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Gautham Narayan

Publications and source records attributed to Gautham Narayan.

At least 55 records · Page 3Linked to original sources

Rubin-Euclid Derived Data Products: Initial Recommendations

This report is the result of a joint discussion between the Rubin and Euclid scientific communities. The work presented in this report was focused on designing and recommending an initial set of Derived Data products (DDPs) that could realize the science goals enabled by joint processing. All interested Rubin and Euclid data rights holders were invited to contribute via an online discussion forum and a series of virtual meetings. Strong interest in enhancing science with joint DDPs emerged from across a wide range of astrophysical domains: Solar System, the Galaxy, the Local Volume, from the nearby to the primaeval Universe, and cosmology.

astro-ph.IM↗

Real-Time Detection of Anomalies in Large-Scale Transient Surveys

New time-domain surveys, such as the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), will observe millions of transient alerts each night, making standard approaches of visually identifying new and interesting transients infeasible. We present two novel methods of automatically detecting anomalous transient light curves in real-time. Both methods are based on the simple idea that if the light curves from a known population of transients can be accurately modelled, any deviations from model predictions are likely anomalies. The first modelling approach is a probabilistic neural network built using Temporal Convolutional Networks (TCNs) and the second is an interpretable Bayesian parametric model of a transient. We demonstrate our methods' ability to provide anomaly scores as a function of time on light curves from the Zwicky Transient Facility. We show that the flexibility of neural networks, the attribute that makes them such a powerful tool for many regression tasks, is what makes them less suitable for anomaly detection when compared with our parametric model. The parametric model is able to identify anomalies with respect to common supernova classes with high precision and recall scores, achieving area under the precision-recall curves (AUCPR) above 0.79 for most rare classes such as kilonovae, tidal disruption events, intermediate luminosity transients, and pair-instability supernovae. Our ability to identify anomalies improves over the lifetime of the light curves. Our framework, used in conjunction with transient classifiers, will enable fast and prioritised followup of unusual transients from new large-scale surveys.

astro-ph.IM↗

Snowmass2021 Cosmic Frontier CF6 White Paper: Multi-Experiment Probes for Dark Energy -- Transients

This invited Snowmass 2021 White Paper highlights the power of joint-analysis of astronomical transients in advancing HEP Science and presents research activities that can realize the opportunities that come with current and upcoming projects. Transients of interest include gravitational wave events, neutrino events, strongly-lensed quasars and supernovae, and Type~Ia supernovae specifically. These transients can serve as probes of cosmological distances in the Universe and as cosmic laboratories of extreme strong-gravity, high-energy physics. Joint analysis refers to work that requires significant coordination from multiple experiments or facilities so encompasses Multi-Messenger Astronomy and optical transient discovery and distributed follow-up programs.

astro-ph.CO↗

SOAR/Goodman Spectroscopic Assessment of Candidate Counterparts of the LIGO-Virgo Event GW190814

On 2019 August 14 at 21:10:39 UTC, the LIGO/Virgo Collaboration (LVC) detected a possible neutron star-black hole merger (NSBH), the first ever identified. An extensive search for an optical counterpart of this event, designated GW190814, was undertaken using the Dark Energy Camera (DECam) on the 4m Victor M. Blanco Telescope at the Cerro Tololo Inter-American Observatory. Target of Opportunity interrupts were issued on 8 separate nights to observe 11 candidates using the 4.1m Southern Astrophysical Research (SOAR) telescope's Goodman High Throughput Spectrograph in order to assess whether any of these transients was likely to be an optical counterpart of the possible NSBH merger. Here, we describe the process of observing with SOAR, the analysis of our spectra, our spectroscopic typing methodology, and our resultant conclusion that none of the candidates corresponded to the gravitational wave merger event but were all instead other transients. Finally, we describe the lessons learned from this effort. Application of these lessons will be critical for a successful community spectroscopic follow-up program for LVC observing run 4 (O4) and beyond.

astro-ph.HE↗

Machine Learning and Cosmology

Methods based on machine learning have recently made substantial inroads in many corners of cosmology. Through this process, new computational tools, new perspectives on data collection, model development, analysis, and discovery, as well as new communities and educational pathways have emerged. Despite rapid progress, substantial potential at the intersection of cosmology and machine learning remains untapped. In this white paper, we summarize current and ongoing developments relating to the application of machine learning within cosmology and provide a set of recommendations aimed at maximizing the scientific impact of these burgeoning tools over the coming decade through both technical development as well as the fostering of emerging communities.

