J/A+A/677/A16ivo://CDS.VizieR/J/A+A/677/A16doi:10.26093/cds/vizier.36770016CDSDemianenko M.Malanchev K.Samorodova E.Sysak M.Shiriaev A.,Derkach D.Hushchyn M.2024-11-28T20:05:04Z2023-08-28T09:08:08ZCDS support team

CDS, Observatoire de Strasbourg, 11 rue de l'Universite, F-67000 Strasbourg, France

cds-question@unistra.frsupernovaeastronomical-modelsphotometryvisible-astronomyModern time-domain photometric surveys collect a lot of observations of various astronomical objects, and the coming era of large-scale surveys will provide even more information. Most of the objects have never received a spectroscopic follow-up, which is especially crucial for transients, e.g., supernovae. Flux time series are actively used as an affordable alternative for photometric classification and characterization, e.g., peak identification and luminosity decline estimation. However, the collected time series are multidimensional, irregularly sampled, may contain outliers, and do not have well-defined systematic uncertainties. This paper presents a search for the best-performing methods to approximate over time and wavelength the observed light curves for generating time series with regular time steps in each passband. We examine several light curve approximation methods based on neural networks such as Multilayer Perceptrons, Bayesian Neural Networks, and Normalizing Flows to approximate observations of a single light curve. Tests based on both the simulated PLAsTiCC and real Zwicky Transient Facility Bright Transient Survey data samples demonstrate that even a few observations are enough to fit networks and improve the quality of approximation compared to state-of-the-art models. The methods described in this work have lower computational complexity and work significantly faster than Gaussian Processes. Additionally, we analyze the performance of the approximation techniques in perspective of further peak identification and transients classification. The study results are released in an open and user-friendly Fulu Python library available on GitHub for the scientific community.2023A&A...677A..16Dhttps://cdsarc.cds.unistra.fr/viz-bin/cat/J/A+A/677/A16CatalogResearchIsServedByTAP VizieR generic servicerelated-tohttps://github.com/HSE-LAMBDA/fulu : HSE-LAMBDA/fulu programshttps://cds.unistra.fr/vizier-org/licences_vizier.htmlhttps://vizier.cds.unistra.fr/viz-bin/VizieR-2?-source=J/A+A/677/A16https://vizier.iucaa.in/viz-bin/VizieR-2?-source=J/A+A/677/A16http://vizieridia.saao.ac.za/viz-bin/VizieR-2?-source=J/A+A/677/A16https://vizier.cds.unistra.fr/viz-bin/votable?-source=J/A+A/677/A16https://vizier.iucaa.in/viz-bin/votable?-source=J/A+A/677/A16http://vizieridia.saao.ac.za/viz-bin/votable?-source=J/A+A/677/A16GETtext/xml+votablehttps://tapvizier.cds.unistra.fr/TAPVizieR/tapOpticaldefaultJ/A+A/677/A16/table9Estimated peaks for the Zwicky Transient Facility (ZTF) Bright Transient Survey (BTS) objectsrecnoRecord number assigned by the VizieR team. Should Not be used for identification.meta.recordintNum[0/1869] Sequnetial number of object (num)meta.idintNameZTF 9-letter identifier (ZTFAAaaaaaaa) (obj_names)meta.id;meta.maincharg+rmagBNNMagnitude of peak in {g} + {r} bands, Bayesian Neural Networks (BBM) (magnitudes_sum_bnn)magphot.mag;em.optdoubleFg+rBNNFlux of peak in {g} + {r} bands, Bayesian Neural Networks (BBM) (fluxes_sum_bnn)mJyphot.flux.density;em.optdoubleTg+rBNNTime of peak in {g} + {r} in MJD, Bayesian Neural Networks (BBM) (times_sum_bnn)dtime.epochdoublegmagBNNMagnitude of peak in {g} band, Bayesian Neural Networks (BBM) (magnitudes_g_bnn)magphot.mag;em.optdoubleFgBNNFlux of peak in {g} band, Bayesian Neural Networks (BBM) (fluxes_g_bnn)mJyphot.flux.density;em.optdoubleTgBNNTime of peak in {g} band in MJD, Bayesian Neural Networks (BBM) (times_g_bnn)dtime.epochdoublermagBNNMagnitude of peak in {r} band, Bayesian Neural Networks (BBM) (magnitudes_r_bnn)magphot.mag;em.optdoubleFrBNNFlux of peak in {r} band, Bayesian Neural Networks (BBM) (fluxes_r_bnn)mJyphot.flux.density;em.optdoubleTrBNNTime of peak in {r} band in MJD, Bayesian Neural Networks (BBM) (times_r_bnn)dtime.epochdoubleg+rmagNNdkMagnitude of peak in {g} + {r} bands, Multilayer Perceptron implemented (MLP) implemented using scikit-learn (magnitudes_sum_nn_sk)magphot.mag;em.optdoubleFg+rNNdkFlux