arXiv subjects

Eric D. Feigelson

Publications and source records attributed to Eric D. Feigelson.

At least 37 records · Page 2Linked to original sources

arXiv2022

The Effect of Molecular Cloud Properties on the Kinematics of Stars Formed in the Trifid Region

The dynamical states of molecular clouds may affect the properties of the stars they form. In the vicinity of the Trifid Nebula ($d=1180\pm25$ pc), the main star cluster (Trifid Main) lies within an expanding section of the molecular cloud; however, ~0.3 deg to the north (Trifid North), the cloud's velocity structure is more tranquil. We acquired a Chandra X-ray observation to identify pre-main-sequence stars in Trifid North, complementing a previous observation of Trifid Main. In Trifid North, we identified 51 candidate pre-main-sequence stars, of which 13 are high-confidence Trifid members based on Gaia EDR3 parallaxes and proper motions. We also re-analyzed membership of Trifid Main and separated out multiple background stellar associations. Trifid North represents a stellar population ~10% as rich as Trifid Main that formed in a separate part of the cloud. The 1D stellar velocity dispersion in Trifid North ($0.6\pm0.2$ km/s) is three times lower than in Trifid Main ($1.9\pm0.2$ km/s). Furthermore, in Trifid Main, proper motions indicate that the portion of the star cluster superimposed on the optical nebula is expanding. Expansion of the HII region around the O-star HD 164492A, and the resulting gas expulsion, can explain both the motions of the stars and gas in Trifid Main. Contrary to previous studies, we find no evidence that a cloud-cloud collision triggered star formation in the region.

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arXiv2022

DarkMix: Mixture Models for the Detection and Characterization of Dark Matter Halos

Dark matter simulations require statistical techniques to properly identify and classify their halos and structures. Nonparametric solutions provide catalogs of these structures but lack the additional learning of a model-based algorithm and might misclassify particles in merging situations. With mixture models, we can simultaneously fit multiple density profiles to the halos that are found in a dark matter simulation. In this work, we use the Einasto profile (Einasto 1965, 1968, 1969) to model the halos found in a sample of the Bolshoi simulation (Klypin et al. 2011), and we obtain their location, size, shape and mass. Our code is implemented in the R statistical software environment and can be accessed on https://github.com/LluisHGil/darkmix.

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arXiv2022

Evolution of X-ray Activity in <25 Myr Old Pre-Main Sequence Stars

Measuring the evolution of X-ray emission from pre-main sequence (PMS) stars gives insight into two issues: the response of magnetic dynamo processes to changes in interior structure and the effects of high-energy radiation on protoplanetary disks and primordial planetary atmospheres. We present a sample of 6,003 stars with ages 7-25Myr in ten nearby open clusters from Chandra X-ray and Gaia-EDR3 surveys. Combined with previous results in large samples of younger (<5Myr) stars in MYStIX and SFiNCs star forming regions, mass-stratified activity-age relations are derived for early phases of stellar evolution. X-ray luminosity (Lx) is constant during the first few Myr, possibly due to the presence of extended X-ray coronas insensitive to temporal changes in stellar size. Lx then decays during the 7-25Myr period, more rapidly as stellar mass increases. This decay is interpreted as decreasing efficiency of the alpha^2 dynamo as radiative cores grow and a solar-type alpha-Omega dynamo emerges. For more massive 3.5-7Mo fully radiative stars, the X-ray emission plummets indicating lack of an effective magnetic dynamo. The findings provide improved measurements of high energy radiation effects on circumstellar material, first the protoplanetary disk and then the atmospheres of young planets. The observed X-ray luminosities can be so high that an inner Earth-mass rocky, unmagnetized planet around a solar-mass PMS star might lose its primary and secondary atmospheres within a few-several million years. PMS X-ray emission may thus have a significant impact on evolution of early planetary atmospheres and the conditions promoting the rise of habitability.

