arXiv subjects

F. Förster

Publications and source records attributed to F. Förster.

At least 19 recordsLinked to original sources

arXiv2025

Tuning into spatial frequency space: Satellite and space debris detection in the ZTF alert stream

A significant challenge in the study of transient astrophysical phenomena is the identification of bogus events, among which human-made satellites and debris in Earth orbit remain major contaminants. Existing pipelines effectively identify satellite trails but can miss more complex signatures, such as collections of satellite glints. In the Rubin Observatory era, the scale of operations will increase tenfold compared to its precursor, the Zwicky Transient Facility (ZTF), requiring improvements in classification purity, data compression for informative alerts, and pipeline speed. We explore the use of the 2D Fast Fourier Transform (FFT) on difference images as a tool to enhance machine learning models for satellite detection. Using the ALeRCE single-stamp classifier as a baseline, we adapt its architecture to incorporate a cutout of the FFT of the difference image alongside the standard ZTF image triplet (science, reference, and difference stamps). We evaluate several stamp sizes and resolutions, focusing on regimes where data compression is critical due to alert size limits and real-time constraints. Incorporating the FFT significantly improves satellite classification, especially in the smallest field-of-view model (16 arcsec), where accuracy increases from 72.0\pm2.9% to 87.8\pm1.3%. This demonstrates the FFT's value in compressing and capturing extended satellite features. However, the FFT alone does not match the full-context performance of the 63 arcsec (95.9\pm1.3%) or multiscale (90.6\pm0.8%) models, highlighting the complementary role of spatial context. We show how FFTs can be leveraged to cull satellite and debris signatures from alert streams in current and future time-domain surveys.

astro-ph.IM↗

arXiv2025

ALeRCE light curve classifier: Tidal disruption event expansion pack

ALeRCE (Automatic Learning for the Rapid Classification of Events) processes the Zwicky Transient Facility (ZTF) alert stream in preparation for the Vera C. Rubin Observatory, classifying objects using a broad taxonomy. The ALeRCE light curve classifier is a balanced random forest (BRF) algorithm with a two-level scheme that uses variability features from the ZTF alert stream and colors from AllWISE and ZTF photometry. This work presents an updated version of the ALeRCE broker light curve classifier that includes tidal disruption events (TDEs) as a new subclass. We incorporated 24 new features, including the distance to the nearest source detected in ZTF science images and a parametric model of the power-law decay for transients. The labeled set was expanded to 219792 spectroscopically classified sources, including 60 TDEs. To integrate TDEs into ALeRCE's taxonomy, we identified specific characteristics that distinguish them from other transients: their central position in a galaxy, their typical decay pattern when fully disrupted, and their lack of color variability after disruption. We developed features to separate TDEs from other transient events. The updated classifier improves performance across all classes and integrates the TDE class with 91 percent recall. It also identifies a large number of potential TDE candidates in the ZTF alert stream's unlabeled data.

astro-ph.IM↗

arXiv2024

AT 2021hdr: A candidate tidal disruption of a gas cloud by a binary super massive black hole system

With a growing number of facilities able to monitor the entire sky and produce light curves with a cadence of days, in recent years there has been an increased rate of detection of sources whose variability deviates from standard behavior, revealing a variety of exotic nuclear transients. The aim of the present study is to disentangle the nature of the transient AT 2021hdr, whose optical light curve used to be consistent with a classic Seyfert 1 nucleus, which was also confirmed by its optical spectrum and high-energy properties. From late 2021, AT 2021hdr started to present sudden brightening episodes in the form of oscillating peaks in the Zwicky Transient Facility (ZTF) alert stream, and the same shape is observed in X-rays and UV from Swift data. The oscillations occur every about 60-90 days with amplitudes of around 0.2 mag in the g and r bands. Very Long Baseline Array (VLBA) observations show no radio emission at milliarcseconds scale. It is argued that these findings are inconsistent with a standard tidal disruption event (TDE), a binary supermassive black hole (BSMBH), or a changing-look active galactic nucleus (AGN); neither does this object resemble previous observed AGN flares, and disk or jet instabilities are an unlikely scenario. Here, we propose that the behavior of AT 2021hdr might be due to the tidal disruption of a gas cloud by a BSMBH. In this scenario, we estimate that the putative binary has a separation of about 0.83 mpc and would merge in about 70000 years. This galaxy is located at 9 kpc from a companion galaxy, and in this work we report this merger for the first time. The oscillations are not related to the companion galaxy.

