arXiv subjects

Vinay L. Kashyap

Publications and source records attributed to Vinay L. Kashyap.

At least 19 recordsLinked to original sources

arXiv2026

The Chandra-Gaia Catalog of Counterparts: Resolving ambiguous Gaia matches to X-ray sources in the Chandra Source Catalog using Machine Learning

We present a framework to cross-match sources from the Chandra Source Catalog (CSC v2.1) with optical sources from Gaia Data Release 3. Unlike purely spatial approaches, we use source properties such as magnitudes, colors, and distances to identify true counterparts, detect chance coincidences, and resolve ambiguities when multiple plausible candidates exist. We define a training set of high-confidence matches using NWAY, a Bayesian cross-matching framework that accounts for positional errors and source densities. We train a gradient-boosted classifier (LightGBM) on a variety of features from both catalogs. Of the ~$254$k unique X-ray sources, we find counterparts for ~$113$k sources, of which plausible multiple counterparts are found for ~$7$k. We find no counterparts for ~$20$k sources for which separation-based cross-matching does find a match, and attribute half of these to chance coincidences. We validate the pipeline on the Chandra Orion Ultradeep Project (COUP), where the machine-learning matches reproduce 95% of NWAY cross-matches without using any positional information. We release a catalog of the ~$113$k Chandra-Gaia counterparts, together with ~$7$k alternative matches and ~$20$k ambiguous NWAY associations, supporting future population studies of sources detectable by both Chandra and Gaia. We discuss limitations and provide a generalization of the framework that is applicable in other cross-matching scenarios.

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arXiv2026

Lynx2030 Science Analysis Group: Final Report

The Lynx2030 Science Analysis Group (SAG) was convened to reassess the scientific goals and technical drivers of the Lynx mission concept amid a rapidly evolving astrophysics landscape. Building on the original Lynx Concept Study, the SAG examined how recent discoveries, emerging facilities, and advances in instrumentation influence the scientific opportunities for a next-generation flagship X-ray observatory. Through focused working groups, the SAG investigated the scientific impact of enhanced capabilities: (i) improved angular resolution, (ii) broader bandpass coverage, (iii) an enhanced microcalorimeter, (iv) new capabilities and observing modes, and (v) larger fields of view. Across a broad range of topics, from the formation of the first black holes and the evolution of galaxies to the baryon cycle, compact objects, stellar explosions, multi-messenger astrophysics, and the dynamic high-energy Universe, the SAG finds that the scientific motivation for a Lynx-class observatory remains compelling and, in many areas, has significantly strengthened over the past decade, prominently through JWST's discovery of the "Little Red Dots", likely massive accreting black holes in infant galaxies whose nature is fundamentally an X-ray question. This report shows that modest extensions beyond the original Lynx design reference mission can unlock transformative science while preserving the observatory's core architecture. Powerful current and future facilities such as Roman, Rubin, JWST, SKA, ngVLA, LISA, and NewAthena highlight the unique role a high-angular-resolution, high-throughput X-ray observatory would play in the multi-wavelength and multi-messenger ecosystem of the 2030s and beyond. The findings of the Lynx2030 SAG confirm Lynx's central vision: an unprecedented view of the hot and energetic Universe, enabling discoveries that will define high-energy astrophysics in the coming decades.

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arXiv2026

ESCAPE: a small explorer mission to study the stellar drivers of exoplanet evolution

