arXiv subjects

Vysakh Anilkumar

Publications and source records attributed to Vysakh Anilkumar.

4 recordsLinked to original sources

arXiv2026

Automatically distinguishing Rubin transients from AGN using variability metrics

Stochastic variability of active galactic nuclei (AGN) can produce contaminants in the search for explosive extragalactic transients (such as supernovae and tidal disruption events). In the new era of the Rubin Observatory's Legacy Survey of Space and Time (LSST), previously uncatalogued AGN, especially those with luminosity near the survey detection limits, are expected to produce a flood of detections that have the potential to contaminate surveys targeting other transients, leading to inefficient use of spectroscopic follow-up time. For surveys aiming to statistically characterise transient demographics, it is advantageous to use easily modelled and reproducible selection criteria to distinguish AGN from other transients, rather than machine learning. We test enacting cuts based on simple data-driven photometric variability parameters to distinguish non-AGN extragalactic transients from standard AGN variability on both Zwicky Transient Facility photometry and simulated LSST photometry from the MALLORN data set. We also investigate the impact of light curve history availability, redshift range and filter selection on selection efficiency. We find that a two-dimensional cut incorporating the ratio of detection flux and pre-detection standard deviation and the ratio of detection flux to pre-detection mean flux is the most effective cut. This approach is easily scalable as these values are included in the LSST alert packets. We provide estimates of the completeness and purity of the sample produced by enacting this cut, and gauge the AGN contamination avoided. The parameters utilised in this approach could also be implemented as features for identifying AGN in a photometric classifier.

astro-ph.HE↗

arXiv2026

Uncertainty-Aware Tidal Disruption Event Classification : A Host-Agnostic Probabilistic Random Forest Approach

The classification of Tidal Disruption Events in large-scale photometric surveys is challenging because deterministic machine learning models produce overconfident misclassifications under varying data quality and low signal-to-noise conditions. Existing lightcurve-based approaches fail to incorporate measurement uncertainties, consequently generating brittle outputs. We present a host-agnostic, uncertainty-aware classification framework using a Probabilistic Random Forest (PRF). Our pipeline extracts 11 characteristic features from nuclear transients in the ZTF alert stream, including rise and decay timescales, blackbody temperature evolution, and pre-transient variability metrics. Relying exclusively on photometric data without host galaxy information ensures effectiveness for the faint transient population expected from the Rubin Observatory. The PRF, which treats feature measurements as distributions, was evaluated against XGBoost through Leave-One-Out Cross-Validation. It yields higher stability and robustness for ambiguous candidates than XGBoost. The two classifiers occupy complementary regimes. XGBoost achieves higher recall in balanced and high-precision scenarios, where its rigid decision boundaries efficiently isolate TDE-like sources, while PRF rejects more false positives by penalizing sources with large feature uncertainties. Applying this framework to archival data identified 11 new candidate TDEs from the unclassified population and 3 potential photometric TDEs within existing training labels previously misclassified as supernovae or active galactic nuclei. This work demonstrates that TDEs can be reliably identified using photometric lightcurve features, providing a host-independent framework. Uncertainty-aware, probabilistic classifiers are essential for the Rubin era to prevent the overconfident misclassifications inherent in deterministic models at low signal-to-noise.

astro-ph.HE↗

arXiv2025

MALLORN: Many Artificial LSST Lightcurves based on Observations of Real Nuclear transients

The Vera C. Rubin Observatory's 10-Year Legacy Survey of Space and Time (LSST) is expected to produce a hundredfold increase in the number of transients we observe. However, there are insufficient spectroscopic resources to follow up on all of the wealth of targets that LSST will provide. As such it is necessary to be able to prioritise objects for followup observations or inclusion in sample studies based purely on their LSST photometry. We are particularly keen to identify tidal disruption events (TDEs) with LSST. TDEs are immensely useful for determining black hole parameters and probing our understanding of accretion physics. To assist in these efforts, we present the Many Artificial LSST Lightcurves based on the Observations of Real Nuclear transients (MALLORN) data set and the corresponding classifier challenge for identifying TDEs. MALLORN comprises 10178 simulated LSST light curves, constructed from real Zwicky Transient Facility (ZTF) observations of 64 TDEs, 727 nuclear supernovae and 1407 AGN with spectroscopic labels using Gaussian process fitting, empirically-motivated spectral energy distributions from SNCosmo and the baseline from the Rubin Survey Simulator. Our novel approach can be easily adapted to simulate transients for any photometric survey using observations from another, requiring only the limiting magnitudes and an estimate of the cadence of observations. The MALLORN Astronomical Classification Challenge, launched on Kaggle on 15/10/2025, will allow competitors to test their photometric classifiers on simulated LSST data to find TDEs and improve upon their capabilities prior to the start of LSST.

astro-ph.HE↗

arXiv2025

Modelling variability power spectra of active galaxies from irregular time series

A common feature of Active Galactic Nuclei (AGN) is their random variations in brightness across the whole emission spectrum, from radio to $\gamma$-rays. Studying the nature and origin of these fluctuations is critical to characterising the underlying variability process of the accretion flow that powers AGN. Random timing fluctuations are often studied with the power spectrum; this quantifies how the amplitude of variations is distributed over temporal frequencies. Red noise variability -- when the power spectrum increases smoothly towards low frequencies -- is ubiquitous in AGN. The commonly used Fourier analysis methods, have significant challenges when applied to arbitrarily sampled light curves of red noise variability. Several time-domain methods exist to infer the power spectral shape in the case of irregular sampling but they suffer from biases which can be difficult to mitigate, or are computationally expensive. In this paper, we demonstrate a method infer the shape of broad-band power spectra for irregular time series, using a Gaussian process regression method scalable to large datasets. The power spectrum is modelled as a power-law model with one or two bends with flexible slopes. The method is fully Bayesian and we demonstrate its utility using simulated light curves. Finally, Ark 564, a well-known variable Seyfert 1 galaxy, is used as a test case and we find consistent results with the literature using independent X-ray data from XMM-Newton and Swift. We provide publicly available, documented and tested implementations in Python and Julia.

astro-ph.GA↗