astro-ph.GA↗

arXiv2024

SDSS1335+0728: The awakening of a $\sim 10^6 M_{\odot}$ black hole

The galaxy SDSS1335+0728, which had exhibited no prior optical variations during the preceding two decades, began showing significant nuclear variability in the Zwicky Transient Facility (ZTF) alert stream from December 2019 (as ZTF19acnskyy). Its behaviour suggests that SDSS1335+0728 hosts a $\sim 10^6 M_{\odot}$ black hole (BH) that is currently in the process of `turning on'. We present a multi-wavelength photometric analysis and spectroscopic follow-up performed with the aim of better understanding the origin of the nuclear variations detected in SDSS1335+0728. We used archival photometry and spectroscopic data to study the state of SDSS1335+0728 prior to December 2019, and new observations from Swift, SOAR/Goodman, VLT/X-shooter, and Keck/LRIS taken after its turn-on to characterise its current state. We find that: (a) since 2021, the UV flux is four times brighter than the flux reported by GALEX in 2004; (b) since June 2022, the mid-infrared flux has risen more than two times, and the W1-W2 WISE colour has become redder; (c) since February 2024, the source has begun showing X-ray emission; (d) the narrow emission line ratios are now consistent with a more energetic ionising continuum; (e) broad emission lines are not detected; and (f) the [OIII] line increased its flux $\sim 3.6$ years after the first ZTF alert, which implies a relatively compact narrow-line-emitting region. We conclude that the variations observed in SDSS1335+0728 could be either explained by an AGN that is just turning on or by an exotic tidal disruption event (TDE). If the former is true, SDSS1335+0728 is one of the strongest cases of an AGN observed in the process of activating. If the latter, it would correspond to the longest and faintest TDE ever observed (or another class of still unknown nuclear transient). Future observations of SDSS1335+0728 are crucial to further understand its behaviour.

astro-ph.GA↗

arXiv2024

ATAT: Astronomical Transformer for time series And Tabular data

The advent of next-generation survey instruments, such as the Vera C. Rubin Observatory and its Legacy Survey of Space and Time (LSST), is opening a window for new research in time-domain astronomy. The Extended LSST Astronomical Time-Series Classification Challenge (ELAsTiCC) was created to test the capacity of brokers to deal with a simulated LSST stream. We describe ATAT, the Astronomical Transformer for time series And Tabular data, a classification model conceived by the ALeRCE alert broker to classify light-curves from next-generation alert streams. ATAT was tested in production during the first round of the ELAsTiCC campaigns. ATAT consists of two Transformer models that encode light curves and features using novel time modulation and quantile feature tokenizer mechanisms, respectively. ATAT was trained on different combinations of light curves, metadata, and features calculated over the light curves. We compare ATAT against the current ALeRCE classifier, a Balanced Hierarchical Random Forest (BHRF) trained on human-engineered features derived from light curves and metadata. When trained on light curves and metadata, ATAT achieves a macro F1-score of 82.9 +- 0.4 in 20 classes, outperforming the BHRF model trained on 429 features, which achieves a macro F1-score of 79.4 +- 0.1. The use of Transformer multimodal architectures, combining light curves and tabular data, opens new possibilities for classifying alerts from a new generation of large etendue telescopes, such as the Vera C. Rubin Observatory, in real-world brokering scenarios.

astro-ph.IM↗

arXiv2023

Persistent and occasional: searching for the variable population of the ZTF/4MOST sky using ZTF data release 11

