Don't Pay Attention to the Noise: Learning Self-supervised Representations of Light Curves with a Denoising Time Series Transformer

Astrophysical light curves are particularly challenging data objects due to the intensity and variety of noise contaminating them. Yet, despite the astronomical volumes of light curves available, the majority of algorithms used to process them are still operating on a per-sample basis. To remedy this, we propose a simple Transformer model –called Denoising Time Series Transformer (DTST)– and show that it excels at removing the noise and outliers in datasets of time series when trained with a masked objective, even when no clean targets are available. Moreover, the use of self-attention enables rich and illustrative queries into the learned representations. We present experiments on real stellar light curves from the Transiting Exoplanet Space Satellite (TESS), showing advantages of our approach compared to traditional denoising techniques1

Integrating Light Curve and Atmospheric Modeling of Transiting Exoplanets

Spectral retrieval techniques are currently our best tool to interpret the observed exoplanet atmospheric data. Said techniques retrieve the optimal atmospheric components and parameters by identifying the best fit to an observed transmission/emission spectrum. Over the past decade, our understanding of remote worlds in our galaxy has flourished thanks to the use of increasingly sophisticated spectral retrieval techniques and the collective effort of the community working on exoplanet atmospheric models. A new generation of instruments in space and from the ground is expected to deliver higher quality data in the next decade; it is therefore paramount to upgrade current models and improve their reliability, their completeness, and the numerical speed with which they can be run. In this paper, we address the issue of reliability of the results provided by retrieval models in the presence of systematics of unknown origin. More specifically, we demonstrate that if we fit directly individual light curves at different wavelengths (L-retrieval), instead of fitting transit or eclipse depths, as it is currently done (S-retrieval), the said methodology is more sensitive against astrophysical and instrumental noise. This new approach is tested, in particular, when discrepant simulated observations from Hubble Space Telescope/Wide Field Camera 3 and Spitzer/IRAC are combined. We find that while S-retrievals converge to an incorrect solution without any warning, L-retrievals are able to flag potential discrepancies between the data sets

Peeking inside the Black Box: Interpreting Deep-learning Models for Exoplanet Atmospheric Retrievals

Deep-learning algorithms are growing in popularity in the field of exoplanetary science due to their ability to model highly nonlinear relations and solve interesting problems in a data-driven manner. Several works have attempted to perform fast retrievals of atmospheric parameters with the use of machine-learning algorithms like deep neural networks (DNNs). Yet, despite their high predictive power, DNNs are also infamous for being "black boxes." It is their apparent lack of explainability that makes the astrophysics community reluctant to adopt them. What are their predictions based on? How confident should we be in them? When are they wrong, and how wrong can they be? In this work, we present a number of general evaluation methodologies that can be applied to any trained model and answer questions like these. In particular, we train three different popular DNN architectures to retrieve atmospheric parameters from exoplanet spectra and show that all three achieve good predictive performance. We then present an extensive analysis of the predictions of DNNs, which can inform us–among other things–of the credibility limits for atmospheric parameters for a given instrument and model. Finally, we perform a perturbation-based sensitivity analysis to identify to which features of the spectrum the outcome of the retrieval is most sensitive. We conclude that, for different molecules, the wavelength ranges to which the DNNs predictions are most sensitive do indeed coincide with their characteristic absorption regions. The methodologies presented in this work help to improve the evaluation of DNNs and to grant interpretability to their predictions

Peeking inside the Black Box: Interpreting Deep-learning Models for Exoplanet Atmospheric Retrievals

Deep-learning algorithms are growing in popularity in the field of exoplanetary science due to their ability to model highly nonlinear relations and solve interesting problems in a data-driven manner. Several works have attempted to perform fast retrievals of atmospheric parameters with the use of machine-learning algorithms like deep neural networks (DNNs). Yet, despite their high predictive power, DNNs are also infamous for being "black boxes." It is their apparent lack of explainability that makes the astrophysics community reluctant to adopt them. What are their predictions based on? How confident should we be in them? When are they wrong, and how wrong can they be? In this work, we present a number of general evaluation methodologies that can be applied to any trained model and answer questions like these. In particular, we train three different popular DNN architectures to retrieve atmospheric parameters from exoplanet spectra and show that all three achieve good predictive performance. We then present an extensive analysis of the predictions of DNNs, which can inform us–among other things–of the credibility limits for atmospheric parameters for a given instrument and model. Finally, we perform a perturbation-based sensitivity analysis to identify to which features of the spectrum the outcome of the retrieval is most sensitive. We conclude that, for different molecules, the wavelength ranges to which the DNNs predictions are most sensitive do indeed coincide with their characteristic absorption regions. The methodologies presented in this work help to improve the evaluation of DNNs and to grant interpretability to their predictions

