arXiv subjects

M. V. Pruzhinskaya

Publications and source records attributed to M. V. Pruzhinskaya.

At least 19 recordsLinked to original sources

arXiv2026

Superluminous supernova search with PineForest

The advent of wide-field astronomical surveys has made available large and complex data sets. However, the process of discovery and interpretation of each potentially new astronomical source is, many times, still handcrafted. In this context, machine learning algorithms have emerged as a powerful tool to mine large data sets and lower the burden on the domain expert. Active learning strategies are specially good in this task. In this article, we used the PineForest algorithm to search for superluminous supernova (SLSN) candidates in the Zwicky Transient Facility data releases. Starting from a data set of $\sim$14 million objects, and using eight previously confirmed SLSN light curves as priors, we found seven SLSN candidates. In the course of this search, we posted to the Transient Name Server three previously unreported transients: AT 2018moa and AT 2018mob as SLSN candidates, and AT 2018mpg as a possible supernova. We also discovered a re-brightening of the SLSN-I SN 2018fcg approximately 1300 days after its main burst, which poses an interesting problem regarding its origin. This study represents the first application of the PineForest algorithm for the targeted search of rare astronomical transients, specifically SLSNe.

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arXiv2026

Anomaly detection in Fink. I. Discovery, follow-up, and classification of unusual sources

Modern wide-field time-domain surveys produce alert streams whose scientific potential is often concentrated in rare and unusual events. Efficient discovery therefore requires automated pipelines to be combined with rapid expert validation and follow-up. We present the first-year performance of the anomaly-detection (AD) pipeline operating within the Fink broker on the Zwicky Transient Facility alert stream, and assess its ability to identify scientifically valid outliers and enable discovery of rare phenomena. The pipeline transforms ZTF light curves into a compact set of features and ranks alerts using an Isolation Forest model trained on archival ZTF data. Each night, the 10 most anomalous candidates are distributed to experts via Slack/Telegram and exposed through an API. We also implement an expert-feedback loop using a public Telegram bot and retrain the model using the Active Anomaly Discovery algorithm. During the first year of operations (starting from 25 January 2023), the AD pipeline identified multiple high-interest sources and triggered dedicated photometric and spectroscopic follow-up. We report the discovery and multi-instrument (11-m SALT telescope, 2.5-m CMO telescope, 0.6-m ASA RC600, 0.25-m FRAM-ORM) follow-up of the rare AM CVn system Fink J062452.88+020818.3 of the WZ Sge type, UX Ori-type star Fink J222324.32+744222.0 and the unusual transient with precursor SN 2023mtp. In addition, the module triggered 33 supernovae, including 30 previously unreported ones, with candidates for superluminous and hostless events. Furthermore, nine new dwarf novae were discovered. These results show that broker-level anomaly detection, coupled with rapid dissemination, expert assessment, and follow-up observations, provide an effective bridge between large-scale survey streams and domain expertise, turning anomaly scores into astrophysical insights and concrete discoveries.

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arXiv2025

Lost and Found - A gallery of overlooked optical nuclear transients from the ZTF archive

Tidal disruption events (TDEs) correspond to the destruction of a star by the tidal forces around a black hole, leading to outbursts which can last from months to years. These transients are rare, and increasing the current sample is paramount to understand them. As part of the Fink alert broker, we have developed an early detection system for TDEs for the Zwicky Transient Facility (ZTF) data. In this paper, we report on the optical transients we found either during the development of this tool, or when applying the classifier to the existing archive. We use this sample to anticipate what improvements to the TDE detection systems will need to be implemented for future surveys. For all the transients, we present optical and infrared archival photometry from ZTF, WISE, and Catalina, and assess the previous nuclear activity of the host. We fit the ZTF lightcurves with both a phenomenological and a physically-motivated model. We report on a total of 19 optical nuclear transients, out of which nine are in passive galaxies, eight in active galaxies, and two for which the activity of the host is uncertain. Two transients are newly discovered repeated TDE candidates, and we compare them to the current sample of repeated optical nuclear transients. One transient is exceptionally long-lived (over 5 years), in an until-now passive galaxy. Three of the TDE-like flares in active galaxies have absolute g-band magnitudes brighter than -24, making them new Extreme Nuclear Transient (ENT) candidates. One seemingly repeated object was revealed to be two independent supernovae in the same galaxy. This sample shows both the potential of our detection system for future discovery, and the relevance of archival searches to reveal overlooked transients. It also raises several points of concern and avenues of improvement for current and future classifiers.

