Niharika Sravan

The follow-up of gravitational-wave events by wide-field surveys is a crucial tool for the discovery of electromagnetic counterparts to gravitational wave sources, such as kilonovae. Machine learning tools can play an important role in aiding search efforts. We have developed a public tool to predict kilonova light curves using simulated low-latency alert data from the International Gravitational Wave Network during observing runs 4 (O4) and 5 (O5). It uses a bidirectional long-short-term memory model to forecast kilonova light curves from binary neutron star and neutron star–black hole mergers in the Zwicky Transient Facility (ZTF) and Rubin Observatory’s Legacy Survey of Space and Time filters. The model achieves a test mean squared error (MSE) of 0.12 for ZTF filters and 0.23 for Rubin filters, calculated by averaging the squared error over all time steps, filters, and light curves in the test set. We evaluate the performance of the model against merger events followed-up by the ZTF partnership during O4c. We also analyze the effect of incorporating constraints on physical features such as ejecta mass. Using ejecta mass, the performance of the model improves to an MSE of 0.1 for ZTF filters and 0.15 for Rubin filters. Our model is publicly available and can help to add important information to help plan follow-up of candidate events discovered by current and next-generation public surveys.

We present observations of SN 2023xgo, a transitional Type Ibn/Icn SN, from −5.6 to 63 days relative to r-band peak. Early spectra show C iii λ5696 emission like Type Icn SNe, shifting to Type Ibn features. The He i velocities (1800-10000 km s−1) and pseudo-equivalent widths are among the highest in the Ibn/Icn class. The light curve declines at 0.14mag d−1 until 30 days, matching SNe Ibn/Icn but slower than fast transients. SN 2023xgo is the faintest in our SN Ibn sample (Mr = −17.65 ± 0.04) but shows typical colour and host properties. Semi-analytical modelling of the light curve suggests a compact CSM shell (∼1012 − 1013 cm), mass-loss rate between 10−4 − 10−3 M⊙ yr−1 with CSM and ejecta masses of ∼0.22 and 0.12 M⊙, respectively. Post-maximum light-curve, spectral modelling favours a ∼3 M⊙ helium star progenitor with extended (∼1015 cm), stratified CSM (density exponent of 2.9) and mass-loss rate of 0.1 − 2.7 M⊙ yr−1. These two mass-loss regimes imply a radially varying CSM, shaped by asymmetry or changes in the progenitor's mass loss over time. This mass-loss behavior fits both binary and single-star evolution. Early Icn-like features stem from hot carbon ionization, fading to Ibn-like with cooling. SN 2023xgo thus offers rare insight into the connection between SNe Icn, Ibn, and SNe Ibn with ejecta signatures.

Among the various classes of fast optical transients (FOTs), kilonovae (KNe), which can emerge as a result of neutron star mergers, are extremely challenging to observe because of not only the rapid timescale on which they fade (on the order of days), but also due to the relative scarcity of their occurrence. This scarcity is compounded by the large number of other FOTs that may initially resemble the characteristic rise of a KNe. While these objects can be ruled out as candidate KNe by taking spectroscopy, a method of confidently ruling out transients based on photometric analysis alone would be incredibly valuable. We describe the compilation of various “imposter” transients, including a plurality of IIb SNe, and investigate a number of comparative metrics by which one might be able to remove transients from consideration without the use of spectroscopy. We provide a list of these objects and their classifications as well as a glossary of the transient types included in the sample.

We present optical, radio, and X-ray observations of EP250108a/SN 2025kg, a broad-line Type Ic supernova (SN Ic-BL) accompanying an Einstein Probe (EP) fast X-ray transient at z = 0.176. EP250108a/SN 2025kg possesses a double-peaked optical light curve, and its spectrum transitions from a blue underlying continuum to a typical SN Ic-BL spectrum over time. We fit a radioactive decay model to the second peak of the optical light curve and find SN parameters that are consistent with the SN Ic-BL population, while its X-ray and radio properties are consistent with those of low-luminosity GRB (LLGRB) 060218/SN 2006aj. We explore three scenarios to understand the system's multiwavelength emission: (a) SN ejecta interacting with an extended circumstellar medium (CSM), (b) the shocked cocoon of a collapsar-driven jet choked in its stellar envelope, and (c) the shocked cocoon of a collapsar-driven jet choked in an extended CSM. Models (b) and (c) can explain the optical light curve and are also consistent with the radio and X-ray observations. We favor model (c) because it can self-consistently explain both the X-ray prompt emission and first optical peak, but we do not rule out model (b). From the properties of the first peak in model (c), we find evidence that EP250108a/SN 2025kg interacts with an extended CSM and infer an envelope mass M-e similar to 0.1 M-circle dot and radius R-e similar to 4 x 10(13) cm. EP250108a/SN 2025kg's multiwavelength properties make it a close analog to LLGRB 060218/SN 2006aj and highlight the power of early follow-up observations in mapping the environments of massive stars prior to core collapse