hep-ph↗

Optical Rebrightening of Extragalactic Transients from the Zwicky Transient Facility

Ongoing large-scale optical time-domain surveys, such as the Zwicky Transient Facility (ZTF), are producing alerts at unprecedented rates. Analysis of transient sources has so far followed two distinct paths: archival analysis of data on transient sources at a time when they are no longer observable and real-time analysis at the time when the sources are first detected. The latter is the realm of alert brokers such as the Arizona-NOIRLab Temporal Analysis and Response to Events System (ANTARES). In this paper, we synthesize the two analysis paths and present a first systematic study of archival alert-broker data, focusing on extragalactic transients with multipeaked light curves identified in the ANTARES archive of ZTF alerts. Our analysis yields a sample of 37 such sources, including core-collapse supernovae (with two analogs of iPTF14hls), thermonuclear supernovae interacting with their surrounding circumstellar medium, tidal disruption events, luminous blue variables, and as yet unclassified objects. A large fraction of the identified sources is currently active, warranting allocation of follow-up resources in the immediate future to further constrain their nature and the physical processes at work.

astro-ph.HE↗

SN 2018agk: A Prototypical Type Ia Supernova with a Smooth Power-law Rise in Kepler (K2)

We present the 30-min cadence Kepler/K2 light curve of the Type Ia supernova (SN Ia) SN 2018agk, covering approximately one week before explosion, the full rise phase and the decline until 40 days after peak. We additionally present ground-based observations in multiple bands within the same time range, including the 1-day cadence DECam observations within the first $\sim$5 days after the first light. The Kepler early light curve is fully consistent with a single power-law rise, without evidence of any bump feature. We compare SN 2018agk with a sample of other SNe~Ia without early excess flux from the literature. We find that SNe Ia without excess flux have slowly-evolving early colors in a narrow range ($g-i\approx -0.20\pm0.20$ mag) within the first $\sim 10$ days. On the other hand, among SNe Ia detected with excess, SN 2017cbv and SN 2018oh tend to be bluer, while iPTF16abc's evolution is similar to normal SNe Ia without excess in $g-i$. We further compare the Kepler light curve of SN 2018agk with companion-interaction models, and rule out the existence of a typical non-degenerate companion undergoing Roche-lobe overflow at viewing angles smaller than $45^{\circ}$.

astro-ph.HE↗

Real-time Detection of Anomalies in Multivariate Time Series of Astronomical Data

Astronomical transients are stellar objects that become temporarily brighter on various timescales and have led to some of the most significant discoveries in cosmology and astronomy. Some of these transients are the explosive deaths of stars known as supernovae while others are rare, exotic, or entirely new kinds of exciting stellar explosions. New astronomical sky surveys are observing unprecedented numbers of multi-wavelength transients, making standard approaches of visually identifying new and interesting transients infeasible. To meet this demand, we present two novel methods that aim to quickly and automatically detect anomalous transient light curves in real-time. Both methods are based on the simple idea that if the light curves from a known population of transients can be accurately modelled, any deviations from model predictions are likely anomalies. The first approach is a probabilistic neural network built using Temporal Convolutional Networks (TCNs) and the second is an interpretable Bayesian parametric model of a transient. We show that the flexibility of neural networks, the attribute that makes them such a powerful tool for many regression tasks, is what makes them less suitable for anomaly detection when compared with our parametric model.