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arXiv2025

The largest ground-based catalogue of M-dwarf flares

We present the largest ground-based catalogue of M-dwarf flares to date, comprising 1,229 time-resolved events identified in Zwicky Transient Facility Data Release 17. Using high-cadence ZTF observations collected between April 2018 and September 2020, we analyzed over 93 million variable light curves containing 4.1 billion photometric measurements. Flare candidates were identified through a machine-learning pipeline trained on simulated light curves generated by injecting TESS-based flare templates into ZTF data and then refined through an extensive post-filtering stage combining an additional classifier, metadata checks, and human inspection. For 655 flares with reliable Gaia-based distances and well-sampled light curves, we derived bolometric energies ranging from 10^31 to 10^35 erg. A clear correlation is observed between flare frequency and spectral subtype, with a sharp increase toward later M dwarfs, particularly near M4-M5, coinciding with the transition to full convection. Using 680 flaring stars with known vertical distances from the Galactic plane, we find that the fraction of flaring stars decreases with increasing Galactic height. The resulting catalogue provides the most comprehensive ground-based sample of M-dwarf flares and establishes a framework for flare detection and classification in upcoming wide-field surveys such as the Vera C. Rubin Observatory Legacy Survey of Space and Time.

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arXiv2024

Real-bogus scores for active anomaly detection

In the task of anomaly detection in modern time-domain photometric surveys, the primary goal is to identify astrophysically interesting, rare, and unusual objects among a large volume of data. Unfortunately, artifacts -- such as plane or satellite tracks, bad columns on CCDs, and ghosts -- often constitute significant contaminants in results from anomaly detection analysis. In such contexts, the Active Anomaly Discovery (AAD) algorithm allows tailoring the output of anomaly detection pipelines according to what the expert judges to be scientifically interesting. We demonstrate how the introduction real-bogus scores, obtained from a machine learning classifier, improves the results from AAD. Using labeled data from the SNAD ZTF knowledge database, we train four real-bogus classifiers: XGBoost, CatBoost, Random Forest, and Extremely Randomized Trees. All the models perform real-bogus classification with similar effectiveness, achieving ROC-AUC scores ranging from 0.93 to 0.95. Consequently, we select the Random Forest model as the main model due to its simplicity and interpretability. The Random Forest classifier is applied to 67 million light curves from ZTF DR17. The output real-bogus score is used as an additional feature for two anomaly detection algorithms: static Isolation Forest and AAD. While results from Isolation Forest remained unchanged, the number of artifacts detected by the active approach decreases significantly with the inclusion of the real-bogus score, from 27 to 3 out of 100. We conclude that incorporating the real-bogus classifier result as an additional feature in the active anomaly detection pipeline significantly reduces the number of artifacts in the outputs, thereby increasing the incidence of astrophysically interesting objects presented to human experts.

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arXiv2024

Coniferest: a complete active anomaly detection framework

We present coniferest, an open source generic purpose active anomaly detection framework written in Python. The package design and implemented algorithms are described. Currently, static outlier detection analysis is supported via the Isolation forest algorithm. Moreover, Active Anomaly Discovery (AAD) and Pineforest algorithms are available to tackle active anomaly detection problems. The algorithms and package performance are evaluated on a series of synthetic datasets. We also describe a few success cases which resulted from applying the package to real astronomical data in active anomaly detection tasks within the SNAD project.