We present observations of the Type IIP supernova (SN) SN 2024jlf, including spectroscopy beginning just 0.7 days (similar to 17 hr) after first light. Rapid follow-up was enabled by the new BTSbot-nearby program, which involves autonomously triggering target-of-opportunity requests for new transients in Zwicky Transient Facility data that are coincident with nearby (D < 60 Mpc) galaxies and identified by the BTSbot machine learning model. Early photometry and nondetections shortly prior to first light show that SN 2024jlf initially brightened by >4 mag day(-1), quicker than similar to 90% of Type II SNe. Early spectra reveal weak flash ionization features: narrow, short-lived (1.3 < tau[days] < 1.8) emission lines of H alpha, He ii, and C iv. Assuming a wind velocity of v(w) = 50 km s(-1), these properties indicate that the red supergiant progenitor exhibited enhanced mass loss in the last year before explosion. We constrain the mass-loss rate to 10(-4)

With large-scale surveys such as the Zwicky Transient Facility (ZTF), it has become possible to obtain a well-sampled light curve spanning the full length of the survey for any discovery within the survey footprint. Similarly, any transient within the footprint that was first detected before the start of the survey will likely have a large number of post-transient observations, making such transients excellent targets to search for the presence of late-time signals, particularly those due to interaction with circumstellar material (CSM). We searched for late-time signals in a sample of 7718 transients, mainly supernovae (SNe), that were first detected during the 10 years before the start of ZTF, aiming to find objects showing signs of late-time interaction with CSM. We found one candidate whose late-time signal is best explained by late-time CSM interaction, with the signal being around 300 days after transient discovery. A thin, distant shell containing less than or similar to 5 M-circle dot of material could explain the recovered signal. We also found five objects whose late-time signal is best explained by faint nuclear transients occurring in host nuclei close to the pre-ZTF transient locations. Finally, we found two objects where it is difficult to determine whether the signal is from a nuclear transient or due to late-time CSM interaction occurring over 5 years after the SN. This study demonstrates the ability of large-scale surveys to find faint transient signals for a variety of objects and uncover a population of previously unknown sources. However, the large number of non-detections shows that strong late-time CSM interaction occurring years after the SN explosion is extremely rare.

Supernovae (SNe) come in various flavors and are classified into different types based on emission and absorption lines in their spectra. SN candidates are now abundant with the advent of large systematic sky surveys like the Zwicky Transient Facility (ZTF), however, the identification bottleneck lies in their spectroscopic confirmation and classification. Fully robotic telescopes with dedicated spectrographs optimized for SN follow-up have eased the burden of data acquisition. However, the task of classifying the spectra still largely rests with the astronomers. Automating this classification step reduces human effort and can make the SN type available sooner to the public. For this purpose, we have developed a deep-learning based program for classifying core-collapse supernovae (CCSNe) with ultra-low resolution spectra from the SED-machine spectrograph on the Palomar 60 inch telescope. The program consists of hierarchical classification task layers, with each layer composed of multiple binary classifiers running in parallel to produce a reliable classification. The binary classifiers utilize recurrent neural networks and convolutional neural networks architecture and are designed to take multiple inputs to supplement spectra with g- and r-band photometry from ZTF. On non-host-contaminated and good quality SEDM spectra ("gold" test set) of CCSNe, CCSNscore is ∼94% accurate in distinguishing between hydrogen-rich (Type II) and hydrogen-poor (Type Ibc) CCSNe. With light curve input, CCSNscore classifies ∼83% of the gold set with high confidence (score ≥0.8 and score-error < 0.05), with ∼98% accuracy. Based on SNIascore's and CCSNscore's real-time performance on bright transients (mpk ≤ 18.5) and our reporting criteria, we expect ∼0.5% (∼4) true SNe Ia to be misclassified as SNe Ibc and ∼6% (∼17) of true CCSNe to be misclassified between Type II and Type Ibc annually on the Transient Name Server.

