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Register a new userThe unpopular Package: a Data-driven Approach to De-trend TESS Full Frame Image Light Curves
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The majority of observed pixels on the Transiting Exoplanet Survey Satellite (TESS) are delivered in the form of full frame images (FFI). However, the FFIs contain systematic effects such as pointing jitter and scattered light from the Earth and Moon that must be removed before downstream analysis. We present unpopular, an open-source Python package to de-trend TESS FFI light curves based on the causal pixel model method. Under the assumption that shared flux variations across multiple distant pixels are likely to be systematics, unpopular removes these common (i.e., popular) trends by modeling the systematics in a given pixels light curve as a linear combination of light curves from many other distant pixels. To prevent overfitting we employ ridge regression and a train-and-test framework where the data points being de-trended are separated from those used to obtain the model coefficients. We also allow for simultaneous fitting with a polynomial model to capture any long-term astrophysical trends. We validate our method by de-trending different sources (e.g., supernova, tidal disruption event, exoplanet-hosting star, fast rotating star) and comparing our light curves to those obtained by other pipelines when appropriate. We also show that unpopular is able to preserve sector-length astrophysical signals, allowing for the extraction of multi-sector light curves from the FFI data. The unpopular source code and tutorials are freely available online.
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The Transiting Exoplanet Survey Satellite (TESS) mission measured light from stars in ~75% of the sky throughout its two year primary mission, resulting in millions of TESS 30-minute cadence light curves to analyze in the search for transiting exoplanets. To search this vast data trove for transit signals, we aim to provide an approach that is both computationally efficient and produces highly performant predictions. This approach minimizes the required human search effort. We present a convolutional neural network, which we train to identify planetary transit signals and dismiss false positives. To make a prediction for a given light curve, our network requires no prior transit parameters identified using other methods. Our network performs inference on a TESS 30-minute cadence light curve in ~5ms on a single GPU, enabling large scale archival searches. We present 181 new planet candidates identified by our network, which pass subsequent human vetting designed to rule out false positives. Our neural network model is additionally provided as open-source code for public use and extension.
FRB181228 was detected by the Molonglo Synthesis Radio Telescope (MOST) at a position and time coincident with Transiting Exoplanet Survey Satellite (TESS) observations, representing the first simultaneous multi-wavelength data collection for a Fast Radio Burst (FRB). The large imaged field-of-view of TESS allows a search over the uncertainty region produced by MOST. However, the TESS pixel scale of 21 and the Full Frame Image (FFI) cadence of 30 minutes is not optimal for the detection of an FOB with a possible millisecond duration. We search the TESS FFIs and find no events with a limiting TESS magnitude of 16, assuming a 30 minute event duration, corresponding to an optical flux density upper limit of approximately 2000 Jy for a ~ms signal duration, assuming no signal loss. In addition, the cosmic ray mitigation method for TESS significantly reduces its sensitivity to short timescale transients, which we quantify. We compare our results to the predictions of Yang, Zhang, and Wei (2019) and find that the upper limit is a factor of two thousand higher than the predicted maximum optical flux density. However, we find that if FRB181228 had occurred in the galaxy thought to host the nearest FRB detection to date (37 Mpc), an FOB may have been detectable by TESS. In the near future, when CHIME and ASKAP will detect hundreds to thousands of FRBs, TESS may be able to detect FOBs from those rare bright and nearby FRBs within this large population (if more sophisticated cosmic ray excision can be implemented).
Data from the Transiting Exoplanet Survey Satellite (TESS) has produced of order one million light curves at cadences of 120 s and especially 1800 s for every ~27-day observing sector during its two-year nominal mission. These data constitute a treasure trove for the study of stellar variability and exoplanets. However, to fully utilize the data in such studies a proper removal of systematic noise sources must be performed before any analysis. The TESS Data for Asteroseismology (TDA) group is tasked with providing analysis-ready data for the TESS Asteroseismic Science Consortium, which covers the full spectrum of stellar variability types, including stellar oscillations and pulsations, spanning a wide range of variability timescales and amplitudes. We present here the two current implementations for co-trending of raw photometric light curves from TESS, which cover different regimes of variability to serve the entire seismic community. We find performance in terms of commonly used noise statistics to meet expectations and to be applicable to a wide range of different intrinsic variability types. Further, we find that the correction of light curves from a full sector of data can be completed well within a few days, meaning that when running in steady-state our routines are able to process one sector before data from the next arrives. Our pipeline is open-source and all processed data will be made available on TASOC and MAST.
X-ray spectral fitting of astronomical sources requires convolving the intrinsic spectrum or model with the instrumental response. Standard forward modeling techniques have proven success in recovering the underlying physical parameters in moderate to high signal-to-noise regimes; however, they struggle to achieve the same level of accuracy in low signal-to-noise regimes. Additionally, the use of machine learning techniques on X-ray spectra requires access to the intrinsic spectrum. Therefore, the measured spectrum must be effectively deconvolved from the instrumental response. In this note, we explore numerical methods for inverting the matrix equation describing X-ray spectral convolution. We demonstrate that traditional methods are insufficient to recover the intrinsic X-ray spectrum and argue that a novel approach is required.
We report the discovery of two short-period massive giant planets from NASAs Transiting Exoplanet Survey Satellite (TESS). Both systems, TOI-558 (TIC 207110080) and TOI-559 (TIC 209459275), were identified from the 30-minute cadence Full Frame Images and confirmed using ground-based photometric and spectroscopic follow-up observations from TESSs Follow-up Observing Program Working Group. We find that TOI-558 b, which transits an F-dwarf ($M_{star}=1.349^{+0.064}_{-0.065} M_{odot}$, $R_{star}=1.496^{+0.042}_{-0.040} R_{odot}$, $T_{eff}=6466^{+95}_{-93}$ K, age $1.79^{+0.91}_{-0.73}$ Gyr) with an orbital period of 14.574 days, has a mass of $3.61pm0.15 M_J$, a radius of $1.086^{+0.041}_{-0.038} R_J$, and an eccentric (e=$0.300^{+0.022}_{-0.020}$) orbit. TOI-559 b transits a G-dwarf ($M_{star}=1.026pm0.057 M_{odot}$, $R_{star}=1.233^{+0.028}_{-0.026} R_{odot}$, $T_{eff}=5925^{+85}_{-76}$ K, age $1.79^{+0.91}_{-0.73}$ Gyr) in an eccentric (e=$0.151pm0.011$) 6.984-day orbit with a mass of $6.01^{+0.24}_{-0.23} M_J$ and a radius of $1.091^{+0.028}_{-0.025} R_J$. Our spectroscopic follow-up also reveals a long-term radial velocity trend for TOI-559, indicating a long-period companion. The statistically significant orbital eccentricity measured for each system suggests that these planets migrated to their current location through dynamical interactions. Interestingly, both planets are also massive ($>3 M_J$), adding to the population of massive hot Jupiters identified by TESS. Prompted by these new detections of high-mass planets, we analyzed the known mass distribution of hot Jupiters but find no significant evidence for multiple populations. TESS should provide a near magnitude-limited sample of transiting hot Jupiters, allowing for future detailed population studies.
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