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arXiv2022

Incorporating Measurement Error in Astronomical Object Classification

Most general-purpose classification methods, such as support-vector machine (SVM) and random forest (RF), fail to account for an unusual characteristic of astronomical data: known measurement error uncertainties. In astronomical data, this information is often given in the data but discarded because popular machine learning classifiers cannot incorporate it. We propose a simulation-based approach that incorporates heteroscedastic measurement error into existing classification method to better quantify uncertainty in classification. The proposed method first simulates perturbed realizations of the data from a Bayesian posterior predictive distribution of a Gaussian measurement error model. Then, a chosen classifier is fit to each simulation. The variation across the simulations naturally reflects the uncertainty propagated from the measurement errors in both labeled and unlabeled data sets. We demonstrate the use of this approach via two numerical studies. The first is a thorough simulation study applying the proposed procedure to SVM and RF, which are well-known hard and soft classifiers, respectively. The second study is a realistic classification problem of identifying high-$z$ $(2.9 \leq z \leq 5.1)$ quasar candidates from photometric data. The data are from merged catalogs of the Sloan Digital Sky Survey, the $Spitzer$ IRAC Equatorial Survey, and the $Spitzer$-HETDEX Exploratory Large-Area Survey. The proposed approach reveals that out of 11,847 high-$z$ quasar candidates identified by a random forest without incorporating measurement error, 3,146 are potential misclassifications with measurement error. Additionally, out of $1.85$ million objects not identified as high-$z$ quasars without measurement error, 936 can be considered new candidates with measurement error.

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arXiv2022

Time domain methods for X-ray and gamma-ray astronomy

A variety of statistical methods for understanding variability in the time domain for low count rate X-ray and gamma-ray sources are explored. Variability can be detected using nonparametric (Anderson-Darling and overdispersion tests) and parametric (sequential likelihood-based tests) tools. Once detected, variability can be characterized by nonparametric (autocorrelation function, structure function,wavelet analysis) and parametric (multiple change point model such as Bayesian Blocks, integer autoregressive models, C-statistic and Poisson regression) methods. New multidimensional variability detection approaches are outlined. Software packages designed for high energy data analysis are deficient but tools are available in the R statistical software environment. Most of the methods presented here are not commonly used in high energy astronomy.

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arXiv2021

A hot Mars-sized exoplanet transiting an M dwarf

We validate the planetary nature of an ultra-short period planet orbiting the M dwarf KOI-4777. We use a combination of space-based photometry from Kepler, high-precision, near-infrared Doppler spectroscopy from the Habitable-zone Planet Finder, and adaptive optics imaging to characterize this system. KOI-4777.01 is a Mars-sized exoplanet ($\mathrm{R}_{p}=0.51 \pm 0.03R_{\oplus}$) orbiting the host star every 0.412-days ($\sim9.9$-hours). This is the smallest validated ultra-short period planet known and we see no evidence for additional massive companions using our HPF RVs. We constrain the upper $3σ$ mass to $M_{p}<0.34~\mathrm{M_\oplus}$ by assuming the planet is less dense than iron. Obtaining a mass measurement for KOI-4777.01 is beyond current instrumental capabilities.

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arXiv2021

X-ray Super-Flares From Pre-Main Sequence Stars: Flare Modeling

Getman et al. (2021) reports the discovery, energetics, frequencies, and effects on environs of $>1000$ X-ray super-flares with X-ray energies $E_X \sim 10^{34}-10^{38}$~erg from pre-main sequence (PMS) stars identified in the $Chandra$ MYStIX and SFiNCs surveys. Here we perform detailed plasma evolution modeling of $55$ bright MYStIX/SFiNCs super-flares from these events. They constitute a large sample of the most powerful stellar flares analyzed in a uniform fashion. They are compared with published X-ray super-flares from young stars in the Orion Nebula Cluster, older active stars, and the Sun. Several results emerge. First, the properties of PMS X-ray super-flares are independent of the presence or absence of protoplanetary disks inferred from infrared photometry, supporting the solar-type model of PMS flaring magnetic loops with both footpoints anchored in the stellar surface. Second, most PMS super-flares resemble solar long duration events (LDEs) that are associated with coronal mass ejections. Slow rise PMS super-flares are an interesting exception. Third, strong correlations of super-flare peak emission measure and plasma temperature with the stellar mass are similar to established correlations for the PMS X-ray emission composed of numerous smaller flares. Fourth, a new correlation of loop geometry is linked to stellar mass; more massive stars appear to have thicker flaring loops. Finally, the slope of a long-standing relationship between the X-ray luminosity and magnetic flux of various solar-stellar magnetic elements appears steeper in PMS super-flares than for solar events.

