Astrophysical Transients Research Articles

A Nançay Radio Telescope study of the hyperactive repeating FRB 20220912A

Abstract The repeating fast radio burst source FRB 20220912A was remarkably active in the weeks after its discovery. Here we report 696 bursts detected with the Nançay Radio Telescope (NRT) as part of the Extragalactic Coherent Light from Astrophysical Transients (ÉCLAT) monitoring campaign. We present 68 observations, conducted from October 2022 to April 2023, with a total duration of 61 hours and an event rate peaking at $75^{+10}_{-9}$ bursts per hour above a fluence threshold of 0.59 Jy ms in the 1.2 − 1.7-GHz band. Most bursts in the sample occur towards the bottom of the observing band. They follow a bimodal wait-time distribution, with peaks at 33.4 ms and 67.0 s. We find a roughly constant dispersion measure (DM) over time (δDM ≲ 2 pc cm−3) when taking into account ‘sad-trombone’ drift, with a mean drift rate of −8.8 MHz ms−1. Nonetheless, we confirm small ∼0.3 pc cm−3 DM variations using microshot structure, while finding that microstructure is rare in our sample – despite the 16 μs time resolution of the data. The cumulative spectral energy distribution shows more high-energy bursts (Eν ≳ 1031 erg Hz−1) than would be expected from a simple power-law distribution. The burst rate per observation appears Poissonian, but the full set of observations is better modelled by a Weibull distribution, showing clustering. We discuss the various observational similarities that FRB 20220912A shares with other (hyper)active repeaters, which as a group are beginning to show a common set of phenomenological traits that provide multiple useful dimensions for their quantitative comparison and modelling.

DECam Multimessenger Astrophysics Pipeline. I. From Raw Data to Single-exposure Candidates

We introduce a pipeline that performs rapid image subtraction and source selection to detect transients, with a focus on identifying gravitational-wave optical counterparts using the Dark Energy Camera (DECam). In this work, we present the pipeline steps from processing raw data to identification of astrophysical transients on individual exposures. We process DECam data and build difference images using the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) Science Pipelines software, and we use flags and principal component analysis to select transients on a per-exposure basis, without associating the results from different exposures. Those candidates will be sent to brokers for further classification and alert distribution. We validate our pipeline using archival exposures that cover various types of objects, and the tested targets include a kilonova (GW170817), supernovae, stellar flares, variable stars (in a resolved galaxy or the Milky Way Bulge), and serendipitous objects. Overall, the data processing produces clean light curves that are comparable with published results, demonstrating the photometric quality of our pipeline. Real transients can be well selected by our pipeline when sufficiently bright (signal-to-noise ratio ≳15). This pipeline is intended to serve as a tool for the broader research community. Although this pipeline is designed for DECam, our method can be easily applied to other instruments and future LSST observations.

Anomaly Detection and Approximate Similarity Searches of Transients in Real-time Data Streams

We present Lightcurve Anomaly Identification and Similarity Search (LAISS), an automated pipeline to detect anomalous astrophysical transients in real-time data streams. We deploy our anomaly detection model on the nightly Zwicky Transient Facility (ZTF) Alert Stream via the ANTARES broker, identifying a manageable ∼1–5 candidates per night for expert vetting and coordinating follow-up observations. Our method leverages statistical light-curve and contextual host galaxy features within a random forest classifier, tagging transients of rare classes (spectroscopic anomalies), of uncommon host galaxy environments (contextual anomalies), and of peculiar or interaction-powered phenomena (behavioral anomalies). Moreover, we demonstrate the power of a low-latency (∼ms) approximate similarity search method to find transient analogs with similar light-curve evolution and host galaxy environments. We use analogs for data-driven discovery, characterization, (re)classification, and imputation in retrospective and real-time searches. To date, we have identified ∼50 previously known and previously missed rare transients from real-time and retrospective searches, including but not limited to superluminous supernovae (SLSNe), tidal disruption events, SNe IIn, SNe IIb, SNe I-CSM, SNe Ia-91bg-like, SNe Ib, SNe Ic, SNe Ic-BL, and M31 novae. Lastly, we report the discovery of 325 total transients, all observed between 2018 and 2021 and absent from public catalogs (∼1% of all ZTF Astronomical Transient reports to the Transient Name Server through 2021). These methods enable a systematic approach to finding the “needle in the haystack” in large-volume data streams. Because of its integration with the ANTARES broker, LAISS is built to detect exciting transients in Rubin data.