The long-term stability of exoplanetary atmospheres depends critically on the extreme-ultraviolet (EUV) photon and high-energy particle fluxes from the host star, which are poorly constrained. To address this key gap in our understanding of atmospheric retention, we present the Extreme-ultraviolet Stellar Characterization for Atmospheric Physics and Evolution (ESCAPE) mission, a NASA Small Explorer concept proposed in 2026. ESCAPE employs extreme- and far-ultraviolet spectroscopy (80 - 1650 Ang) to provide the first comprehensive study of the stellar EUV history and stellar coronal mass ejection (CME) environments that control atmospheric mass-loss and determine the habitability of rocky exoplanets. This paper outlines both the primary science goals of the mission, the breadth of future general observer investigations, and a detailed design study of the mission's instrumentation. The ESCAPE instrument comprises a grazing incidence telescope that feeds multiple diffraction gratings and a photon-counting detector. We describe a demonstration of the Hettrick-Bowyer telescope, etched silicon diffraction gratings, the microchannel plate detector and housing, and gold and zirconium coatings. We present a STOP analysis that verifies ESCAPE's ability to meet its structural integrity, thermal stability, and optical performance requirements throughout the mission environment.

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arXiv2026

The LIRA-Ising Model: Estimating the boundaries of irregularly shaped X-ray sources

Mapping the boundary of an extended source is a key step in the study of its morphology. The background contamination and statistical fluctuations of typical astronomical images make this a challenging statistical task, particularly for X-ray images with low surface brightness. We develop a three-step Bayesian procedure to identify the boundaries of irregularly shaped sources. We first apply a Bayesian multiscale reconstruction algorithm known as LIRA to obtain posterior pixelwise probability distributions of the source intensity that properly account for known structures, astrophysical background, and the effect of the telescope point spread function. Next, we adopt an Ising model to group pixels with similar intensities into cohesive regions corresponding to background and source. Finally, the boundary is derived on the basis of the most likely aggregation of pixels into the source region. Because the overall model combines LIRA and the Ising model, we call it LIRA-Ising. We verify the proposed method using a set of simulation studies. We then apply it to the Chandra X-ray Observatory images of two high redshift quasars, PKS J1421-0643 and 0730+257, to determine the extent and morphology of X-ray jets. Our method shows a uniform X-ray surface brightness of PKS J1421-0643 jet, and identifies knotty structure in the X-ray jet of 0730+257.

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arXiv2026

A simple, flexible method for timing cross-calibration of space missions

The timing (cross-)calibration of astronomical instruments is often done by comparing pulsar times-of-arrival (TOAs) to a reference timing model. In high-energy astronomy, the choice of solar system ephemerides and source positions used to barycenter the photon arrival times has a significant impact on the procedure, requiring a full reprocessing of the data each time a new convention is used. Our method, developed as part of the activities of the International Astronomical Consortium for High Energy Calibration (IACHEC), adapts an existing pulsar solution to arbitrary JPL ephemerides and source positions by simulating geocentric TOAs and refitting timing models (implemented with PINT). We validate the procedure and apply it to thousands of observations of the Crab pulsar from 15 missions spanning 1996--2025, demonstrating inter-ephemeris TOA consistency at the $\lesssim5 μ$s level, using the DE200/FK5-based Jodrell Bank Monthly Ephemeris as a common reference. We release the TOAExtractor open-source tool and a TOA database to support future calibration and scientific studies. Instrument timing performance is broadly consistent with mission specifications; the X-ray-to-radio phase offset varies with energy and time at a level that is marginally consistent with the uncertainties of the radio ephemeris, motivating coordinated multiwavelength follow-up.

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arXiv2025

Detecting stellar flares in the presence of a deterministic trend and stochastic volatility

We develop a new and powerful method to analyze time series to rigorously detect flares in the presence of an irregularly oscillatory baseline, and apply it to stellar light curves observed with TESS. First, we remove the underlying non-stochastic trend using a time-varying amplitude harmonic model. We then model the stochastic component of the light curves in a manner analogous to financial time series, as an ARMA+GARCH process, allowing us to detect and characterize impulsive flares as large deviations inconsistent with the correlation structure in the light curve. We apply the method to exemplar light curves from TIC13955147 (a G5V eruptive variable), TIC269797536 (an M4 high-proper motion star), and TIC441420236 (AU Mic, an active dMe flare star), detecting up to $145$, $460$, and $403$ flares respectively, at rates ranging from ${\approx}0.4$--$8.5$~day$^{-1}$ over different sectors and under different detection thresholds. We detect flares down to amplitudes of $0.03$%, $0.29$%, and $0.007$% of the bolometric luminosity for each star respectively. We model the distributions of flare energies and peak fluxes as power-laws, and find that the solar-like star exhibits values similar to that on the Sun ($α_{E,P}\approx1.85,2.36$), while for the less- and highly-active low-mass stars $α_{E,P}>2$ and $<2$ respectively.