of peak in {g} + {r} bands, Multilayer Perceptron implemented (MLP) implemented using scikit-learn (fluxes_sum_nn_sk)mJyphot.flux.density;em.optdoubleTg+rNNdkTime of peak in {g} + {r} bands in MJD, Multilayer Perceptron implemented (MLP) implemented using scikit-learn (times_sum_nn_sk)dtime.epochdoublegmagNNdkMagnitude of peak in {g} band, Multilayer Perceptron implemented (MLP) implemented using scikit-learn (magnitudes_g_nn_sk)magphot.mag;em.optdoubleFgNNdkFlux of peak in {g} band, Multilayer Perceptron implemented (MLP) implemented using scikit-learn (fluxes_g_nn_sk)mJyphot.flux.density;em.optdoubleTgNNdkTime of peak in {g} band in MJD, Multilayer Perceptron implemented (MLP) implemented using scikit-learn (times_g_nn_sk)dtime.epochdoublermagNNdkMagnitude of peak in {r} band, Multilayer Perceptron implemented (MLP) implemented using scikit-learn (magnitudes_r_nn_sk)magphot.mag;em.optdoubleFrNNdkFlux of peak in {r} band, Multilayer Perceptron implemented (MLP) implemented using scikit-learn (fluxes_r_nn_sk)mJyphot.flux.density;em.optdoubleTrNNdkTime of peak in {r} band in MJD, Multilayer Perceptron implemented (MLP) implemented using scikit-learn (times_r_nn_sk)dtime.epochdoubleg+rmagNNptMagnitude of peak in {g} + {r} bands, Multilayer Perceptron implemented (MLP) (magnitudes_sum_nn_pt)magphot.mag;em.optdoubleFg+rNNptFlux of peak in {g} + {r} bands, Multilayer Perceptron implemented (MLP) implemented using pytorch (fluxes_sum_nn_pt)mJyphot.flux.density;em.optdoubleTg+rNNptTime of peak in {g} + {r} bands in MJD, Multilayer Perceptron implemented (MLP) implemented using pytorch (times_sum_nn_pt)dtime.epochdoublegmagNNptMagnitude of peak in {g} band, Multilayer Perceptron implemented (MLP) implemented using pytorch (magnitudes_g_nn_pt)magphot.mag;em.optdoubleFgNNptFlux of peak in {g} band, Multilayer Perceptron implemented (MLP) implemented using pytorch (fluxes_g_nn_pt)mJyphot.flux.density;em.optdoubleTgNNptTime of peak in {g} band in MJD, Multilayer Perceptron implemented (MLP) implemented using pytorch, (times_g_nn_pt)dtime.epochdoublermagNNptMagnitude of peak in {r} band, Multilayer Perceptron implemented (MLP) implemented using pytorch (magnitudes_r_nn_pt)magphot.mag;em.optdoubleFrNNptFlux of peak in {r} band, Multilayer Perceptron implemented (MLP) implemented using pytorch (fluxes_r_nn_pt)mJyphot.flux.density;em.optdoubleTrNNptTime of peak in {r} band in MJD, Multilayer Perceptron implemented (MLP) implemented using pytorch (times_r_nn_pt)dtime.epochdoubleg+rmagNFMagnitude of peak in {g} + {r} bands, Normalizing Flows (NF) (magnitudes_sum_nf)magphot.mag;em.optdoubleFg+rNFFlux of peak in {g} + {r} bands, Normalizing Flows (NF) (fluxes_sum_nf)mJyphot.flux.density;em.optdoubleTg+rNFTime of peak in {g} + {r} bands in MJD, Normalizing Flows (NF) (times_sum_nf)dtime.epochdoublegmagNFMagnitude of peak in {g} band, Normalizing Flows (NF) (magnitudes_g_nf)magphot.mag;em.optdoubleFgNFFlux of peak in {g} band, Normalizing Flows (NF) (fluxes_g_nf)mJyphot.flux.density;em.optdoubleTgNFTime of peak in {g} band in MJD, Normalizing Flows (NF) (times_g_nf)dtime.epochdoublermagNFMagnitude of peak in {r} band, Normalizing Flows (NF) (magnitudes_r_nf)magphot.mag;em.optdoubleFrNFFlux of peak in {r} band, Normalizing Flows (NF) (fluxes_r_nf)mJyphot.flux.density;em.optdoubleTrNFTime of peak in {r} band in MJD, Normalizing Flows (NF) (times_r_nf)dtime.epochdoubleg+rmagGPMagnitude of peak in {g} + {r} bands, Gaussian Processes (GP) (magnitudes_sum_gp)magphot.mag;em.optdoubleFg+rGPFlux of peak in {g} + {r} bands, Gaussian Processes (GP) (fluxes_sum_gp)mJyphot.flux.density;em.optdoubleTg+rGPTime of peak in {g} + {r} bands in MJD, Gaussian Processes (GP) (times_sum_gp)dtime.epochdoublegmagGPMagnitude of peak in {g} band, Gaussian Processes (GP) (magnitudes_g_gp)magphot.mag;em.optdoubleFgGPFlux of peak in {g} band, Gaussian Processes (GP) (fluxes_g_gp)mJyphot.flux.density;em.optdoubleTgGPTime of peak in {g} band in MJD, Gaussian Processes (GP) (times_g_gp)dtime.epochdoublermagGPMagnitude of peak in {r} band, Gaussian Processes (GP) (magnitudes_r_gp)magphot.mag;em.optdoubleFrGPFlux of peak in {r} band, Gaussian Processes (GP) (fluxes_r_gp)mJyphot.flux.density;em.optdoubleTrGPTime of peak in {r} band in MJD, Gaussian Processes (GP) (times_r_gp)dtime.epochdouble