GWSkyNet. II. A Refined Machine-learning Pipeline for Real-time Classification of Public Gravitational Wave Alerts

Electromagnetic follow-up observations of gravitational wave events offer critical insights and provide significant scientific gain from this new class of astrophysical transients. Accurate identification of gravitational wave candidates and rapid release of sky localization information are crucial for the success of these electromagnetic follow-up observations. However, searches for gravitational wave candidates in real time suffer from a nonnegligible false alarm rate. By leveraging the sky localization information and other metadata associated with gravitational wave candidates, GWSkyNet, a machine-learning classifier developed by Cabero et al., demonstrated promising accuracy for the identification of the origin of event candidates. We improve the performance of the classifier for LIGO–Virgo–KAGRA's (LVK) fourth observing run by reviewing and updating the architecture and features used as inputs by the algorithm. We also retrain and fine-tune the classifier with data from the third observing run. To improve the prospect of electromagnetic follow-up observations, we incorporate GWSkyNet into LVK's low-latency infrastructure as an automatic pipeline for the evaluation of gravitational wave alerts in real time. We test the readiness of the algorithm on an LVK mock data challenge campaign. The results show that by thresholding on the GWSkyNet score, noise masquerading as astrophysical sources can be rejected efficiently and the majority of true astrophysical signals can be correctly identified.

Kilonova Spectral Inverse Modelling with Simulation-based Inference: An Amortized Neural Posterior Estimation Analysis

Kilonovae represent a category of astrophysical transients, identifiable as the electromagnetic (EM) counterparts associated with the coalescence events of binary systems comprising neutron stars and neutron star–black hole pairs. They act as probes for heavy-element nucleosynthesis in astrophysical environments. These studies rely on an inference of the physical parameters (e.g., ejecta mass, velocity, composition) that describe kilonovae-based on EM observations. This is a complex inverse problem typically addressed with sampling-based methods such as Markov Chain Monte Carlo or nested sampling algorithms. However, repeated inferences can be computationally expensive, due to the sequential nature of these methods. This poses a significant challenge to ensuring the reliability and statistical validity of the posterior approximations and, thus, the inferred kilonova parameters themselves. We present a novel approach: simulation-based inference using simulations produced by KilonovaNet. Our method employs an ensemble of amortized neural posterior estimation (ANPE) with an embedding network to directly predict posterior distributions from simulated spectral energy distributions. We take advantage of the quasi-instantaneous inference time of ANPE to demonstrate the reliability of our posterior approximations using diagnostics tools, including coverage diagnostic and posterior predictive checks. We further test our model with real observations from AT 2017gfo, the only kilonova with multimessenger data, demonstrating agreement with previous likelihood-based methods while reducing inference time down to a few seconds. The inference results produced by ANPE appear to be conservative and reliable, paving the way for testable and more efficient kilonova parameter inference.

Detecting the Early Optical Flashes of Gamma-Ray Bursts with Small Telescope Arrays

We present an observational approach for the independent detection of the early optical emission of long gamma-ray bursts (GRBs). For this purpose, we explore the potential of the Large Array Survey Telescope (LAST). This array of small optical telescopes can be used to scan a wide region of the sky, and to focus on a smaller field of view with increased sensitivity, as needed. The modularity of the array facilitates dynamic scanning of multiple fields, by shifting telescope pointing directions with high cadence. This can significantly increase the effective sky-coverage of a blind survey on short timescales. For events associated with gamma-ray counterparts, the valuable early time data can supplement high-energy observations. Regardless of gamma-ray association, detections can potentially be used to explore various phenomena associated with GRBs, such as orphan afterglows; dirty fireballs; and choked jets. We simulate a sample of GRBs and their respective optical signals at early times. After accounting for dynamic cadence, the light curves are given as input to a machine-learning classifier, used to identify astrophysical transients. We find that, by dedicating half of an LAST array to a blind search, one would expect to independently detect 7–11 GRBs yr–1, corresponding to an approximate intrinsic event rate of 0.12 deg–2 yr–1.