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arXiv2021

X-ray Super-Flares From Pre-Main Sequence Stars: Flare Energetics And Frequency

Solar-type stars exhibit their highest levels of magnetic activity during their early convective pre-main sequence (PMS) phase of evolution. The most powerful PMS flares, super-flares and mega-flares, have peak X-ray luminosities of $\log(L_X)=30.5-34.0$~erg~s$^{-1}$ and total energies $\log(E_X)=34-38$~erg. Among $>24,000$ X-ray selected young ($t<5$~Myr) members of 40 nearby star-forming regions from our earlier $Chandra$ MYStIX and SFiNCs surveys, we identify and analyze a well-defined sample of 1,086 X-ray super-flares and mega-flares, the largest sample ever studied. Most are considerably more powerful than optical/X-ray super-flares detected on main sequence stars. This study presents energy estimates of these X-ray flares and the properties of their host stars. These events are produced by young stars of all masses over evolutionary stages ranging from protostars to diskless stars, with the occurrence rate positively correlated with stellar mass. Flare properties are indistinguishable for disk-bearing and diskless stars indicating star-disk magnetic fields are not involved. A slope $α\simeq2$ in the flare energy distributions $dN/dE_X \propto E_X^{-α}$ is consistent with those of optical/X-ray flaring from older stars and the Sun. Mega-flares ($\log(E_X) > 36.2$~erg) from solar-mass stars have occurrence rate of $1.7_{-0.6}^{+1.0}$ flares/star/year and contribute at least $10-20$\% to the total PMS X-ray energetics. These explosive events may have important astrophysical effects on protoplanetary disk photoevaporation, ionization of disk gas, production of spallogenic radionuclides in disk solids, and hydrodynamic escape of young planetary atmospheres. Our following paper details plasma and magnetic loop modeling of the $>50$ brightest X-ray mega-flares.

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arXiv2020

21st Century Statistical and Computational Challenges in Astrophysics

Modern astronomy has been rapidly increasing our ability to see deeper into the universe, acquiring enormous samples of cosmic populations. Gaining astrophysical insights from these datasets requires a wide range of sophisticated statistical and machine learning methods. Long-standing problems in cosmology include characterization of galaxy clustering and estimation of galaxy distances from photometric colors. Bayesian inference, central to linking astronomical data to nonlinear astrophysical models, addresses problems in solar physics, properties of star clusters, and exoplanet systems. Likelihood-free methods are growing in importance. Detection of faint signals in complicated noise is needed to find periodic behaviors in stars and detect explosive gravitational wave events. Open issues concern treatment of heteroscedastic measurement errors and understanding probability distributions characterizing astrophysical systems. The field of astrostatistics needs increased collaboration with statisticians in the design and analysis stages of research projects, and to jointly develop new statistical methodologies. Together, they will draw more astrophysical insights into astronomical populations and the cosmos itself.

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arXiv2019

Algorithms and Statistical Models for Scientific Discovery in the Petabyte Era

The field of astronomy has arrived at a turning point in terms of size and complexity of both datasets and scientific collaboration. Commensurately, algorithms and statistical models have begun to adapt --- e.g., via the onset of artificial intelligence --- which itself presents new challenges and opportunities for growth. This white paper aims to offer guidance and ideas for how we can evolve our technical and collaborative frameworks to promote efficient algorithmic development and take advantage of opportunities for scientific discovery in the petabyte era. We discuss challenges for discovery in large and complex data sets; challenges and requirements for the next stage of development of statistical methodologies and algorithmic tool sets; how we might change our paradigms of collaboration and education; and the ethical implications of scientists' contributions to widely applicable algorithms and computational modeling. We start with six distinct recommendations that are supported by the commentary following them. This white paper is related to a larger corpus of effort that has taken place within and around the Petabytes to Science Workshops (https://petabytestoscience.github.io/).

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arXiv2019

IRAS 09002-4732: A Laboratory for the Formation of Rich Stellar Clusters

IRAS 09002-4732 is a poorly studied embedded cluster of stars in the Vela Molecular Ridge at a distance of 1.7kpc. Deep observations with the Chandra X-ray Observatory, combined with existing optical and infrared surveys, produce a catalog of 441 probable pre-main sequence members of the region. The stellar spatial distribution has two components: most stars reside in a rich, compact, elliptical cluster, but a minority reside within a molecular filament several parsecs long that straddles the cluster. The filament has active distributed star formation with dozens of unclustered protostars. The cluster pre-main sequence population is $\leq 0.8$ Myr old and deeply embedded; its most massive member is extremely young producing an ultracompact H II region. The cluster total population deduced from the X-ray luminosity function is surprisingly rich, twice that of the Orion Nebula Cluster. The cluster core is remarkably dense where strong N-body interactions should be occurring; its Initial Mass Function may be deficient in massive stars. We infer that IRAS 09002-4732 is a rare case where a rich cluster is forming today in a molecular filament, consistent with astrophysical models of cluster formation in clouds that involve the hierarchical formation and merging of groups in molecular filaments.

