arXiv subjects

Aarya A. Patil

Publications and source records attributed to Aarya A. Patil.

8 recordsLinked to original sources

arXiv2026

SAGUI: SED-based Segmentation of Multi-band Galaxy Images -- Application to JADES in GOODS-South

We present sagui, a modular framework for the analysis of multi-band imaging data in spatially resolved galaxies, with synergies to integral-field spectroscopy (IFS). Building on the spectro-spatial paradigm introduced by capivara for IFS data, sagui extends this approach to imaging datasets, enabling a coherent, pixel-level treatment of spatial and spectral information across multiple bands. The method follows a two-stage strategy: a starlet-based decomposition is first used to identify and mask spatial structures across multiple scales while suppressing noise, and a spectral-similarity analysis then partitions the image into coherent pixel groups that preserve spectral consistency. In addition to compact and high-contrast structures, the framework incorporates a dedicated statistical treatment, based on a copula transform, to identify and recover faint, diffuse low-surface-brightness components. We demonstrate the method across a diverse range of galaxy morphologies, highlighting its ability to characterize complex spatial structures, including clumps, bars, interacting systems, and low-surface-brightness features. As a case study, we apply it to eleven morphologically diverse galaxies from the James Webb Space Telescope Advanced Deep Extragalactic Survey in the GOODS--South field. sagui is released under an MIT license and is available at https://rafaelsdesouza.github.io/sagui/.

astro-ph.IM↗

arXiv2025

AT2022zod: An Unusual Tidal Disruption Event in an Elliptical Galaxy at Redshift 0.11

Tidal Disruption Events (TDEs) have long been hypothesized as valuable indicators of black holes, offering insight into their demographics and behaviour out to high redshift. TDEs have also enabled the discovery of a few Massive Black Holes (MBHs) with inferred masses of $10^4$--$10^6\,M_\odot$, often associated with the nuclei of dwarf galaxies or ultra-compact dwarf galaxies (UCDs). Here we present AT2022zod, an extreme and short-lived optical flare in an elliptical galaxy at z=0.11, residing within 3kpc of the galaxy's centre. Its luminosity and ~30-day duration make it unlikely to have originated from the host galaxy's central supermassive black hole (SMBH), which estimate to have a mass around $10^8\,M_\odot$. Assuming that the emission mechanism is consistent with known observed TDEs, we find that such a rapidly evolving transient could either be produced by a MBH in the intermediate-mass range or, alternatively, result from the tidal disruption of a star on a non-parabolic orbit around the central SMBH. We suggest that the most plausible origin for AT2022zod is the tidal disruption of a star by a MBH embedded in a UCD. As the Vera C. Rubin Observatory's Legacy Survey of Space and Time comes online, we propose that AT2022zod serves as an important event for refining search strategies and characterization techniques for intermediate-mass black holes.

astro-ph.HE↗

arXiv2025

Beyond prewhitening: detection of gravity modes and their period spacings in slowly pulsating B stars using the multitaper F-test

Gravity modes in main-sequence stars have traditionally been studied using a prewhitening approach, which iteratively identifies modes in the Fourier domain and subsequently tunes their frequencies, amplitudes, and phases through time-domain regression. While effective, this method becomes inefficient when analysing large volumes of long time-series data and often relies on subjective stopping criteria to determine the number of iterations. We aim to perform frequency extraction of gravity modes in slowly pulsating B (SPB) stars using a statistically robust, data-driven approach based on advanced power spectrum and harmonic analysis techniques. Our approach employs the multitaper non-uniform fast Fourier transform, mtNUFFT, a power spectrum estimator that addresses several statistical limitations of traditional methods such as the Lomb-Scargle periodogram. We apply its extension, the multitaper F-test, to extract coherent gravity modes from 4-year Kepler light curves of SPB stars and to search for period spacing patterns among the extracted modes. The multitaper F-test enables fast and accurate extraction of the properties of gravity modes with quasi-infinite lifetimes, preferentially selecting modes that exhibit purely periodic behaviour. Although the method typically extracts fewer frequencies than conventional prewhitening, it recovers most known modes and, in some cases, reveals new ones. We also find evidence for gravity modes with long but finite lifetimes, and detect more than one period spacing pattern in some of the studied SPB stars. Overall, the multitaper F-test offers a more objective and statistically sound alternative to prewhitening. It scales efficiently to large datasets containing thousands of pulsators, and has the potential to facilitate mode identification and to distinguish between the different excitation mechanisms operating in SPB stars.