GLADEnet: A progressive web app for multi-messenger cosmology and electromagnetic follow-ups of gravitational-wave sources

Multi-messenger astronomy is an emerging field of research aimed at unravelling the physics governing astrophysical transients. GW170817 stands out as the first multi-messenger observation of the coalescence of a binary system of neutron stars, detected by the LIGO and Virgo gravitational-wave interferometers, along with space- and ground-based electromagnetic telescopes. It is a striking example of how multi-messenger observations significantly enhance our understanding of the physics of compact objects, relativistic outflows, and nucleosynthesis. It shows a new way of making cosmology and has the potential to resolve the tension between different measurements of the expansion rate of the Universe. To optimise multi-messenger observational strategies, to evaluate the efficiency of the searches for counterparts, and to identify the host galaxy of the source in a large sky localisation, information about the volumes of galaxies within the gravitational-wave localisation is of paramount importance. This requires the use of galaxy catalogues and appropriate knowledge of their completeness. Here, we describe a new interactive web tool named GLADEnet that allows us to identify catalogued galaxies and to assess the incompleteness of the catalogue of galaxies in real time across the gravitational-wave sky localisation. This measure is of particular importance when using catalogues such as the GLADE catalogue (Galaxy List for the Advanced Detector Era), which includes a collection of various catalogues that make completeness differ across different regions of the sky. We discuss the analysis steps to defining a completeness coefficient and provide a comprehensive guide on how to use the web app, detailing its functionalities. The app is geared towards managing the vast collection of over 22 million objects in GLADE. The completeness coefficient and the GLADE galaxy list will be disseminated in real time via GLADEnet, powered by the Virtual Observatory (VO) standard and tools.

A lanthanide-rich kilonova in the aftermath of a long gamma-ray burst.

Observationally, kilonovae are astrophysical transients powered by the radioactive decay of nuclei heavier than iron, thought tobe synthesized in the merger of two compact objects1-4. Over the first few days, the kilonova evolution is dominated by a large number of radioactive isotopes contributing to the heating rate2,5. On timescales of weeks to months, its behaviour is predicted to differ depending on the ejecta composition and the merger remnant6-8. Previous work has shown that the kilonova associated with gamma-ray burst 230307A is similar to kilonova AT2017gfo (ref. 9), and mid-infrared spectra revealed an emission line at 2.15 micrometres that was attributed to tellurium. Here we report a multi-wavelength analysis, including publicly available James Webb Space Telescope data9 and our own Hubble Space Telescope data, for the same gamma-ray burst. We model its evolution up to two months after the burst and show that, at these late times, the recession of the photospheric radius and the rapidly decaying bolometric luminosity (Lbol ∝ t-2.7±0.4, where t is time) support the recombination of lanthanide-rich ejecta as they cool.

The S-PLUS Transient Extension Program: imaging pipeline, transient identification, and survey optimization for multimessenger astronomy

ABSTRACT We present the S-PLUS Transient Extension Program (STEP): a supernova and fast transient survey conducted in the southern hemisphere using data from the Southern Photometric Local Universe Survey (S-PLUS) Main Survey and the T80-South telescope. Transient astrophysical phenomena have a range of interest that goes through different fields of astrophysics and cosmology. With the detection of an electromagnetic counterpart to the gravitational wave (GW) event GW170817 from a binary neutron stars merger, new techniques and resources to study fast astrophysical transients in the multimessenger context have increased. In this paper, we present the STEP overview, the SN follow-up data obtained, data reduction, analysis of new transients and deep learning algorithms to optimize transient candidate selection. Additionally, we present prospects and optimized strategy for the search of gravitational wave counterparts in the current LIGO/Virgo/Kagra observational run (O4) in the context of T80-South telescope.

An Automated Photometric Pipeline for the ILMT data

The International Liquid Mirror Telescope (ILMT) is a 4-meter survey telescope continuously observing towards the zenith in the SDSS g’, r’, and i’ bands. This survey telescope is designed to detect various astrophysical transients (for example, supernovae) and very faint objects like multiply-imaged quasars and low surface brightness galaxies. A single scan of a 22′ strip of sky contains a large amount of photometric information. To process this type of data, it becomes critical to have tools or pipelines that can handle it efficiently and accurately with minimal human biases. We offer a fully automated pipeline generated in Python to perform aperture photometry over the ILMT data acquired with the CCD in Time Delayed Integration (TDI) mode. The instrumental magnitudes are calibrated with respect to the Pan-STARRS-1 catalogue. The light curves generated from the calibrated magnitudes will allows us to characterize the objects as variable stars or rapidly decaying transients.

