skip to main content

Title: Localizing Sources of Variability in Crowded TESS Photometry

The Transiting Exoplanet Survey Satellite (TESS) has an exceptionally large plate scale of 21″ px−1, causing most TESS light curves to record the blended light of multiple stars. This creates a danger of misattributing variability observed by TESS to the wrong source, which would invalidate any analysis. We developed a method that can localize the origin of variability on the sky to better than one fifth of a pixel. Given measured frequencies of variability (e.g., from periodogram analysis), we show that the best-fit sinusoid amplitudes to raw light curves extracted from each pixel are distributed in the same way as light from the variable source. The primary assumption of this method is that other nearby stars are not variable at the same frequencies. Essentially, we are using the high frequency resolution of TESS to overcome limitations from its low spatial resolution. We have implemented our method in an open-source Python package,TESS_localize(, that determines the location of a variable source on the sky and the most likely Gaia source given TESS pixel data and a set of observed frequencies of variability. Our method utilizes models of the TESS pixel response function, and we characterize systematics in the more » residuals of fitting these models to data. We find that even stars more than three pixels outside a photometric aperture can produce significant contaminant signals in the extracted light curves. Given the ubiquity of source blending in TESS light curves, verifying the source of observed variability should be a standard step in TESS analyses.

« less
Publication Date:
Journal Name:
The Astronomical Journal
Page Range or eLocation-ID:
Article No. 141
DOI PREFIX: 10.3847
Sponsoring Org:
National Science Foundation
More Like this
  1. Abstract

    Given its large plate scale of 21″ pixel−1, analyses of data from the Transiting Exoplanet Survey Satellite (TESS) space telescope must be wary of source confusion from blended light curves, which creates the potential to attribute observed photometric variability to the wrong astrophysical source. We explore the impact of light curve contamination on the detection of fast yellow pulsating supergiant (FYPS) stars as a case study to demonstrate the importance of confirming the source of detected signals in the TESS pixel data. While some of the FYPS signals have already been attributed to contamination from nearby eclipsing binaries, others are suggested to be intrinsic to the supergiant stars. In this work, we carry out a detailed analysis of the TESS pixel data to fit the source locations of the dominant signals reported for 17 FYPS stars with the Python packageTESS_localize. We are able to reproduce the detections of these signals for 14 of these sources, obtaining consistent source locations for four. Three of these originate from contaminants, while the signal reported for BZ Tuc is likely a spurious frequency introduced to the light curve of this 127 day Cepheid by the data processing pipeline. Other signals are not significantmore »enough to be localized with our methods, or have long periods that are difficult to analyze given other TESS systematics. Since no localizable signals hold up as intrinsic pulsation frequencies of the supergiant targets, we argue that unambiguous detection of pulsational variability should be obtained before FYPS are considered a new class of pulsator.

    « less
  2. Abstract

    Stellar variability is a limiting factor for planet detection and characterization, particularly around active M-type stars. Here we revisit one of the most active stars from the Kepler mission, the M4 star GJ 1243, and use a sample of 414 flare events from 11 months of 1-minute cadence light curves to study the empirical morphology of white-light stellar flares. We use a Gaussian process detrending technique to account for the underlying starspots. We present an improved analytic, continuous flare template that is generated by stacking the flares onto a scaled time and amplitude and uses a Markov Chain Monte Carlo analysis to fit the model. Our model is defined using classical flare events but can also be used to model complex, multipeaked flare events. We demonstrate the utility of our model using TESS data at the 10-minute, 2-minute, and 20 s cadence modes. Our new flare model code is made publicly available on GitHub.5