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arXiv2025

XRISM view of a stellar flare: High-resolution Fe K spectra of HR 1099, an RS CVn-type star

A high-resolution X-ray spectroscopic observation was made of the RS CVn-type binary star HR 1099 using the Resolve instrument onboard XRISM for its calibration purposes. During the $\sim$400 ks telescope time covering 1.5 binary orbit, a flare lasting for $\sim$100 ks was observed with a released X-ray radiation energy of $\sim 10^{34}$ erg, making it the first stellar flare ever observed with an X-ray microcalorimeter spectrometer. The flare peak count rate is 6.4 times higher than that in quiescence and is distinguished clearly in time thanks to the long telescope time. Many emission lines were detected in the 1.7--10 keV range both in the flare and quiescent phases. Using the high spectral resolution of Resolve in the Fe K band (6.5--7.0 keV), we resolved the inner-shell lines of Fe XIX--XXIV as well as the outer-shell lines of Fe XXV--XXVI. These lines have peaks in the contribution functions at different temperatures over a wide range, allowing us to construct the differential emission measure (DEM) distribution over the electron temperature of 1--10 keV (roughly 10--100 MK) based only on Fe lines, thus without an assumption of the elemental abundance. The reconstructed DEM has a bimodal distribution, and only the hotter component increased during the flare. The elemental abundance was derived based on the DEM distribution thus constructed. A significant abundance increase was observed during the flare for Ca and Fe, which are some of the elements with the lowest first ionization potential among those analyzed, but not for Si, S, and Ar. This behavior is seen in some giant solar flares and the present result is a clear example in stellar flares.

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arXiv2025

Chandra Large Project Observations of the Supernova Remnant N132D: Measuring the Expansion of the Forward Shock

We present results from the Chandra X-ray Observatory Large Project (878 ks in 28 observations) of the Large Magellanic Cloud supernova remnant N132D. We measure the expansion of the forward shock in the bright southern rim to be $0.\!^{\prime\prime}10 \pm 0.\!^{\prime\prime}02$ over the $\sim14.5$ yr baseline, which corresponds to a velocity of $1620\pm400~\mathrm{km\,s^{-1}}$ after accounting for several instrumental effects. We measure an expansion of $0.\!^{\prime\prime}23 \pm 0.\!^{\prime\prime}02$ and a shock velocity of $3840\pm260~\mathrm{km\,s^{-1}}$ for two features in an apparent blowout region in the northeast. The emission-measure-weighted average temperature inferred from X-ray spectral fits to regions in the southern rim is $0.95\pm0.17$ keV, consistent with the electron temperature implied by the shock velocity after accounting for Coulomb equilibration and adiabatic expansion. In contrast, the emission-measure-weighted average temperature for the northeast region is $0.77\pm0.04$ keV, which is significantly lower than the value inferred from the shock velocity. We fit 1-D evolutionary models for the shock in the southern rim and northeast region, using the measured radius and propagation velocity into a constant density and power-law profile circumstellar medium. We find good agreement with the age of $\sim2500$ years derived from optical expansion measurements for explosion energies of $1.5-3.0 \times 10^{51}\,\mathrm{erg}$, ejecta masses of $2-6 \,\mathrm{M_{\odot}}$ and ambient medium densities of $\sim0.33-0.66$ $\mathrm{amu~cm}^{-3}$ in the south and $\sim0.01-0.02$ $\mathrm{amu~cm}^{-3}$ in the northeast assuming a constant density medium. These results are consistent with previous studies that suggested the progenitor of N132D was an energetic supernova that exploded into a pre-existing cavity.