cs.LG↗

An Early-Time Optical and Ultraviolet Excess in the type-Ic SN 2020oi

We present photometric and spectroscopic observations of Supernova 2020oi (SN 2020oi), a nearby ($\sim$17 Mpc) type-Ic supernova (SN Ic) within the grand-design spiral M100. We undertake a comprehensive analysis to characterize the evolution of SN 2020oi and constrain its progenitor system. We detect flux in excess of the fireball rise model $δt \approx 2.5$ days from the date of explosion in multi-band optical and UV photometry from the Las Cumbres Observatory and the Neil Gehrels Swift Observatory, respectively. The derived SN bolometric luminosity is consistent with an explosion with $M_{\rm ej} = 0.81 \pm 0.03 M_{\odot}$, $E_{k}= 0.79 \pm 0.09 \times 10^{51} \rm{erg} \rm{s}^{-1}$, and $M_{\rm Ni56} = 0.08 \pm 0.02 M_{\odot}$. Inspection of the event's decline reveals the highest $Δm_{15,\rm{bol}}$ reported for a stripped-envelope event to date. Modeling of optical spectra near event peak indicates a partially mixed ejecta comparable in composition to the ejecta observed in SN 1994I, while the earliest spectrum shows signatures of a possible interaction with material of a distinct composition surrounding the SN progenitor. Further, Hubble Space Telescope (HST) pre-explosion imaging reveals a stellar cluster coincident with the event. From the cluster photometry, we derive the mass and age of the SN progenitor using stellar evolution models implemented in the BPASS library. Our results indicate that SN 2020oi occurred in a binary system from a progenitor of mass $M_{\rm ZAMS} \approx 9.5 \pm 1.0 M_{\odot}$, corresponding to an age of $27 \pm 7$ Myr. SN 2020oi is the dimmest SN Ic event to date for which an early-time flux excess has been observed, and the first in which an early excess is unlikely to be associated with shock-cooling.

astro-ph.HE↗

Testing the Consistency of Dust Laws in SN Ia Host Galaxies: A BayeSN Examination of Foundation DR1

We apply BayeSN, our new hierarchical Bayesian model for the SEDs of Type Ia supernovae (SNe Ia), to analyse the $griz$ light curves of 157 nearby SNe Ia ($0.015<z<0.08$) from the public Foundation DR1 dataset. We train a new version of BayeSN, continuous from 0.35--0.95 $μ$m, which we use to model the properties of SNe Ia in the rest-frame $z$-band, study the properties of dust in their host galaxies, and construct a Hubble diagram of SN Ia distances determined from full $griz$ light curves. Our $griz$ Hubble diagram has a low total RMS of 0.13 mag using BayeSN, compared to 0.16 mag using SALT2. Additionally, we test the consistency of the dust law $R_V$ between low- and high-mass host galaxies by using our model to fit the full time- and wavelength-dependent SEDs of SNe Ia up to moderate reddening (peak apparent $B-V \lesssim 0.3$). Splitting the population at the median host mass, we find $R_V=2.84\pm0.31$ in low-mass hosts, and $R_V=2.58\pm0.23$ in high-mass hosts, both consistent with the global value of $R_V=2.61\pm0.21$ that we estimate for the full sample. For all choices of mass split we consider, $R_V$ is consistent across the step within $\lesssim1.2σ$. Modelling population distributions of dust laws in low- and high-mass hosts, we find that both subsamples are highly consistent with the full sample's population mean $μ(R_V) = 2.70\pm0.25$ with a 95% upper bound on the population $σ(R_V) < 0.61$. The $R_V$ population means are consistent within $\lesssim1.2σ$. We find that simultaneous fitting of host-mass-dependent dust properties within our hierarchical model does not account for the conventional mass step.

astro-ph.GA↗

El-CID: A filter for Gravitational-wave Electromagnetic Counterpart Identification

As gravitational-wave (GW) interferometers become more sensitive and probe ever more distant reaches, the number of detected binary neutron star mergers will increase. However, detecting more events farther away with GWs does not guarantee corresponding increase in the number of electromagnetic counterparts of these events. Current and upcoming wide-field surveys that participate in GW follow-up operations will have to contend with distinguishing the kilonova from the ever increasing number of transients they detect, many of which will be consistent with the GW sky-localization. We have developed a novel tool based on a temporal convolutional neural network architecture, trained on sparse early-time photometry and contextual information for Electromagnetic Counterpart Identification (El-CID). The overarching goal for El-CID is to slice through list of new transient candidates that are consistent with the GW sky localization, and determine which sources are consistent with kilonovae, allowing limited target-of-opportunity resources to be used judiciously. In addition to verifying the performance of our algorithm on an extensive testing sample, we validate it on AT2017gfo - the only EM counterpart of a binary neutron star merger discovered to date - and AT2019npv - a supernova that was initially suspected as a counterpart of the gravitational-wave event, GW190814, but was later ruled out after further analysis.