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arXiv2019

Advanced Astrophysics Discovery Technology in the Era of Data Driven Astronomy

Experience suggests that structural issues in how institutional Astrophysics approaches data-driven science and the development of discovery technology may be hampering the community's ability to respond effectively to a rapidly changing environment in which increasingly complex, heterogeneous datasets are challenging our existing information infrastructure and traditional approaches to analysis. We stand at the confluence of a new epoch of multimessenger science, remote co-location of data and processing power and new observing strategies based on miniaturized spacecraft. Significant effort will be required by the community to adapt to this rapidly evolving range of possible discovery moduses. In the suggested creation of a new Astrophysics element, Advanced Astrophysics Discovery Technology, we offer an affirmative solution that places the visibility of discovery technologies at a level that we suggest is fully commensurate with their importance to the future of the field.

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arXiv2019

AutoRegressive Planet Search: Application to the Kepler Mission

The 4-year light curves of 156,717 stars observed with NASA's Kepler mission are analyzed using the AutoRegressive Planet Search (ARPS) methodology described by Caceres et al. (2019). The three stages of processing are: maximum likelihood ARIMA modeling of the light curves to reduce stellar brightness variations; constructing the Transit Comb Filter periodogram to identify transit-like periodic dips in the ARIMA residuals; Random Forest classification trained on Kepler Team confirmed planets using several dozen features from the analysis. Orbital periods between 0.2 and 100 days are examined. The result is a recovery of 76% of confirmed planets, 97% when period and transit depth constraints are added. The classifier is then applied to the full Kepler dataset; 1,004 previously noticed and 97 new stars have light curve criteria consistent with the confirmed planets, after subjective vetting removes clear False Alarms and False Positive cases. The 97 Kepler ARPS Candidate Transits mostly have periods $P<10$ days; many are UltraShort Period hot planets with radii $<1$% of the host star. Extensive tabular and graphical output from the ARPS time series analysis is provided to assist in other research relating to the Kepler sample.

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arXiv2019

AutoRegressive Planet Search: Methodology

The detection of periodic signals from transiting exoplanets is often impeded by extraneous aperiodic photometric variability, either intrinsic to the star or arising from the measurement process. Frequently, these variations are autocorrelated wherein later flux values are correlated with previous ones. In this work, we present the methodology of the Autoregessive Planet Search (ARPS) project which uses Autoregressive Integrated Moving Average (ARIMA) and related statistical models that treat a wide variety of stochastic processes, as well as nonstationarity, to improve detection of new planetary transits. Providing a time series is evenly spaced or can be placed on an evenly spaced grid with missing values, these low-dimensional parametric models can prove very effective. We introduce a planet-search algorithm to detect periodic transits in the residuals after the application of ARIMA models. Our matched-filter algorithm, the Transit Comb Filter (TCF), is closely related to the traditional Box-fitting Least Squares and provides an analogous periodogram. Finally, if a previously identified or simulated sample of planets is available, selected scalar features from different stages of the analysis -- the original light curves, ARIMA fits, TCF periodograms, and folded light curves -- can be collectively used with a multivariate classifier to identify promising candidates while efficiently rejecting false alarms. We use Random Forests for this task, in conjunction with Receiver Operating Characteristic (ROC) curves, to define discovery criteria for new, high fidelity planetary candidates. The ARPS methodology can be applied to both evenly spaced satellite light curves and densely cadenced ground-based photometric surveys.