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arXiv2025

Representation Learning for Time-Domain High-Energy Astrophysics: Discovery of Extragalactic Fast X-ray Transient XRT 200515

We present a novel representation learning method for downstream tasks like anomaly detection, unsupervised classification, and similarity searches in high-energy data sets. This enabled the discovery of a new extragalactic fast X-ray transient (FXT) in Chandra archival data, XRT 200515, a needle-in-the-haystack event and the first Chandra FXT of its kind. Recent serendipitous discoveries in X-ray astronomy, including FXTs from binary neutron star mergers and an extragalactic planetary transit candidate, highlight the need for systematic transient searches in X-ray archives. We introduce new event file representations, E-t maps and E-t-dt cubes, that effectively encode both temporal and spectral information, enabling the seamless application of machine learning to variable-length event file time series. Our unsupervised learning approach employs PCA or sparse autoencoders to extract low-dimensional, informative features from these data representations, followed by clustering in the embedding space with DBSCAN. New transients are identified within transient-dominant clusters or through nearest-neighbour searches around known transients, producing a catalogue of 3559 candidates (3447 flares and 112 dips). XRT 200515 exhibits unique temporal and spectral variability, including an intense, hard <10s initial burst, followed by spectral softening in an ~800s oscillating tail. We interpret XRT 200515 as either the first giant magnetar flare observed at low X-ray energies or the first extragalactic Type I X-ray burst from a faint, previously unknown low-mass X-ray binary in the LMC. Our method extends to data sets from other observatories such as XMM-Newton, Swift-XRT, eROSITA, Einstein Probe, and upcoming missions like AXIS.

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arXiv2024

Six Maxims of Statistical Acumen for Astronomical Data Analysis

The production of complex astronomical data is accelerating, especially with newer telescopes producing ever more large-scale surveys. The increased quantity, complexity, and variety of astronomical data demand a parallel increase in skill and sophistication in developing, deciding, and deploying statistical methods. Understanding limitations and appreciating nuances in statistical and machine learning methods and the reasoning behind them is essential for improving data-analytic proficiency and acumen. Aiming to facilitate such improvement in astronomy, we delineate cautionary tales in statistics via six maxims, with examples drawn from the astronomical literature. Inspired by the significant quality improvement in business and manufacturing processes by the routine adoption of Six Sigma, we hope the routine reflection on these Six Maxims will improve the quality of both data analysis and scientific findings in astronomy.

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arXiv2024

Separating States in Astronomical Sources Using Hidden Markov Models: With a Case Study of Flaring and Quiescence on EV Lac

We present a new method to distinguish between different states (e.g., high and low, quiescent and flaring) in astronomical sources with count data. The method models the underlying physical process as latent variables following a continuous-space Markov chain that determines the expected Poisson counts in observed light curves in multiple passbands. For the underlying state process, we consider several autoregressive processes, yielding continuous-space hidden Markov models of varying complexity. Under these models, we can infer the state that the object is in at any given time. The continuous state predictions from these models are then dichotomized with the help of a finite mixture model to produce state classifications. We apply these techniques to X-ray data from the active dMe flare star EV Lac, splitting the data into quiescent and flaring states. We find that a first-order vector autoregressive process efficiently separates flaring from quiescence: flaring occurs over 30-40% of the observation durations, a well-defined persistent quiescent state can be identified, and the flaring state is characterized by higher plasma temperatures and emission measures.