Keck Infrared Transient Survey. I. Survey Description and Data Release 1

We present the Keck Infrared Transient Survey, a NASA Key Strategic Mission Support program to obtain near-infrared (NIR) spectra of astrophysical transients of all types, and its first data release, consisting of 105 NIR spectra of 50 transients. Such a data set is essential as we enter a new era of IR astronomy with the James Webb Space Telescope (JWST) and the upcoming Nancy Grace Roman Space Telescope (Roman). NIR spectral templates will be essential to search JWST images for stellar explosions of the first stars and to plan an effective Roman SN Ia cosmology survey, both key science objectives for mission success. Between 2022 February and 2023 July, we systematically obtained 274 NIR spectra of 146 astronomical transients, representing a significant increase in the number of available NIR spectra in the literature. Here, we describe the first release of data from the 2022A semester. We systematically observed three samples: a flux-limited sample that includes all transients <17 mag in a red optical band (usually ZTF r or ATLAS o bands); a volume-limited sample including all transients within redshift z < 0.01 (D ≈ 50 Mpc); and an SN Ia sample targeting objects at phases and light-curve parameters that had scant existing NIR data in the literature. The flux-limited sample is 39% complete (60% excluding SNe Ia), while the volume-limited sample is 54% complete and is 79% complete to z = 0.005. Transient classes observed include common Type Ia and core-collapse supernovae, tidal disruption events, luminous red novae, and the newly categorized hydrogen-free/helium-poor interacting Type Icn supernovae. We describe our observing procedures and data reduction using PypeIt, which requires minimal human interaction to ensure reproducibility.

Kilonova-Targeting Lightcurve Classification for Wide Field Survey Telescope

With the enhancement of the sensitivity of gravitational wave (GW) detectors and capabilities of large survey facilities, such as the Vera Rubin Observatory Legacy Survey of Space and Time (LSST) and the 2.5 m Wide Field Survey Telescope (WFST), we now have the potential to detect an increasing number of distant kilonova (KN). However, distinguishing KN from the plethora of detected transients in ongoing and future follow-up surveys presents a significant challenge. In this study, our objective is to establish an efficient classification mechanism tailored for the follow-up survey conducted by WFST, with a specific focus on identifying KN associated with GW. We employ a novel temporal convolutional neural network architecture, trained using simulated multi-band photometry lasting for 3 days by WFST, accompanied by contextual information, i.e., luminosity distance information by GW. By comparison of the choices of contextual information, we can reach 95% precision and 94% recall for our best model. It also performs good validation of photometry data on AT2017gfo and AT2019npv. Furthermore, we investigate the ability of the model to distinguish KN in a GW follow-up survey. We conclude that there is over 80% probability that we can capture true KN in 20 selected candidates among ∼250 detected astrophysical transients that have passed the real–bogus filter and cross-matching.

Internal shocks hydrodynamics: the collision of two cold shells in detail

ABSTRACT Emission in many astrophysical transients originates from a shocked fluid. A central engine typically produces an outflow with varying speeds, leading to internal collisions within the outflow at finite distances from the source. Each such collision produces a pair of forward and reverse shocks with the two shocked regions separated by a contact discontinuity (CD). As a useful approximation, we consider the head-on collision between two cold and uniform shells (a slower leading shell and a faster trailing shell) of finite radial width, and study the dynamics of shock propagation in planar geometry. We find significant differences between the forward and reverse shocks, in terms of their strength, internal energy production efficiency, and the time it takes for the shocks to sweep through the respective shells. We consider the subsequent propagation of rarefaction waves in the shocked regions and explore the cases where these waves can catch up with the shock fronts and thereby limit the internal energy dissipation. We demonstrate the importance of energy transfer from the trailing to leading shell through pdV work across the CD. We outline the parameter space regions relevant for models of different transients,e.g. Gamma-ray burst internal shock model, fast radio burst blast wave model, Giant flare due to magnetars, and superluminous supernovae ejecta. We find that the reverse shock likely dominates the internal energy production for many astrophysical transients.