We present a variability, color and morphology based classifier, designed to identify transients, persistently variable, and non-variable sources, from the Zwicky Transient Facility (ZTF) Data Release 11 (DR11) light curves of extended and point sources. The main motivation to develop this model was to identify active galactic nuclei (AGN) at different redshift ranges to be observed by the 4MOST ChANGES project. Still, it serves as a more general time-domain astronomy study. The model uses nine colors computed from CatWISE and PS1, a morphology score from PS1, and 61 single-band variability features computed from the ZTF DR11 g and r light curves. We trained two versions of the model, one for each ZTF band. We used a hierarchical local classifier per parent node approach, where each node was composed of a balanced random forest model. We adopted a 17-class taxonomy, including non-variable stars and galaxies, three transient classes, five classes of stochastic variables, and seven classes of periodic variables. The macro averaged precision, recall and F1-score are 0.61, 0.75, and 0.62 for the g-band model, and 0.60, 0.74, and 0.61, for the r-band model. When grouping the four AGN classes into one single class, its precision, recall, and F1-score are 1.00, 0.95, and 0.97, respectively, for both the g and r bands. We applied the model to all the sources in the ZTF/4MOST overlapping sky, avoiding ZTF fields covering the Galactic bulge, including 86,576,577 light curves in the g-band and 140,409,824 in the r-band. Only 0.73\% of the g-band light curves and 2.62\% of the r-band light curves were classified as stochastic, periodic, or transient with high probability ($P_{init}\geq0.9$). We found that, in general, more reliable results are obtained when using the g-band model. Using the latter, we identified 384,242 AGN candidates, 287,156 of which have $P_{init}\geq0.9$.

astro-ph.IM↗

arXiv2023

Multiwavelength monitoring of the nucleus in PBC J2333.9-2343: the giant radio galaxy with a blazar-like core

PBC J2333.9-2343 is a giant radio galaxy at z = 0.047 with a bright central core associated to a blazar nucleus. If the nuclear blazar jet is a new phase of the jet activity, then the small orientation angle suggest a dramatic change of the jet direction. We present observations obtained between September 2018 and January 2019 (cadence larger than three days) with Effeslberg, SMARTS-1.3m, ZTF, ATLAS, Swift, and Fermi-LAT, and between April-July 2019 (daily cadence) with SMARTS-1.3m and ATLAS. Large (>2x) flux increases are observed on timescales shorter than a month, which are interpreted as flaring events. The cross correlation between the SMARTS-1.3m monitoring in the NIR and optical shows that these data do not show significant time lag within the measured errors. A comparison of the optical variability properties between non-blazars and blazars AGN shows that PBC J2333.9-2343 has properties more comparable to the latter. The SED of the nucleus shows two peaks, that were fitted with a one zone leptonic model. Our data and modelling shows that the high energy peak is dominated by External Compton from the dusty torus with mild contribution from Inverse Compton from the jet. The derived jet angle of 3 degrees is also typical of a blazar. Therefore, we confirm the presence of a blazar-like core in the center of this giant radio galaxy, likely a Flat Spectrum Radio Quasar with peculiar properties.

astro-ph.HE↗

arXiv2022

Type II supernovae from the Carnegie Supernova Project-I. III. Understanding SN II diversity through correlations between physical and observed properties

SNe II show great photometric and spectroscopic diversity which is attributed to the varied physical characteristics of their progenitor and explosion properties. In this study, the third of a series of papers where we analyse a sample of SNe II observed by the Carnegie Supernova Project-I, we present correlations between their observed and physical properties. Our analysis shows that explosion energy is the physical property that correlates with the highest number of parameters. We recover previously suggested relationships between the hydrogen-rich envelope mass and the plateau duration, and find that more luminous SNe II with higher expansion velocities, faster declining light curves, and higher Ni masses are consistent with higher energy explosions. In addition, faster declining SNe II are also compatible with more concentrated Ni in the inner regions of the ejecta. Positive trends are found between the initial mass, explosion energy, and Ni mass. While the explosion energy spans the full range explored with our models, the initial mass generally arises from a relatively narrow range. Observable properties were measured from our grid of models to determine the effect of each physical parameter on the observed SN II diversity. We argue that explosion energy is the physical parameter causing the greatest impact on SN II diversity, when assuming standard single-star evolution as in the models used in this study. The inclusion of pre-SN models assuming higher mass loss produces a significant increase in the strength of some correlations, particularly those between the progenitor hydrogen-rich envelope mass and the plateau and optically thick phase durations. These differences clearly show the impact of having different treatments of stellar evolution, implying that changes in the assumption of standard single-star evolution are necessary for a complete understanding of SN II diversity.