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arXiv2024

Effect of Systematic Uncertainties on Density and Temperature Estimates in Coronae of Capella

We estimate the coronal density of Capella using the O VII and Fe XVII line systems in the soft X-ray regime that have been observed over the course of the Chandra mission. Our analysis combines measures of error due to uncertainty in the underlying atomic data with statistical errors in the Chandra data to derive meaningful overall uncertainties on the plasma density of the coronae of Capella. We consider two Bayesian frameworks. First, the so-called pragmatic-Bayesian approach considers the atomic data and their uncertainties as fully specified and uncorrectable. The fully-Bayesian approach, on the other hand, allows the observed spectral data to update the atomic data and their uncertainties, thereby reducing the overall errors on the inferred parameters. To incorporate atomic data uncertainties, we obtain a set of atomic data replicates, the distribution of which captures their uncertainty. A principal component analysis of these replicates allows us to represent the atomic uncertainty with a lower-dimensional multivariate Gaussian distribution. A $t$-distribution approximation of the uncertainties of a subset of plasma parameters including a priori temperature information, obtained from the temperature-sensitive-only Fe XVII spectral line analysis, is carried forward into the density- and temperature-sensitive O VII spectral line analysis. Markov Chain Monte Carlo based model fitting is implemented including Multi-step Monte Carlo Gibbs Sampler and Hamiltonian Monte Carlo. Our analysis recovers an isothermally approximated coronal plasma temperature of $\approx$5 MK and a coronal plasma density of $\approx$10$^{10}$ cm$^{-3}$, with uncertainties of 0.1 and 0.2 dex respectively.

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arXiv2024

Joint Deconvolution of Astronomical Images in the Presence of Poisson Noise

We present a new method for joint likelihood deconvolution (Jolideco) of a set of astronomical observations of the same sky region in the presence of Poisson noise. The observations may be obtained from different instruments with different resolution, and different point spread functions. Jolideco reconstructs a single flux image by optimizing the posterior distribution based on the joint Poisson likelihood of all observations under a patch-based image prior. The patch prior is parameterised via a Gaussian Mixture model which we train on high-signal-to-noise astronomical images, including data from the James Webb Telescope and the GLEAM radio survey. This prior favors correlation structures among the reconstructed pixel intensities that are characteristic of those observed in the training images. It is, however, not informative for the mean or scale of the reconstruction. By applying the method to simulated data we show that the combination of multiple observations and the patch-based prior leads to much improved reconstruction quality in many different source scenarios and signal to noise regimes. We demonstrate that with the patch prior Jolideco yields superior reconstruction quality relative to alternative standard methods such as the Richardson-Lucy method. We illustrate the results of Jolideco applied to example data from the Chandra X-ray Observatory and the Fermi-LAT Gamma-ray Space Telescope. By comparing the measured width of a counts based and the corresponding Jolideco flux profile of an X-ray filament in SNR 1E 0102.2-721} we find the deconvolved width of 0.58+- 0.02 arcsec to be consistent with the theoretical expectation derived from the known width of the PSF.

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arXiv2024

Spectral fit residuals as an indicator to increase model complexity

Spectral fitting of X-ray data usually involves minimizing statistics like the chi-square and the Cash statistic. Here we discuss their limitations and introduce two measures based on the cumulative sum (CuSum) of model residuals to evaluate whether model complexity could be increased: the percentage of bins exceeding a nominal threshold in a CuSum array (pct$_{CuSum}$), and the excess area under the CuSum compared to the nominal (p$_\textit{area}$). We demonstrate their use with an application to a $\textit{Chandra}$ ACIS spectral fit.

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arXiv2023

Breakthroughs in Cool Star Physics with the Line Emission Mapper X-ray Probe

We outline some of the highlights of the scientific case for the advancement of stellar high energy physics using the Line Emission Mapper X-ray Probe ({\it LEM}). The key to advancements with LEM lie in its large effective area -- up to 100 times that of the {\it Chandra} MEG -- and 1~eV spectral resolution. The large effective area opens up for the first time the ability to study time-dependent phenomena on their natural timescales at high resolution, such as flares and coronal mass ejections, and also opens the sky to much fainter targets than available to {\it Chandra} or {\it XMM-Newton}.