We present multi-wavelength analysis of ZTF23abelseb (AT 2023sva), an optically discovered fast-fading (Δmr = 2.2 mag in Δt = 0.74 days), luminous (Mr ∼ −30.0 mag) and red (g − r = 0.50 mag) transient at z = 2.28 with accompanying luminous radio emission. AT 2023sva does not possess a γ-ray burst (GRB) counterpart to an isotropic equivalent energy limit of Eγ, iso < 1.6 × 1052 erg, determined through searching γ-ray satellite archives between the last non-detection and first detection, making it the sixth example of an optically-discovered afterglow with a redshift measurement and no detected GRB counterpart. We analyze AT 2023sva’s optical, radio, and X-ray observations to characterize the source. From radio analyses, we find the clear presence of strong interstellar scintillation (ISS) 72 days after the initial explosion, allowing us to place constraints on the source’s angular size and bulk Lorentz factor. When comparing the source sizes derived from ISS of orphan events to those of the classical GRB population, we find orphan events have statistically smaller source sizes. We also utilize Bayesian techniques to model the multi-wavelength afterglow. Within this framework, we find evidence that AT 2023sva possesses a shallow power-law structured jet viewed slightly off-axis (θv = 0.07 ± 0.02) just outside of the jet’s core opening angle (θc = 0.06 ± 0.02). We determine this is likely the reason for the lack of a detected GRB counterpart, but also investigate other scenarios. AT 2023sva’s evidence for possessing a structured jet stresses the importance of broadening orphan afterglow search strategies to a diverse range of GRB jet angular energy profiles, to maximize the return of future optical surveys.

Type Ia supernovae ( are used to determine the distance-redshift relation and build the Hubble diagram. Neglecting their host-galaxy peculiar velocities (PVs) may bias the measurement of cosmological parameters. The smaller the redshift, the larger the effect is. We used realistic simulations of observed by the Zwicky Transient Facility (ZTF) to investigate the effect of different methods of taking PVs into account. We studied the impact of neglecting galaxy PVs and their correlations in an analysis of the Hubble diagram. We find that it is necessary to use the PV full covariance matrix computed from the velocity power spectrum to take the sample variance into account. Considering the results we have obtained using simulations, we determine the PV systematic effects in the context of the ZTF SN Ia DR2 sample. We determine the PV impact on the intercept of the Hubble diagram, $a_B$, which is directly linked to the measurement of $H_0$. We show that not taking into account PVs and their correlations results in a shift in the $H_0$ value of about $1.0$ and a slight underestimation of the $H_0$ error bar.

Machine learning methods are well established in the classification of quasars (QSOs). However, the advent of light-curve observations adds a great amount of complexity to the problem. Our goal is to use the Zwicky Transient Facility (ZTF) to create a catalog of QSOs. We process the ZTF DR20 light curves with a transformer artificial neural network and combine different surveys with extreme gradient boosting. Based on ZTF g -band and Wide-field Infrared Survey Explorer (WISE) observations, we find 4,849,574 objects classified as QSOs with confidence higher than 90% (QZO). We robustly classify objects fainter than the 5 σ signal-to-noise ratio (SNR) limit at g = 20.8 by requiring g  <  n _obs /80 + 20.375. For 33% of QZO objects, with available WISE data, we publish redshifts with estimated error Δ z /(1 +  z ) = 0.14. We find that ZTF classification is superior to the Pan-STARRS static bands, and on par with WISE and Gaia measurements, but the light curves provide the most important features for QSO classification in the ZTF data set. Using ZTF g -band data with at least 100 observational epochs per light curve, we obtain a 97% F1 score for QSOs. We find that with 3 day median cadence, a survey time span of at least 900 days is required to achieve a 90% QSO F1 score. However, one can obtain the same score with a survey time span of 1800 days and the median cadence prolonged to 12 days.