astro-ph.SR↗

arXiv2022

Type II supernovae from the Carnegie Supernova Project-I. II. Physical parameter distributions from hydrodynamical modelling

Linking supernovae to their progenitors is a powerful method for furthering our understanding of the physical origin of their observed differences, while at the same time testing stellar evolution theory. In this second study of a series of three papers where we characterise SNe II to understand their diversity, we derive progenitor properties (initial and ejecta masses, and radius), explosion energy, $^{56}$Ni mass, and its degree of mixing within the ejecta for a large sample of SNe II. This dataset was obtained by the Carnegie Supernova Project-I and is characterised by a high cadence of their optical and NIR light curves and optical spectra that were homogeneously observed and processed. A large grid of hydrodynamical models and a fitting procedure based on MCMC methods were used to fit the bolometric light curve and the evolution of the photospheric velocity of 53 SNe II. We infer ejecta masses between 7.9 and 14.8 $M_{\odot}$, explosion energies between 0.15 and 1.40 foe, and $^{56}$Ni masses between 0.006 and 0.069 $M_{\odot}$. We define a subset of 24~SNe (the `gold sample') with well-sampled bolometric light curves and expansion velocities for which we consider the results more robust. Most SNe~II in the gold sample ($\sim$88%) are found with ejecta masses in the range of $\sim$8-10 $M_{\odot}$, coming from low zero-age main-sequence masses (9-12 $M_{\odot}$). The modelling of the initial-mass distribution of the gold sample gives an upper mass limit of 21.3$^{+3.8}_{-0.4}$ $M_{\odot}$ and a much steeper distribution than that for a Salpeter massive-star IMF. This IMF incompatibility is due to the large number of low-mass progenitors found -- when assuming standard stellar evolution. This may imply that high-mass progenitors lose more mass during their lives than predicted. However, a deeper analysis of all stellar evolution assumptions is required to test this hypothesis.

astro-ph.SR↗

arXiv2021

Type II supernovae from the Carnegie Supernova Project-I. I. Bolometric light curves of 74 SNe II using uBgVriYJH photometry

The present study is the first of a series of three papers where we characterise the type II supernovae (SNe~II) from the Carnegie Supernova Project-I to understand their diversity in terms of progenitor and explosion properties. In this first paper, we present bolometric light curves of 74 SNe~II. We outline our methodology to calculate the bolometric luminosity, which consists of the integration of the observed fluxes in numerous photometric bands ($uBgVriYJH$) and black-body (BB) extrapolations to account for the unobserved flux at shorter and longer wavelengths. BB fits were performed using all available broadband data except when line blanketing effects appeared. Photometric bands bluer than $r$ that are affected by line blanketing were removed from the fit, which makes near-infrared (NIR) observations highly important to estimate reliable BB extrapolations to the infrared. BB fits without NIR data produce notably different bolometric light curves, and therefore different estimates of SN~II progenitor and explosion properties when data are modelled. We present two methods to address the absence of NIR observations: (a) colour-colour relationships from which NIR magnitudes can be estimated using optical colours, and (b) new prescriptions for bolometric corrections as a function of observed SN~II colours. Using our 74 SN~II bolometric light curves, we provide a full characterisation of their properties based on several observed parameters. We measured magnitudes at different epochs, as well as durations and decline rates of different phases of the evolution. An analysis of the light-curve parameter distributions was performed, finding a wide range and a continuous sequence of observed parameters which is consistent with previous analyses using optical light curves.