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arXiv2024

SNAD catalogue of M-dwarf flares from the Zwicky Transient Facility

Most of the stars in the Universe are M spectral class dwarfs, which are known to be the source of bright and frequent stellar flares. In this paper, we propose new approaches to discover M-dwarf flares in ground-based photometric surveys. We employ two approaches: a modification of a traditional method of parametric fit search and a machine learning algorithm based on active anomaly detection. The algorithms are applied to Zwicky Transient Facility (ZTF) data release 8, which includes the data from the ZTF high-cadence survey, allowing us to reveal flares lasting from minutes to hours. We analyze over 35 million ZTF light curves and visually scrutinize 1168 candidates suggested by the algorithms to filter out artifacts, occultations of a star by an asteroid, and other types of known variable objects. The result of this analysis is the largest catalogue of ZTF flaring stars to date, representing 134 flares with amplitudes ranging from -0.2 to -4.6 magnitudes, including repeated flares. Using Pan-STARRS DR2 colors, we assign a spectral subclass to each object in the sample. For 13 flares with well-sampled light curves and available geometric distances from Gaia DR3, we estimate the bolometric energy. This research shows that the proposed methods combined with the ZTF's cadence strategy are suitable for identifying M-dwarf flares and other fast transients, allowing for the extraction of significant astrophysical information from their light curves.

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arXiv2023

Rainbow: a colorful approach on multi-passband light curve estimation

We present Rainbow, a physically motivated framework which enables simultaneous multi-band light curve fitting. It allows the user to construct a 2-dimensional continuous surface across wavelength and time, even in situations where the number of observations in each filter is significantly limited. Assuming the electromagnetic radiation emission from the transient can be approximated by a black-body, we combined an expected temperature evolution and a parametric function describing its bolometric light curve. These three ingredients allow the information available in one passband to guide the reconstruction in the others, thus enabling a proper use of multi-survey data. We demonstrate the effectiveness of our method by applying it to simulated data from the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC) as well as real data from the Young Supernova Experiment (YSE DR1). We evaluate the quality of the estimated light curves according to three different tests: goodness of fit, time of peak prediction and ability to transfer information to machine learning (ML) based classifiers. Results confirm that Rainbow leads to equivalent (SNII) or up to 75% better (SN Ibc) goodness of fit when compared to the Monochromatic approach. Similarly, accuracy when using Rainbow best-fit values as a parameter space in multi-class ML classification improves for all classes in our sample. An efficient implementation of Rainbow has been publicly released as part of the light curve package at https://github.com/light-curve/light-curve-python. Our approach enables straight forward light curve estimation for objects with observations in multiple filters and from multiple experiments. It is particularly well suited for situations where light curve sampling is sparse.

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arXiv2023

Enabling the discovery of fast transients: A kilonova science module for the Fink broker

We describe the fast transient classification algorithm in the center of the kilonova (KN) science module currently implemented in the Fink broker and report classification results based on simulated catalogs and real data from the ZTF alert stream. We used noiseless, homogeneously sampled simulations to construct a basis of principal components (PCs). All light curves from a more realistic ZTF simulation were written as a linear combination of this basis. The corresponding coefficients were used as features in training a random forest classifier. The same method was applied to long (>30 days) and medium (<30 days) light curves. The latter aimed to simulate the data situation found within the ZTF alert stream. Classification based on long light curves achieved 73.87% precision and 82.19% recall. Medium baseline analysis resulted in 69.30% precision and 69.74% recall, thus confirming the robustness of precision results when limited to 30 days of observations. In both cases, dwarf flares and point Type Ia supernovae were the most frequent contaminants. The final trained model was integrated into the Fink broker and has been distributing fast transients, tagged as KN_candidates, to the astronomical community, especially through the GRANDMA collaboration. We showed that features specifically designed to grasp different light curve behaviors provide enough information to separate fast (KN-like) from slow (non-KN-like) evolving events. This module represents one crucial link in an intricate chain of infrastructure elements for multi-messenger astronomy which is currently being put in place by the Fink broker team in preparation for the arrival of data from the Vera Rubin Observatory Legacy Survey of Space and Time.