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arXiv2019

AutoRegressive Planet Search: Feasibility Study for Irregular Time Series

Sensitive signal processing methods are needed to detect transiting planets from ground-based photometric surveys. Caceres et al. (2019) show that the AutoRegressive Planet Search (ARPS) method --- a combination of autoregressive integrated moving average (ARIMA) parametric modeling, a new Transit Comb Filter (TCF) periodogram, and machine learning classification --- is effective when applied to evenly spaced light curves from space-based missions. We investigate here whether ARIMA and TCF will be effective for ground-based survey light curves that are often sparsely sampled with high noise levels from atmospheric and instrumental conditions. The ARPS procedure is applied to selected light curves with strong planetary signals from the Kepler mission that have been altered to simulate the conditions of ground-based exoplanet surveys. Typical irregular cadence patterns are used from the HATSouth survey. We also evaluate recovery of known planets from HATSouth. Simulations test transit signal recovery as a function of cadence pattern and duration, stellar magnitude, planet orbital period and transit depth. Detection rates improve for shorter periods and deeper transits. The study predicts that the ARPS methodology will detect planets with $\gtrsim 0.1$\% transit depth and periods $\lesssim 40$ days in HATSouth stars brighter than $\sim$15 mag. ARPS methodology is therefore promising for planet discovery from ground-based exoplanet surveys with sufficiently dense cadence patterns.

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arXiv2019

Mixture Models in Astronomy

Mixture models combine multiple components into a single probability density function. They are a natural statistical model for many situations in astronomy, such as surveys containing multiple types of objects, cluster analysis in various data spaces, and complicated distribution functions. This chapter in the CRC Handbook of Mixture Analysis is concerned with astronomical applications of mixture models for cluster analysis, classification, and semi-parametric density estimation. We present several classification examples from the literature, including identification of a new class, analysis of contaminants, and overlapping populations. In most cases, mixtures of normal (Gaussian) distributions are used, but it is sometimes necessary to use different distribution functions derived from astrophysical experience. We also address the use of mixture models for the analysis of spatial distributions of objects, like galaxies in redshift surveys or young stars in star-forming regions. In the case of galaxy clustering, mixture models may not be the optimal choice for understanding the homogeneous and isotropic structure of voids and filaments. However, we show that mixture models, using astrophysical models for star clusters, may provide a natural solution to the problem of subdividing a young stellar population into subclusters. Finally, we explore how mixture models can be used for mathematically advanced modeling of data with heteroscedastic uncertainties or missing values, providing two example algorithms, the measurement error regression model of Kelly (2007) and the Extreme Deconvolution model of Bovy et al. (2011). The challenges presented by astronomical science, aided by the public availability of catalogs from major surveys and missions, are a rich area for collaboration between statisticians and astronomers.

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arXiv2019

Characterizing Variable Stars in a Single Night with LSST

Stars exhibit a bewildering variety of variable behaviors ranging from explosive magnetic flares to stochastically changing accretion to periodic pulsations or rotations. The principal LSST surveys will have cadences too sparse and irregular to capture most of these phenomena. A novel idea is proposed here to observe a single Galactic field, rich in unobscured stars, in a continuous sequence of $\sim 15$ second exposures for one long winter night in a single photometric band. The result will be a unique dataset of $\sim 1$ million regularly spaced stellar lightcurves. The lightcurves will gives a particularly comprehensive collection of dM star variability. A powerful array of statistical procedures can be applied to the ensemble of lightcurves from the long-standing fields of time series analysis, signal processing and econometrics. Dozens of `features' describing the variability can be extracted and subject to machine learning classification, giving a unique authoritative objective classification of rapidly variable stars. The most effective features can then inform the wider LSST community on the best approaches to variable star identification and classification from the sparse, irregular cadences that dominate the LSST project.

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arXiv2019

Autoregressive Times Series Methods for Time Domain Astronomy

Celestial objects exhibit a wide range of variability in brightness at different wavebands. Surprisingly, the most common methods for characterizing time series in statistics -- parametric autoregressive modeling -- is rarely used to interpret astronomical light curves. We review standard ARMA, ARIMA and ARFIMA (autoregressive moving average fractionally integrated) models that treat short-memory autocorrelation, long-memory $1/f^α$ `red noise', and nonstationary trends. Though designed for evenly spaced time series, moderately irregular cadences can be treated as evenly-spaced time series with missing data. Fitting algorithms are efficient and software implementations are widely available. We apply ARIMA models to light curves of four variable stars, discussing their effectiveness for different temporal characteristics. A variety of extensions to ARIMA are outlined, with emphasis on recently developed continuous-time models like CARMA and CARFIMA designed for irregularly spaced time series. Strengths and weakness of ARIMA-type modeling for astronomical data analysis and astrophysical insights are reviewed.

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