astro-ph.SR↗

arXiv2021

Searching for changing-state AGNs in massive datasets -- I: applying deep learning and anomaly detection techniques to find AGNs with anomalous variability behaviours

The classic classification scheme for Active Galactic Nuclei (AGNs) was recently challenged by the discovery of the so-called changing-state (changing-look) AGNs (CSAGNs). The physical mechanism behind this phenomenon is still a matter of open debate and the samples are too small and of serendipitous nature to provide robust answers. In order to tackle this problem, we need to design methods that are able to detect AGN right in the act of changing-state. Here we present an anomaly detection (AD) technique designed to identify AGN light curves with anomalous behaviors in massive datasets. The main aim of this technique is to identify CSAGN at different stages of the transition, but it can also be used for more general purposes, such as cleaning massive datasets for AGN variability analyses. We used light curves from the Zwicky Transient Facility data release 5 (ZTF DR5), containing a sample of 230,451 AGNs of different classes. The ZTF DR5 light curves were modeled with a Variational Recurrent Autoencoder (VRAE) architecture, that allowed us to obtain a set of attributes from the VRAE latent space that describes the general behaviour of our sample. These attributes were then used as features for an Isolation Forest (IF) algorithm, that is an anomaly detector for a "one class" kind of problem. We used the VRAE reconstruction errors and the IF anomaly score to select a sample of 8,809 anomalies. These anomalies are dominated by bogus candidates, but we were able to identify 75 promising CSAGN candidates.

astro-ph.IM↗

arXiv2020

Alert Classification for the ALeRCE Broker System: The Light Curve Classifier

We present the first version of the ALeRCE (Automatic Learning for the Rapid Classification of Events) broker light curve classifier. ALeRCE is currently processing the Zwicky Transient Facility (ZTF) alert stream, in preparation for the Vera C. Rubin Observatory. The ALeRCE light curve classifier uses variability features computed from the ZTF alert stream, and colors obtained from AllWISE and ZTF photometry. We apply a Balanced Random Forest algorithm with a two-level scheme, where the top level classifies each source as periodic, stochastic, or transient, and the bottom level further resolves each of these hierarchical classes, amongst 15 total classes. This classifier corresponds to the first attempt to classify multiple classes of stochastic variables (including core- and host-dominated active galactic nuclei, blazars, young stellar objects, and cataclysmic variables) in addition to different classes of periodic and transient sources, using real data. We created a labeled set using various public catalogs (such as the Catalina Surveys and {\em Gaia} DR2 variable stars catalogs, and the Million Quasars catalog), and we classify all objects with $\geq6$ $g$-band or $\geq6$ $r$-band detections in ZTF (868,371 sources as of 2020/06/09), providing updated classifications for sources with new alerts every day. For the top level we obtain macro-averaged precision and recall scores of 0.96 and 0.99, respectively, and for the bottom level we obtain macro-averaged precision and recall scores of 0.57 and 0.76, respectively. Updated classifications from the light curve classifier can be found at the \href{http://alerce.online}{ALeRCE Explorer website}.

astro-ph.IM↗

arXiv2020

The early discovery of SN 2017ahn: signatures of persistent interaction in a fast declining Type II supernova

We present high-cadence, comprehensive data on the nearby ($D\simeq33\,\rm{Mpc}$) Type II SN 2017ahn, discovered within $\sim$1 day of explosion, from the very early phases after explosion to the nebular phase. The observables of SN 2017ahn show a significant evolution over the $\simeq470\,\rm{d}$ of our follow-up campaign, first showing prominent, narrow Balmer lines and other high-ionization features purely in emission (i.e. flash spectroscopy features), which progressively fade and lead to a spectroscopic evolution similar to that of more canonical Type II supernovae. Over the same period, the decline of the light curves in all bands is fast, resembling the photometric evolution of linearly declining H-rich core-collapse supernovae. The modeling of the light curves and early flash spectra suggest a complex circumstellar medium surrounding the progenitor star at the time of explosion, with a first dense shell produced during the very late stages of its evolution being swept up by the rapidly expanding ejecta within the first $\sim6\,\rm{d}$ of the supernova evolution, while signatures of interaction are observed also at later phases. Hydrodynamical models support the scenario in which linearly declining Type II supernovae are predicted to arise from massive yellow super/hyper giants depleted of most of their hydrogen layers.