astro-ph.SR↗

arXiv2024

Improving Power Spectrum Estimation using Multitapering: Efficient asteroseismic analyses for understanding stars, the Milky Way, and beyond

Asteroseismic time-series data have imprints of stellar oscillation modes, whose detection and characterization through time-series analysis allows us to probe stellar interior physics. Such analyses usually occur in the Fourier domain by computing the Lomb-Scargle (LS) periodogram, an estimator of the power spectrum underlying unevenly-sampled time-series data. However, the LS periodogram suffers from the statistical problems of (1) inconsistency (or noise) and (2) bias due to high spectral leakage. Here, we develop a multitaper power spectrum estimator using the Non-Uniform Fast Fourier Transform (mtNUFFT) to tackle the inconsistency and bias problems of the LS periodogram. Using a simulated light curve, we show that the mtNUFFT power spectrum estimate of solar-like oscillations has lower variance and bias than the LS estimate. We also apply our method to the Kepler-91 red giant, and combine it with PBjam peakbagging to obtain mode parameters and a derived age estimate of $3.97 \pm 0.52$ Gyr. PBjam allows the improvement of age precision relative to the $4.27 \pm 0.75$ Gyr APOKASC-2 (uncorrected) estimate, whereas partnering mtNUFFT with PBjam speeds up peakbagging thrice as much as LS. This increase in efficiency has promising implications for Galactic archaeology, in addition to stellar structure and evolution studies. Our new method generally applies to time-domain astronomy and is implemented in the public Python package tapify, available at https://github.com/aaryapatil/tapify.

astro-ph.IM↗

arXiv2024

Improving Harmonic Analysis using Multitapering: Precise frequency estimation of stellar oscillations using the harmonic F-test

In Patil et. al 2024a, we developed a multitaper power spectrum estimation method, mtNUFFT, for analyzing time-series with quasi-regular spacing, and showed that it not only improves upon the statistical issues of the Lomb-Scargle periodogram, but also provides a factor of three speed up in some applications. In this paper, we combine mtNUFFT with the harmonic F-test to test the hypothesis that a strictly periodic signal or its harmonic (as opposed to e.g. a quasi-periodic signal) is present at a given frequency. This mtNUFFT/F-test combination shows that multitapering allows detection of periodic signals and precise estimation of their frequencies, thereby improving both power spectrum estimation and harmonic analysis. Using asteroseismic time-series data for the Kepler-91 red giant, we show that the F-test automatically picks up the harmonics of its transiting exoplanet as well as certain dipole ($l=1$) mixed modes. We use this example to highlight that we can distinguish between different types of stellar oscillations, e.g., transient (damped, stochastically-excited) and strictly periodic (undamped, heat-driven). We also illustrate the technique of dividing a time-series into chunks to further examine the transient versus periodic nature of stellar oscillations. The harmonic F-test combined with mtNUFFT is implemented in the public Python package tapify (https://github.com/aaryapatil/tapify), which opens opportunities to perform detailed investigations of periodic signals in time-domain astronomy.