QoQ: a Q-transform based test for gravitational wave transient events

The observation of transient gravitational waves (GWs) is hindered by the presence of transient noise, colloquially referred to as glitches. These glitches can often be misidentified as GWs by searches for unmodeled transients using the excess-power type of methods and sometimes even excite template waveforms for compact binary coalescences while using matched filter techniques. They thus create a significant background in the searches. This background is more critical in getting identified promptly and efficiently within the context of real-time searches for GW transients. Such searches are the ones that have enabled multi-messenger astrophysics with the start of the Advanced LIGO and Advanced Virgo data taking in 2015 and they will continue to enable the field for further discoveries. With this work we propose and demonstrate the use of a signal-based test that quantifies the fidelity of the time-frequency decomposition of the putative signal based on first principles on how astrophysical transients are expected to be registered in the detectors and empirically measuring the instrumental noise. It is based on the Q-transform and a measure of the occupancy of the corresponding time-frequency pixels over select time-frequency volumes; we call it ‘QoQ’. Our method shows a 40% reduction in the number of retraction of public alerts that were issued by the LIGO-Virgo-KAGRA collaborations during the third observing run with negligible loss in sensitivity. Receiver Operator Characteristic measurements suggest the method can be used in online and offline searches for transients, reducing their background significantly.

Searching for long faint astronomical high energy transients: a data driven approach

HERMES Pathfinder is an in-orbit demonstration consisting of a constellation of six 3U nano-satellites hosting simple but innovative detectors for the monitoring of cosmic high-energy transients. The main objective of HERMES Pathfinder is to prove that accurate position of high-energy cosmic transients can be obtained using miniaturized hardware. The transient position is obtained by studying the delay time of arrival of the signal to different detectors hosted by nano-satellites on low-Earth orbits. In this context, we need to develop novel tools to fully exploit the future scientific data output of HERMES Pathfinder. In this paper, we introduce a new framework to assess the background count rate of a spaceborne, high energy detector; a key step towards the identification of faint astrophysical transients. We employ a neural network to estimate the background lightcurves on different timescales. Subsequently, we employ a fast change-point and anomaly detection technique called Poisson-FOCuS to identify observation segments where statistically significant excesses in the observed count rate relative to the background estimate exist. We test the new software on archival data from the NASA Fermi Gamma-ray Burst Monitor (GBM), which has a collecting area and background level of the same order of magnitude to those of HERMES Pathfinder. The neural network performances are discussed and analyzed over period of both high and low solar activity. We were able to confirm events in the Fermi-GBM catalog, both solar flares and gamma-ray bursts, and found events, not present in Fermi-GBM database, that could be attributed to solar flares, terrestrial gamma-ray flashes, gamma-ray bursts and galactic X-ray flashes. Seven of these are selected and further analyzed, providing an estimate of localisation and a tentative classification.

Quantifying chaos and randomness in magnetar bursts

ABSTRACT In this study, we explore the dynamical stability of magnetar bursts within the context of the chaos-randomness phase space for the first time, aiming to uncover unique behaviours compared to various astrophysical transients, including fast radio bursts (FRBs). We analyse burst energy time series data from active magnetar sources SGR J1550−5418 and SGR J1935+2154, focusing on burst arrival time and energy differences between consecutive events. We find a distinct separation in the time domain, where magnetar bursts exhibit significantly lower randomness compared to FRBs, solar flares, and earthquakes, with a slightly higher degree of chaos. In the energy domain, magnetar bursts exhibit a broad consistency with other phenomena, primarily due to the wide distribution of chaos-randomness observed across different bursts and sources. Intriguingly, contrary to expectations from the FRB-magnetar connection, the arrival time patterns of magnetar bursts in our analysis do not exhibit significant proximity to repeating FRBs in the chaos-randomness plane. This finding may challenge the hypothesis that FRBs are associated with typical magnetar bursts but indirectly supports the evidence that FRBs may primarily be linked to special magnetar bursts like peculiar X-ray bursts from SGR J1935+2154 observed simultaneously with Galactic FRB 200428.