  3. Context. The TESS space mission has recently demonstrated its great potential to discover new pulsating white dwarf and pre-white dwarf stars, and to detect periodicities with high precision in already known white-dwarf pulsators. Aims. We report the discovery of two new pulsating He-rich atmosphere white dwarfs (DBVs) and present a detailed asteroseismological analysis of three already known DBV stars employing observations collected by the TESS mission along with ground-based data. Methods. We processed and analyzed TESS observations of the three already known DBV stars PG 1351+489 (TIC 471015205), EC 20058−5234 (TIC 101622737), and EC 04207−4748 (TIC 153708460), and the two new DBV pulsators WDJ152738.4−50207.4 (TIC 150808542) and WD 1708−871 (TIC 451533898), whose variability is reported for the first time in this paper. We also carried out a detailed asteroseismological analysis using fully evolutionary DB white-dwarf models built considering the complete evolution of the progenitor stars. We constrained the stellar mass of three of these target stars by means of the observed period spacing, and derived a representative asteroseismological model using the individual periods, when possible. Results. We extracted frequencies from the TESS light curves of these DBV stars using a standard pre-whitening procedure to derive the potential pulsation frequencies. Allmore »the oscillation frequencies that we found are associated with g -mode pulsations with periods spanning from ∼190 s to ∼936 s. We find hints of rotation from frequency triplets in some of the targets, including the two new DBVs. For three targets, we find constant period spacings, which allowed us to infer their stellar masses and constrain the harmonic degree ℓ of the modes. We also performed period-to-period fit analyses and found an asteroseismological model for three targets, with stellar masses generally compatible with the spectroscopic masses. Obtaining seismological models allowed us to estimate the seismological distances and compare them with the precise astrometric distances measured with Gaia . We find a good agreement between the seismic and the astrometric distances for three stars (PG 1351+489, EC 20058-5234, and WD 1708-871); although, for the other two stars (EC 04207-4748 and WD J152738.4-50207), the discrepancies are substantial. Conclusions. The high-quality data from the TESS mission continue to provide important clues which can be used to help determine the internal structure of pulsating pre-white dwarf and white dwarf stars through the tools of asteroseismology.« less
  4. Context. Pulsation frequencies reveal the interior structures of white dwarf stars, shedding light on the properties of these compact objects that represent the final evolutionary stage of most stars. Two-minute cadence photometry from the Transiting Exoplanet Survey Satellite (TESS) records pulsation signatures from bright white dwarfs over the entire sky. Aims. As part of a series of first-light papers from TESS Asteroseismic Science Consortium Working Group 8, we aim to demonstrate the sensitivity of TESS data, by measuring pulsations of helium-atmosphere white dwarfs in the DBV instability strip, and what asteroseismic analysis of these measurements can reveal about their stellar structures. We present a case study of the pulsating DBV WD 0158−160 that was observed as TIC 257459955 with the two-minute cadence for 20.3 days in TESS Sector 3. Methods. We measured the frequencies of variability of TIC 257459955 with an iterative periodogram and prewhitening procedure. The measured frequencies were compared to calculations from two sets of white dwarf models to constrain the stellar parameters: the fully evolutionary models from LPCODE and the structural models from WDEC . Results. We detected and measured the frequencies of nine pulsation modes and eleven combination frequencies of WD 0158−160 to ∼0.01  μ Hzmore »precision. Most, if not all, of the observed pulsations belong to an incomplete sequence of dipole (ℓ = 1) modes with a mean period spacing of 38.1 ± 1.0 s. The global best-fit seismic models from both LPCODE and WDEC have effective temperatures that are ≳3000 K hotter than archival spectroscopic values of 24 100–25 500 K; however, cooler secondary solutions are found that are consistent with both the spectroscopic effective temperature and distance constraints from Gaia astrometry. Conclusions. Our results demonstrate the value of the TESS data for DBV white dwarf asteroseismology. The extent of the short-cadence photometry enables reliably accurate and extremely precise pulsation frequency measurements. Similar subsets of both the LPCODE and WDEC models show good agreement with these measurements, supporting that the asteroseismic interpretation of DBV observations from TESS is not dominated by the set of models used. However, given the sensitivity of the observed set of pulsation modes to the stellar structure, external constraints from spectroscopy and/or astrometry are needed to identify the best seismic solutions.« less
  5. Abstract

    In this work, we present classification results on early supernova light curves from SCONE, a photometric classifier that uses convolutional neural networks to categorize supernovae (SNe) by type using light-curve data. SCONE is able to identify SN types from light curves at any stage, from the night of initial alert to the end of their lifetimes. Simulated LSST SNe light curves were truncated at 0, 5, 15, 25, and 50 days after the trigger date and used to train Gaussian processes in wavelength and time space to produce wavelength–time heatmaps. SCONE uses these heatmaps to perform six-way classification between SN types Ia, II, Ibc, Ia-91bg, Iax, and SLSN-I. SCONE is able to perform classification with or without redshift, but we show that incorporating redshift information improves performance at each epoch. SCONE achieved 75% overall accuracy at the date of trigger (60% without redshift), and 89% accuracy 50 days after trigger (82% without redshift). SCONE was also tested on bright subsets of SNe (r< 20 mag) and produced 91% accuracy at the date of trigger (83% without redshift) and 95% five days after trigger (94.7% without redshift). SCONE is the first application of convolutional neural networks to the early-time photometricmore »transient classification problem. All of the data processing and model code developed for this paper can be found in the SCONE software package1

    located at (Qu 2021).

    « less