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arXiv2023

A Possible Third Body in the X-Ray System GRS 1747-312 and Models with Higher-Order Multiplicity

GRS 1747-312 is a bright Low-Mass X-ray Binary in the globular cluster Terzan 6, located at a distance of 9.5 kpc from the Earth. It exhibits regular outbursts approximately every 4.5 months, during which periodic eclipses are known to occur. These eclipses have only been observed in the outburst phase, and are not clearly seen when the source is quiescent. Recent Chandra observations of the source were performed in June 2019 and April, June, and August of 2021. Two of these observations captured the source during its outburst, and showed clear flux decreases at the expected time of eclipse. The other two observations occurred when the source was quiescent. We present the discovery of a dip that occurred during the quiescent state. The dip is of longer duration and its time of occurrence does not fit the ephemeris of the shorter eclipses. We study the physical characteristics of the dip and determine that it has all the properties of an eclipse by an object with a well defined surface. We find that there are several possibilities for the nature of the object causing the 5.3 ks eclipse. First, GRS 1747-312 may be an X-ray triple, with an LMXB orbited by an outer third object, which could be an M-dwarf, brown dwarf, or planet. Second, there could be two LMXBs in close proximity to each other, likely bound together. Whatever the true nature of the eclipser, its presence suggests that the GRS 1747-312 system is unique.

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arXiv2023

X-ray Activity Variations and Coronal Abundances of the Star-Planet Interaction candidate HD 179949

We carry out detailed spectral and timing analyses of the $Chandra$ X-ray data of HD 179949, a prototypical example of a star with a close-in giant planet with possible star-planet interaction (SPI) effects. We find a low coronal abundance $A({\rm Fe})/A({\rm H}){\approx}0.2$ relative to the solar photospheric baseline of Anders & Grevesse (1989), and significantly lower than the stellar photosphere as well. We further find low abundances of high First Ionization Potential (FIP) elements $A({\rm O})/A({\rm Fe}){\lesssim}1$, $A({\rm Ne})/A({\rm Fe}){\lesssim}0.1$, but with indications of higher abundances of $A({\rm N})/A({\rm Fe}){\gg}1, A({\rm Al})/A({\rm Fe}){\lesssim}10$. We estimate a FIP bias for this star in the range $\approx{-0.3}$ to ${-0.1}$, larger than the ${\lesssim}-$0.5 expected for stars of this type, but similar to stars hosting close-in hot Jupiters. We detect significant intensity variability over time scales ranging from 100 s - 10 ks, and also evidence for spectral variability over time scales of 1-10 ks. We combine the $Chandra$ flux measurements with $Swift$ and XMM-$Newton$ measurements to detect periodicities and determine that the dominant signal is tied to the stellar polar rotational period, consistent with expectations that the corona is rotational-pole dominated. We also find evidence for periodicity at both the planetary orbital frequency and at its beat frequency with the stellar polar rotational period, suggesting the presence of a magnetic connection between the planet and the stellar pole. If these periodicities represent an SPI signal, it is likely driven by a quasi-continuous form of heating (e.g., magnetic field stretching) rather than sporadic, hot, impulsive flare-like reconnections.

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arXiv2023

Classification of Chandra X-ray Sources in Cygnus OB2

We have devised a predominantly Naive Bayes method to classify the optical/IR matches to X-ray sources detected by Chandra in the Cygnus OB2 association into foreground, member, and background objects. We employ a variety of X-ray, optical, and infrared characteristics to construct likelihoods using training sets defined by well-measured sources. Combinations of optical photometry from SDSS (riz) and IPHAS (riHa), IR magnitudes from UKIDSS and 2MASS (JHK), X-ray quantiles and hardness ratios, and estimates of extinction Av are used to compute the relative probabilities that a given source belongs to one of the classes. We use Principal Component Analysis of photometric magnitude combinations to isolate the best axes for classification. We incorporate measurement errors into the classification. We evaluate the accuracy of the classification by inspection and reclassify a number of sources based on IR magnitudes, presence of disks, and X-ray spectral hardness. We also consider systematic errors due to extinction. We find that about 6100 objects are association members, 1400 are background, and 500 are foreground objects. The overall classification accuracy is 95%.

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