astro-ph.HE↗

arXiv2020

Progenitor properties of type II supernovae: fitting to hydrodynamical models using Markov chain Monte Carlo methods

The progenitor and explosion properties of type II supernovae (SNe II) are fundamental to understand the evolution of massive stars. Special interest has been given to the range of initial masses of their progenitors, but despite the efforts made, it is still uncertain. Direct imaging of progenitors in pre-explosion images point out an upper initial mass cutoff of $\sim$18$M_{\odot}$. However, this is in tension with previous studies in which progenitor masses inferred by light curve modelling tend to favour high-mass solutions. Moreover, it has been argued that light curve modelling alone cannot provide a unique solution for the progenitor and explosion properties of SNe II. We develop a robust method which helps us to constrain the physical parameters of SNe II by fitting simultaneously their bolometric light curve and the evolution of the photospheric velocity to hydrodynamical models using statistical inference techniques. Pre-supernova red supergiant models were created using the stellar evolution code MESA, varying the initial progenitor mass. The explosion of these progenitors was then processed through hydrodynamical simulations, where the explosion energy, synthesised nickel mass, and the latter's spatial distribution within the ejecta were changed. We compare to observations via Markov chain Monte Carlo methods. We apply this method to a well-studied set of SNe with an observed progenitor in pre-explosion images and compare with results in the literature. Progenitor mass constraints are found to be consistent between our results and those derived by pre-SN imaging and the analysis of late-time spectral modelling. We have developed a robust method to infer progenitor and explosion properties of SN II progenitors which is consistent with other methods in the literature, which suggests that hydrodynamical modelling is able to accurately constrain physical properties of SNe II.

astro-ph.SR↗

arXiv2020

The Automatic Learning for the Rapid Classification of Events (ALeRCE) Alert Broker

We introduce the Automatic Learning for the Rapid Classification of Events (ALeRCE) broker, an astronomical alert broker designed to provide a rapid and self--consistent classification of large etendue telescope alert streams, such as that provided by the Zwicky Transient Facility (ZTF) and, in the future, the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). ALeRCE is a Chilean--led broker run by an interdisciplinary team of astronomers and engineers, working to become intermediaries between survey and follow--up facilities. ALeRCE uses a pipeline which includes the real--time ingestion, aggregation, cross--matching, machine learning (ML) classification, and visualization of the ZTF alert stream. We use two classifiers: a stamp--based classifier, designed for rapid classification, and a light--curve--based classifier, which uses the multi--band flux evolution to achieve a more refined classification. We describe in detail our pipeline, data products, tools and services, which are made public for the community (see \url{https://alerce.science}). Since we began operating our real--time ML classification of the ZTF alert stream in early 2019, we have grown a large community of active users around the globe. We describe our results to date, including the real--time processing of $9.7\times10^7$ alerts, the stamp classification of $1.9\times10^7$ objects, the light curve classification of $8.5\times10^5$ objects, the report of 3088 supernova candidates, and different experiments using LSST-like alert streams. Finally, we discuss the challenges ahead to go from a single-stream of alerts such as ZTF to a multi--stream ecosystem dominated by LSST.