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arXiv2023

Reduction of supernova light curves by vector Gaussian processes

Bolometric light curves play an important role in understanding the underlying physics of various astrophysical phenomena, as they allow for a comprehensive modeling of the event and enable comparison between different objects. However, constructing these curves often requires the approximation and extrapolation from multicolor photometric observations. In this study, we introduce vector Gaussian processes as a new method for reduction of supernova light curves. This method enables us to approximate vector functions, even with inhomogeneous time-series data, while considering the correlation between light curves in different passbands. We applied this methodology to a sample of 29 superluminous supernovae (SLSNe) assembled using the Open Supernova Catalog. Their multicolor light curves were approximated using vector Gaussian processes. Subsequently, under the black-body assumption for the SLSN spectra at each moment of time, we reconstructed the bolometric light curves. The vector Gaussian processes developed in this work are accessible via the Python library gp-multistate-kernel on GitHub. Our approach provides an efficient tool for analyzing light curve data, opening new possibilities for astrophysical research.

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arXiv2022

SNAD Transient Miner: Finding Missed Transient Events in ZTF DR4 using k-D trees

We report the automatic detection of 11 transients (7 possible supernovae and 4 active galactic nuclei candidates) within the Zwicky Transient Facility fourth data release (ZTF DR4), all of them observed in 2018 and absent from public catalogs. Among these, three were not part of the ZTF alert stream. Our transient mining strategy employs 41 physically motivated features extracted from both real light curves and four simulated light curve models (SN Ia, SN II, TDE, SLSN-I). These features are input to a k-D tree algorithm, from which we calculate the 15 nearest neighbors. After pre-processing and selection cuts, our dataset contained approximately a million objects among which we visually inspected the 105 closest neighbors from seven of our brightest, most well-sampled simulations, comprising 89 unique ZTF DR4 sources. Our result illustrates the potential of coherently incorporating domain knowledge and automatic learning algorithms, which is one of the guiding principles directing the SNAD team. It also demonstrates that the ZTF DR is a suitable testing ground for data mining algorithms aiming to prepare for the next generation of astronomical data.

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arXiv2021

The Influence of the Peculiar Velocities of SNe Ia in Clusters of Galaxies on the Redshift Measurements

The most intriguing question of modern astronomy is the question of our Universe formation. The Hubble diagram analysis with Type Ia Supernovae (SNe Ia) is widely used to estimate the cosmological parameters with high accuracy. The cosmological measurements allow us to better understand the early stages of the Universe evolution. Nevertheless, the redshift on the SN Ia Hubble diagram includes the contribution from the unknown peculiar velocities that decreases the accuracy of such measurements. We consider the contamination of peculiar velocities of the host galaxies for those SNe that exploded in galaxy clusters. For this purpose, we use the Pantheon cosmological sample and offer the procedure to minimise the influence of such uncertainties.

astro-ph.CO↗

arXiv2021

Anomaly detection in the Zwicky Transient Facility DR3

We present results from applying the SNAD anomaly detection pipeline to the third public data release of the Zwicky Transient Facility (ZTF DR3). The pipeline is composed of 3 stages: feature extraction, search of outliers with machine learning algorithms and anomaly identification with followup by human experts. Our analysis concentrates in three ZTF fields, comprising more than 2.25 million objects. A set of 4 automatic learning algorithms was used to identify 277 outliers, which were subsequently scrutinised by an expert. From these, 188 (68%) were found to be bogus light curves -- including effects from the image subtraction pipeline as well as overlapping between a star and a known asteroid, 66 (24%) were previously reported sources whereas 23 (8%) correspond to non-catalogued objects, with the two latter cases of potential scientific interest (e. g. 1 spectroscopically confirmed RS Canum Venaticorum star, 4 supernovae candidates, 1 red dwarf flare). Moreover, using results from the expert analysis, we were able to identify a simple bi-dimensional relation which can be used to aid filtering potentially bogus light curves in future studies. We provide a complete list of objects with potential scientific application so they can be further scrutinised by the community. These results confirm the importance of combining automatic machine learning algorithms with domain knowledge in the construction of recommendation systems for astronomy. Our code is publicly available at https://github.com/snad-space/zwad