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arXiv2023

Decoding the age-chemical structure of the Milky Way disk: An application of Copulas and Elicitable Maps

In the Milky Way, the distribution of stars in the $[α/\mathrm{Fe}]$ vs. $[\mathrm{Fe/H}]$ and $[\mathrm{Fe/H}]$ vs. age planes holds essential information about the history of star formation, accretion, and dynamical evolution of the Galactic disk. We investigate these planes by applying novel statistical methods called copulas and elicitable maps to the ages and abundances of red giants in the APOGEE survey. We find that the low- and high-$α$ disk stars have a clean separation in copula space and use this to provide an automated separation of the $α$ sequences using a purely statistical approach. This separation reveals that the high-$α$ disk ends at the same [$α$/Fe] and age at high $[\mathrm{Fe/H}]$ as the low-$[\mathrm{Fe/H}]$ start of the low-$α$ disk, thus supporting a sequential formation scenario for the high- and low-$α$ disks. We then combine copulas with elicitable maps to precisely obtain the correlation between stellar age $τ$ and metallicity $[\mathrm{Fe/H}]$ conditional on Galactocentric radius $R$ and height $z$ in the range $0 < R < 20$ kpc and $|z| < 2$ kpc. The resulting trends in the age-metallicity correlation with radius, height, and [$α$/Fe] demonstrate a $\approx 0$ correlation wherever kinematically-cold orbits dominate, while the naively-expected negative correlation is present where kinematically-hot orbits dominate. This is consistent with the effects of spiral-driven radial migration, which must be strong enough to completely flatten the age-metallicity structure of the low-$α$ disk.

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arXiv2022

The Astropy Project: Sustaining and Growing a Community-oriented Open-source Project and the Latest Major Release (v5.0) of the Core Package

The Astropy Project supports and fosters the development of open-source and openly-developed Python packages that provide commonly needed functionality to the astronomical community. A key element of the Astropy Project is the core package $\texttt{astropy}$, which serves as the foundation for more specialized projects and packages. In this article, we summarize key features in the core package as of the recent major release, version 5.0, and provide major updates for the Project. We then discuss supporting a broader ecosystem of interoperable packages, including connections with several astronomical observatories and missions. We also revisit the future outlook of the Astropy Project and the current status of Learn Astropy. We conclude by raising and discussing the current and future challenges facing the Project.

astro-ph.IM↗

arXiv2022

Functional Data Analysis for Extracting the Intrinsic Dimensionality of Spectra: Application to Chemical Homogeneity in the Open Cluster M67

High-resolution spectroscopic surveys of the Milky Way have entered the Big Data regime and have opened avenues for solving outstanding questions in Galactic archaeology. However, exploiting their full potential is limited by complex systematics, whose characterization has not received much attention in modern spectroscopic analyses. In this work, we present a novel method to disentangle the component of spectral data space intrinsic to the stars from that due to systematics. Using functional principal component analysis on a sample of $18,933$ giant spectra from APOGEE, we find that the intrinsic structure above the level of observational uncertainties requires ${\approx}$10 functional principal components (FPCs). Our FPCs can reduce the dimensionality of spectra, remove systematics, and impute masked wavelengths, thereby enabling accurate studies of stellar populations. To demonstrate the applicability of our FPCs, we use them to infer stellar parameters and abundances of 28 giants in the open cluster M67. We employ Sequential Neural Likelihood, a simulation-based Bayesian inference method that learns likelihood functions using neural density estimators, to incorporate non-Gaussian effects in spectral likelihoods. By hierarchically combining the inferred abundances, we limit the spread of the following elements in M67: $\mathrm{Fe} \lesssim 0.02$ dex; $\mathrm{C} \lesssim 0.03$ dex; $\mathrm{O}, \mathrm{Mg}, \mathrm{Si}, \mathrm{Ni} \lesssim 0.04$ dex; $\mathrm{Ca} \lesssim 0.05$ dex; $\mathrm{N}, \mathrm{Al} \lesssim 0.07$ dex (at 68% confidence). Our constraints suggest a lack of self-pollution by core-collapse supernovae in M67, which has promising implications for the future of chemical tagging to understand the star formation history and dynamical evolution of the Milky Way.

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