astro-ph.HE↗

The ANTARES Astronomical Time-Domain Event Broker

We describe the Arizona-NOIRLab Temporal Analysis and Response to Events System (ANTARES), a software instrument designed to process large-scale streams of astronomical time-domain alerts. With the advent of large-format CCDs on wide-field imaging telescopes, time-domain surveys now routinely discover tens of thousands of new events each night, more than can be evaluated by astronomers alone. The ANTARES event broker will process alerts, annotating them with catalog associations and filtering them to distinguish customizable subsets of events. We describe the data model of the system, the overall architecture, annotation, implementation of filters, system outputs, provenance tracking, system performance, and the user interface.

astro-ph.IM↗

GHOST: Using Only Host Galaxy Information to Accurately Associate and Distinguish Supernovae

We present GHOST, a database of 16,175 spectroscopically classified supernovae and the properties of their host galaxies. We have developed a host galaxy association method using image gradients that achieves fewer misassociations for low-z hosts and higher completeness for high-z hosts than previous methods. We use dimensionality reduction to identify the host galaxy properties that distinguish supernova classes. Our results suggest that the hosts of SLSNe, SNe Ia, and core collapse supernovae can be separated using host brightness information and extendedness measures derived from the host's light profile. Next, we train a random forest model with data from GHOST to predict supernova class using exclusively host galaxy information and the radial offset of the supernova. We can distinguish SNe Ia and core collapse supernovae with ~70% accuracy without any photometric data from the event itself. Vera C. Rubin Observatory will usher in a new era of transient population studies, demanding improved photometric tools for rapid identification and classification of transient events. By identifying the host features with high discriminatory power, we will maintain SN sample purities and continue to identify scientifically relevant events as data volumes increase. The GHOST database and our corresponding software for associating transients with host galaxies are both publicly available.

astro-ph.GA↗

Witnessing History: Rates and Detectability of Naked-Eye Milky-Way Supernovae

The Milky Way hosts on average a few supernova explosions per century, yet in the past millennium only five supernovae have been identified confidently in the historical record. This deficit of naked-eye supernovae is at least partly due to dust extinction in the Galactic plane. We explore this effect quantitatively, developing a formalism for the supernova probability distribution, accounting for dust and for the observer's flux limit. We then construct a fiducial axisymmetric model for the supernova and dust densities, featuring an exponential dependence on galactocentric radius and height, with core-collapse events in a thin disk and Type Ia events including a thick disk component. When no flux limit is applied, our model predicts supernovae are intrinsically concentrated in the Galactic plane, with Type Ia events extending to higher latitudes reflecting their thick disk component. We then apply a flux limit and include dust effects, to predict the sky distribution of historical supernovae. We use well-observed supernovae as light-curve templates, and introduce naked-eye discovery criteria. The resulting sky distributions are strikingly inconsistent with the locations of confident historical supernovae, none of which lie near our model's central peaks. Indeed, SN 1054 lies off the plane almost exactly in the anticenter, and SN 1181 is in the 2nd Galactic quadrant. We discuss possible explanations for these discrepancies. We calculate the percentage of all supernovae bright enough for historical discovery: $\simeq 13\%$ of core-collapse and $\simeq 33\%$ of Type Ia events. Using these and the confident historical supernovae, we estimate the intrinsic Galactic supernova rates, finding general agreement with other methods. Finally, we urge searches for supernovae in historical records from civilizations in the southern hemisphere.

astro-ph.SR↗

AT 2020iko: a WZ Sge-type DN candidate with an anomalous precursor event

The ongoing Zwicky Transient Facility (ZTF) survey is generating a massive alert rate from a variety of optical transients and variable stars, which are being filtered down to subsets meeting user-specified criteria by broker systems such as ANTARES. In a beta implementation of the algorithm of Soraisam et al. (2020) on ANTARES, we flagged AT 2020iko from the ZTF real-time alert stream as an anomalous source. This source is located close to a red extended SDSS source. In the first few epochs of detection, it exhibited a V-shaped brightness profile, preceded by non-detections both in ZTF and in ASASSN extending to 2014. Its full light curve shows a precursor event, followed by a main superoutburst and at least two rebrightenings. A low-resolution spectrum of this source points to a dwarf nova (DN) nature. Although some of the features of AT 2020iko indicate an SU UMa-type DN, its large amplitude, presence of rebrightenings, and inferred supercycle period of > 6 yr are in favor of AT 2020iko being a new WZ Sge-type dwarf nova candidate, a subset of rare DNe consisting of extreme mass-ratio (< 0.1) binaries with orbital period around the period minimum. AT 2020iko's precursor event brightened by 6.5 mag, while its decay spanned 3-5 mag. We speculate this superoutburst is associated with a less expanded accretion disk than in typical superoutbursts in WZ Sge systems, with the large depth of the precursor decay implying an extremely small mass-ratio. To the best of our knowledge, such a precursor event has not been recorded for any DN. This result serves to demonstrate the efficacy of our real-time anomaly search algorithm.