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arXiv2020

Local dark energy in the Sculptor Filament of galaxies

Two dozens of different mass galaxies observed at distances less than 10 Mpc from the Local Group are organized in the elongated structure known as the Sculptor Filament. We use recent Hubble Space Telescope data on local galaxies to study the dynamical structure and evolutionary trends of the filament. An N-body computer model, which reproduces its observed kinematics, is constructed under the assumption that the filament is embedded in the universal dark energy background. In the model, the motions of the filament members are controlled by their mutual gravity attraction force and the anti-gravity repulsion force produced by the local dark energy. It is found that the dark energy repulsion dominates the force field of the outer parts of the filament. Because of this, the filament expands and its expansion proceeds with acceleration. The dark energy domination increases with cosmic time and introduces to the filament the linear velocity--distance relation with the universal time-rate ("the Hubble constant") that depends asymptotically on the dark energy density only.

astro-ph.CO↗

arXiv2019

SUGAR: An improved empirical model of Type Ia Supernovae based on spectral features

Type Ia Supernovae (SNe Ia) are widely used to measure the expansion of the Universe. Improving distance measurements of SNe Ia is one technique to better constrain the acceleration of expansion and determine its physical nature. This document develops a new SNe Ia spectral energy distribution (SED) model, called the SUpernova Generator And Reconstructor (SUGAR), which improves the spectral description of SNe Ia, and consequently could improve the distance measurements. This model is constructed from SNe Ia spectral properties and spectrophotometric data from The Nearby Supernova Factory collaboration. In a first step, a PCA-like method is used on spectral features measured at maximum light, which allows us to extract the intrinsic properties of SNe Ia. Next, the intrinsic properties are used to extract the average extinction curve. Third, an interpolation using Gaussian Processes facilitates using data taken at different epochs during the lifetime of a SN Ia and then projecting the data on a fixed time grid. Finally, the three steps are combined to build the SED model as a function of time and wavelength. This is the SUGAR model. The main advancement in SUGAR is the addition of two additional parameters to characterize SNe Ia variability. The first is tied to the properties of SNe Ia ejecta velocity, the second is correlated with their calcium lines. The addition of these parameters, as well as the high quality the Nearby Supernova Factory data, makes SUGAR an accurate and efficient model for describing the spectra of normal SNe Ia as they brighten and fade. The performance of this model makes it an excellent SED model for experiments like ZTF, LSST or WFIRST.