astro-ph.IM↗

arXiv2020

Design and operation of the ATLAS Transient Science Server

The Asteroid Terrestrial impact Last Alert System (ATLAS) system consists of two 0.5m Schmidt telescopes with cameras covering 29 square degrees at plate scale of 1.86 arcsec per pixel. Working in tandem, the telescopes routinely survey the whole sky visible from Hawaii (above $δ> -50^{\circ}$) every two nights, exposing four times per night, typically reaching $o < 19$ magnitude per exposure when the moon is illuminated and $c < 19.5$ per exposure in dark skies. Construction is underway of two further units to be sited in Chile and South Africa which will result in an all-sky daily cadence from 2021. Initially designed for detecting potentially hazardous near earth objects, the ATLAS data enable a range of astrophysical time domain science. To extract transients from the data stream requires a computing system to process the data, assimilate detections in time and space and associate them with known astrophysical sources. Here we describe the hardware and software infrastructure to produce a stream of clean, real, astrophysical transients in real time. This involves machine learning and boosted decision tree algorithms to identify extragalactic and Galactic transients. Typically we detect 10-15 supernova candidates per night which we immediately announce publicly. The ATLAS discoveries not only enable rapid follow-up of interesting sources but will provide complete statistical samples within the local volume of 100 Mpc. A simple comparison of the detected supernova rate within 100 Mpc, with no corrections for completeness, is already significantly higher (factor 1.5 to 2) than the current accepted rates.

astro-ph.IM↗

arXiv2020

Studying Type II supernovae as cosmological standard candles using the Dark Energy Survey

Despite vast improvements in the measurement of the cosmological parameters, the nature of dark energy and an accurate value of the Hubble constant (H$_0$) in the Hubble-Lemaître law remain unknown. To break the current impasse, it is necessary to develop as many independent techniques as possible, such as the use of Type II supernovae (SNe II). The goal of this paper is to demonstrate the utility of SNe II for deriving accurate extragalactic distances, which will be an asset for the next generation of telescopes where more-distant SNe II will be discovered. More specifically, we present a sample from the Dark Energy Survey Supernova Program (DES-SN) consisting of 15 SNe II with photometric and spectroscopic information spanning a redshift range up to 0.35. Combining our DES SNe with publicly available samples, and using the standard candle method (SCM), we construct the largest available Hubble diagram with SNe II in the Hubble flow (70 SNe II) and find an observed dispersion of 0.27 mag. We demonstrate that adding a colour term to the SN II standardisation does not reduce the scatter in the Hubble diagram. Although SNe II are viable as distance indicators, this work points out important issues for improving their utility as independent extragalactic beacons: find new correlations, define a more standard subclass of SNe II, construct new SN II templates, and dedicate more observing time to high-redshift SNe II. Finally, for the first time, we perform simulations to estimate the redshift-dependent distance-modulus bias due to selection effects.

astro-ph.HE↗

arXiv2020

Luminous Type II supernovae for their low expansion velocities

We present optical and near-IR data of three Type II supernovae (SNe II), SN 2008bm, SN 2009aj, and SN 2009au. These SNe display the following common characteristics: signs of early interaction of the ejecta with circumstellar material (CSM), blue $B\!-\!V$ colours, weakness of metal lines, low expansion velocities, and $V$-band absolute magnitudes 2-3 mag brighter than those expected for normal SNe II based on their expansion velocities. Two more SNe reported in the literature (SN 1983K and LSQ13fn) share properties similar to our sample. Analysing this set of five SNe II, which are luminous for their low expansion velocities (LLEV), we find that their properties can be reproduced assuming ejecta-CSM interaction that lasts between 4-11 weeks post explosion. The contribution of this interaction to the radiation field seems to be the dominant component determining the observed weakness of metal lines in the spectra rather than the progenitor metallicity. Based on hydrodynamic simulations, we find that the interaction of the ejecta with a CSM of ~3.6 M$_\odot$ can reproduce the light curves and expansion velocities of SN 2009aj. Using data collected by the Chilean Automatic Supernova Search, we estimate an upper limit for the LLEV SNe II fraction to be 2-4 per cent of all normal SNe II. With the current data-set, it is not clear whether the LLEV events are a separated class of SNe II with a different progenitor system, or whether they are the extreme of a continuum mediated by CSM interaction with the rest of the normal SN II population.

astro-ph.HE↗