astro-ph.SR↗

A Hierarchical Bayesian SED Model for Type Ia Supernovae in the Optical to Near-Infrared

While conventional Type Ia supernova (SN Ia) cosmology analyses rely primarily on rest-frame optical light curves to determine distances, SNe Ia are excellent standard candles in near-infrared (NIR) light, which is significantly less sensitive to dust extinction. A SN Ia spectral energy distribution (SED) model capable of fitting rest-frame NIR observations is necessary to fully leverage current and future SN Ia datasets from ground- and space-based telescopes including HST, LSST, JWST, and RST. We construct a hierarchical Bayesian model for SN Ia SEDs, continuous over time and wavelength, from the optical to NIR ($B$ through $H$, or $0.35 -1.8\, μ$m). We model the SED as a combination of physically-distinct host galaxy dust and intrinsic spectral components. The distribution of intrinsic SEDs over time and wavelength is modelled with probabilistic functional principal components and the covariance of residual functions. We train the model on a nearby sample of 79 SNe Ia with joint optical and NIR light curves by sampling the global posterior distribution over dust and intrinsic latent variables, SED components, and population hyperparameters. The photometric distances of SNe Ia with NIR data near maximum light obtain a total RMS error of 0.10 mag with our BayeSN model, compared to 0.14 mag with SNooPy and SALT2 for the same sample. Jointly fitting the optical and NIR data of the full sample for a global host dust law, we find $R_V = 2.9 \pm 0.2$, consistent with the Milky Way average.

astro-ph.CO↗

A classification algorithm for time-domain novelties in preparation for LSST alerts: Application to variable stars and transients detected with DECam in the Galactic Bulge

With the advent of the Large Synoptic Survey Telescope (LSST), time-domain astronomy will be faced with an unprecedented volume and rate of data. Real-time processing of variables and transients detected by such large-scale surveys is critical to identifying the more unusual events and allocating scarce follow-up resources efficiently. We develop an algorithm to identify these novel events within a given population of variable sources. We determine the distributions of magnitude changes (dm) over time intervals (dt) for a given passband f, pf(dm|dt), and use these distributions to compute the likelihood of a test source being consistent with the population, or an outlier. We demonstrate our algorithm by applying it to the DECam multi-band time-series data of more than 2000 variable stars identified by Saha et al. (2019) in the Galactic Bulge that are largely dominated by long-period variables and pulsating stars. Our algorithm discovers 18 outlier sources in the sample, including a microlensing event, a dwarf nova, and two chromospherically active RS CVn stars, as well as sources in the Blue Horizontal Branch region of the color-magnitude diagram without any known counterparts. We compare the performance of our algorithm for novelty detection with multivariate KDE and Isolation Forest on the simulated PLAsTiCC dataset. We find that our algorithm yields comparable results despite its simplicity. Our method provides an efficient way for flagging the most unusual events in a real-time alert-broker system.

astro-ph.SR↗

Delay Time Distributions of Type Ia Supernovae From Galaxy and Cosmic Star Formation Histories

We present analytical reconstructions of type Ia supernova (SN Ia) delay time distributions (DTDs) by way of two independent methods: by a Markov chain Monte Carlo best-fit technique comparing the volumetric SN Ia rate history to today's compendium cosmic star-formation history, and secondly through a maximum likelihood analysis of the star formation rate histories of individual galaxies in the GOODS/CANDELS field, in comparison to their resultant SN Ia yields. We adopt a flexible skew-normal DTD model, which could match a wide range of physically motivated DTD forms. We find a family of solutions that are essentially exponential DTDs, similar in shape to the $β\approx-1$ power-law DTDs, but with more delayed events (>1 Gyr in age) than prompt events (<1 Gyr). Comparing these solutions to delay time measures separately derived from field galaxies and galaxy clusters, we find the skew-normal solutions can accommodate both without requiring a different DTD form in different environments. These model fits are generally inconsistent with results from single-degenerate binary population synthesis models, and are seemingly supportive of double-degenerate progenitors for most SN Ia events.

astro-ph.GA↗