VERITAS highlights of observations and results

Located in southern Arizona, VERITAS is amongst the most sensitive detectors for astrophysical very high energy (VHE; E&gt;100 GeV) gamma rays and has been operational since April 2007. We highlight some recent results from VERITAS observations. These include the long-term observations of the gamma-ray binaries HESS J0632+057 and LS I +61° 303, the observations of the Galactic Center region, and of the supernova remnant Cas A. We discuss the results from a decade of multi-wavelength observations of the blazar 1ES 1215+303, the EHT 2017 campaign on the M87 galaxy, the discovery of 3C 264 in VHE, and the observation of three flaring quasars. Brief highlights of the indirect dark matter searches and targets-of-opportunity (ToO) observations are also discussed. The ToO observations allow for rapid follow-up of multi-messenger alerts and astrophysical transients.

Dense forests of microshots in bursts from FRB 20220912A

ABSTRACT We report on exceptionally bright bursts (&amp;gt;400 Jy ms) detected from the repeating fast radio burst source FRB 20220912A using the Nançay radio telescope (NRT), as part of the ECLAT (Extragalactic Coherent Light from Astrophysical Transients) monitoring campaign. These bursts exhibit extremely luminous, broad-band, short-duration structures (∼16 μs), which we term ‘microshots’ and which can be especially well studied in the NRT data given the excellent signal-to-noise and dynamic range (32-bit samples). The estimated peak flux density of the brightest microshot is 450 Jy. We show that the microshots are clustered into dense ‘forests’ by modelling them as Weibull distributions and obtaining Weibull shape parameters of approximately 0.5. Our polarimetric analysis reveals that the bursts are nearly 100 per cent linearly polarized; have ≲10 per cent circular polarization fractions; a near-zero average rotation measure of 0.10(6) rad m−2; and varying polarization position angles over the burst duration. For one of the bursts, we analyse raw voltage data from simultaneous observations with the Westerbork RT-1 single 25-m dish. These data allow us to measure the scintillation bandwidth, 0.30(3) MHz, and to probe the bursts on (sub-)microsecond time-scales. Some important nuances related to dedispersion are also discussed. We propose that the emission mechanism for the broad-band microshots is potentially different from the emission mechanism of the broader burst components, which still show a residual drift of a few hundred MHz ms−1 after correcting for dispersion using the microshots. We discuss how the observed emission is phenomenologically analogous to different types of radio bursts from the Sun.

What’s the Difference? The Potential for Convolutional Neural Networks for Transient Detection without Template Subtraction

We present a study of the potential for convolutional neural networks (CNNs) to enable separation of astrophysical transients from image artifacts, a task known as “real–bogus” classification, without requiring a template-subtracted (or difference) image, which requires a computationally expensive process to generate, involving image matching on small spatial scales in large volumes of data. Using data from the Dark Energy Survey, we explore the use of CNNs to (1) automate the real–bogus classification and (2) reduce the computational costs of transient discovery. We compare the efficiency of two CNNs with similar architectures, one that uses “image triplets” (templates, search, and difference image) and one that takes as input the template and search only. We measure the decrease in efficiency associated with the loss of information in input, finding that the testing accuracy is reduced from ∼96% to ∼91.1%. We further investigate how the latter model learns the required information from the template and search by exploring the saliency maps. Our work (1) confirms that CNNs are excellent models for real–bogus classification that rely exclusively on the imaging data and require no feature engineering task and (2) demonstrates that high-accuracy (>90%) models can be built without the need to construct difference images, but some accuracy is lost. Because, once trained, neural networks can generate predictions at minimal computational costs, we argue that future implementations of this methodology could dramatically reduce the computational costs in the detection of transients in synoptic surveys like Rubin Observatory's Legacy Survey of Space and Time by bypassing the difference image analysis entirely.

Monte Carlo Radiation Transport for Astrophysical Transients Powered by Circumstellar Interaction

In this paper, we introduce SuperLite, an open-source Monte Carlo radiation transport code designed to produce synthetic spectra for astrophysical transient phenomena affected by circumstellar interaction. SuperLite utilizes Monte Carlo methods for semi-implicit, semirelativistic radiation transport in high-velocity shocked outflows, employing multigroup structured opacity calculations. The code enables rapid post-processing of hydrodynamic profiles to generate high-quality spectra that can be compared with observations of transient events, including superluminous supernovae, pulsational pair-instability supernovae, and other peculiar transients. We present the methods employed in SuperLite and compare the code’s performance to that of other radiative transport codes, such as SuperNu and CMFGEN. We show that SuperLite has successfully passed standard Monte Carlo radiation transport tests and can reproduce spectra of typical supernovae of Type Ia, Type IIP, and Type IIn.