the surge in polarization at late times originates from an asymmetric distribution of ⁵⁶ Ni deep in the ejecta. The distinct sources of asymmetries at early and late times are consistent with the temporal evolution of the observed polarization and the polarization angle in SN 2023ixf.

We present 21 transients from our three-epoch, four-band NIRCam observations covering 14.16 arcmin ² in the Spitzer IRAC Dark Field (IDF), taken by the JWST Prime Extragalactic Areas for Reionization and Lensing Science program with a time cadence of ∼6 months. A separate Hubble Space Telescope program provided Advanced Camera for Surveys optical imaging contemporaneous with the second and third epochs of the NIRCam observations. The NIRSpec spectroscopy on three transients confirmed a Type Ia supernova at z = 1.63 and the host galaxies of the other two at z = 2.64 and 1.90, respectively. Combining these with the photometric redshifts ( z _ph ) of the host galaxies in the rest of the sample, we find that the transients are in either a “mid- z ” group at z > 1.6 with M _V ≲ − 16.0 mag or a “low- z ” group at z < 0.4 with M _H ≳ − 14.0 mag. The mid- z transients are consistent with supernovae. In contrast, the low- z transients’ luminosities fall in the range of the so-called “gap transients” between supernovae and novae. However, this latter conclusion is only tentative due to possible catastrophic failures in z _ph that could bias them to low- z . Conversely, if they are indeed at z < 0.4, it would be worth studying similar transients in the future. Our work further demonstrates the power of NIRCam in transient science and also shows that it would be more fruitful to carry out a long-term monitoring program with more passbands, a higher cadence, and prompt follow-up spectroscopy. Being in the continuous viewing zone of the JWST, the IDF is an ideal field for this purpose.

Training physics-informed neural networks (PINNs) on stiff, time-dependent PDEs remains a fundamental challenge due to optimization instabilities and gradient pathologies. Through a series of rigorous ablation studies and Neural Tangent Kernel (NTK) analysis, we identify that the exact enforcement of initial conditions (ICs) is a decisive factor in stabilizing the training landscape. We present the first systematic ablation of two core strategies: hard initial-condition constrained transformation and self-adaptive loss weighting. Our findings demonstrate that embedding ICs directly into the network architecture provides an implicit time-marching effect, effectively reducing spectral bias and enabling the solution of highly stiff benchmarks, including sharp transitions and high-frequency coupled systems, primarily under periodic boundary conditions, with a Dirichlet extension reported as an additional robustness check. This work provides a scalable framework for developing reliable and physically-consistent neural solvers for complex mechanical systems.