astro-ph.CO↗

arXiv2018

Correcting for peculiar velocities of Type Ia Supernovae in clusters of galaxies

Type Ia Supernovae (SNe Ia) are widely used to measure the expansion of the Universe. To perform such measurements the luminosity and cosmological redshift ($z$) of the SNe Ia have to be determined. The uncertainty on $z$ includes an unknown peculiar velocity, which can be very large for SNe Ia in the virialized cores of massive clusters. We determine which SNe Ia exploded in galaxy clusters. We then study how the correction for peculiar velocities of host galaxies inside the clusters improves the Hubble residuals. Using 145 SNe Ia from the Nearby Supernova Factory we found 11 candidates for membership in clusters. To estimate the redshift of a cluster we applied the bi-weight technique. Then, we use the galaxy cluster redshift instead of the host galaxy redshift to construct the Hubble diagram. For SNe Ia inside galaxy clusters the dispersion around the Hubble diagram when peculiar velocities are taken into account is smaller in comparison with a case without peculiar velocity correction, with a $wRMS=0.130\pm0.038$ mag instead of $wRMS=0.137\pm0.036$ mag. The significance of this improvement is 3.58 $σ$. If we remove the very nearby Virgo cluster member SN2006X ($z<0.01$) from the analysis, the significance decreases to 1.34 $σ$. The peculiar velocity correction is found to be highest for the SNe Ia hosted by blue spiral galaxies, with high local specific star formation rate and smaller stellar mass, seemingly counter to what might be expected given the heavy concentration of old, massive elliptical galaxies in clusters. As expected, the Hubble residuals of SNe Ia associated with massive galaxy clusters improve when the cluster redshift is taken as the cosmological redshift of the SN. This fact has to be taken into account in future cosmological analyses in order to achieve higher accuracy for cosmological redshift measurements. Here we provide an approach to do so.

astro-ph.CO↗

arXiv2016

Multicolour modelling of SN 2013dx associated with GRB 130702A

We present optical observations of SN 2013dx, related to the Fermi burst GRB 130702A occurred at a redshift z = 0.145. It is the second-best sampled GRB-SN after SN~1998bw: the observational light curves contain more than 280 data points in uBgrRiz filters until 88 day after the burst, and the data were collected from our observational collaboration (Maidanak Observatory, Abastumani Observatory, Crimean Astrophysical Observatory, Mondy Observatory, National Observatory of Turkey, Observatorio del Roque de los Muchachos) and from the literature. We model numerically the multicolour light curves using the one-dimensional radiation hydrodynamical code STELLA, previously widely implemented for the modelling of typical non-GRB SNe. The best-fitted model has the following parameters: pre-supernova star mass M = 25 M_Sun, mass of a compact remnant M_CR = 6 M_Sun, total energy of the outburst E_oburst = 3.5 x 10^(52) erg, pre-supernova star radius R = 100 R_Sun, M_56Ni = 0.2 M_Sun which is totally mixed through the ejecta; M_O = 16.6 M_Sun, M_Si = 1.2 M_Sun, and M_Fe = 1.2 M_Sun, and the radiative efficiency of the SN is 0.1 per cent.

astro-ph.HE↗

arXiv2015

The optical identifcation of events with poorly defined locations: The case of the Fermi GBM GRB140801A

We report the early discovery of the optical afterglow of gamma-ray burst (GRB) 140801A in the 137 deg$^2$ 3-$σ$ error-box of the Fermi Gamma-ray Burst Monitor (GBM). MASTER is the only observatory that automatically react to all Fermi alerts. GRB 140801A is one of the few GRBs whose optical counterpart was discovered solely from its GBM localization. The optical afterglow of GRB 140801A was found by MASTER Global Robotic Net 53 sec after receiving the alert, making it the fastest optical detection of a GRB from a GBM error-box. Spectroscopy obtained with the 10.4-m Gran Telescopio Canarias and the 6-m BTA of SAO RAS reveals a redshift of $z=1.32$. We performed optical and near-infrared photometry of GRB 140801A using different telescopes with apertures ranging from 0.4-m to 10.4-m. GRB 140801A is a typical burst in many ways. The rest-frame bolometric isotropic energy release and peak energy of the burst is $E_\mathrm{iso} = 5.54_{-0.24}^{+0.26} \times 10^{52}$ erg and $E_\mathrm{p, rest}\simeq280$ keV, respectively, which is consistent with the Amati relation. The absence of a jet break in the optical light curve provides a lower limit on the half-opening angle of the jet $θ=6.1$ deg. The observed $E_\mathrm{peak}$ is consistent with the limit derived from the Ghirlanda relation. The joint Fermi GBM and Konus-Wind analysis shows that GRB 140801A could belong to the class of intermediate duration. The rapid detection of the optical counterpart of GRB 140801A is especially important regarding the upcoming experiments with large coordinate error-box areas.

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