On the Compatibility of Ground-based and Space-based Data: WASP-96 b, an Example

The study of exoplanetary atmospheres relies on detecting minute changes in the transit depth at different wavelengths. To date, a number of ground- and space-based instruments have been used to obtain transmission spectra of exoplanets in different spectral bands. One common practice is to combine observations from different instruments in order to achieve a broader wavelength coverage. We present here two inconsistent observations of WASP-96 b, one by the Hubble Space Telescope (HST) and the other by the Very Large Telescope (VLT). We present two key findings in our investigation: (1) a strong water signature is detected via the HST WFC3 observations and (2) a notable offset in transit depth (>1100 ppm) can be seen when the ground-based and space-based observations are combined. The discrepancy raises the question of whether observations from different instruments could indeed be combined. We attempt to align the observations by including an additional parameter in our retrieval studies but are unable to definitively ascertain that the aligned observations are indeed compatible. The case of WASP-96 b signals that compatibility of instruments should not be assumed. While wavelength overlaps between instruments can help, it should be noted that combining data sets remains risky business. The difficulty of combining observations also strengthens the need for next-generation instruments that possess broader spectral coverage

Pushing the Limits of Exoplanet Discovery via Direct Imaging with Deep Learning

Further advances in exoplanet detection and characterisation require sampling a diverse population of extrasolar planets. One technique to detect these distant worlds is through the direct detection of their thermal emission. The so-called direct imaging technique, is suitable for observing young planets far from their star. These are very low signal-to-noise-ratio (SNR) measurements and limited ground truth hinders the use of supervised learning approaches. In this paper, we combine deep generative and discriminative models to bypass the issues arising when directly training on real data. We use a Generative Adversarial Network to obtain a suitable dataset for training Convolutional Neural Network classifiers to detect and locate planets across a wide range of SNRs. Tested on artificial data, our detectors exhibit good predictive performance and robustness across SNRs. To demonstrate the limits of the detectors, we provide maps of the precision and recall of the model per pixel of the input image. On real data, the models can re-confirm bright source detections

ARES. III. Unveiling the Two Faces of KELT-7 b with HST WFC3*

We present the analysis of the hot-Jupiter KELT-7 b using transmission and emission spectroscopy from the Hubble Space Telescope, both taken with the Wide Field Camera 3. Our study uncovers a rich transmission spectrum that is consistent with a cloud-free atmosphere and suggests the presence of H_{2}O and H^{−}. In contrast, the extracted emission spectrum does not contain strong absorption features and, although it is not consistent with a simple blackbody, it can be explained by a varying temperature–pressure profile, collision induced absorption, and H^{-}. KELT-7 b had also been studied with other space-based instruments and we explore the effects of introducing these additional data sets. Further observations with Hubble, or the next generation of space-based telescopes, are needed to allow for the optical opacity source in transmission to be confirmed and for molecular features to be disentangled in emission

Pyelonephritis in slaughter pigs and sows: Morphological characterization and aspects of pathogenesis and aetiology

<p>Abstract</p> <p>Background</p> <p>Pyelonephritis is a serious disease in pig production that needs to be further studied. The purpose of this study was to describe the morphology, investigate the pathogenesis, and evaluate the aetiological role of <it>Escherichia coli </it>in pyelonephritis in slaughtered pigs by concurrent bacteriological, gross and histopathological examinations.</p> <p>Methods</p> <p>From Danish abattoirs, kidneys and corresponding lymph nodes from 22 slaughtered finishing pigs and 26 slaughtered sows with pyelonephritis were collected and evaluated by bacteriology and pathology. Based on gross lesions, each kidney (lesion) was grouped as acute, chronic, chronic active, or normal and their histological inflammatory stage was determined as normal (0), acute (1), sub-acute (2), chronic active (3), or chronic (4). Immunohistochemical identification of neutrophils, macrophages, T-lymphocytes, B-lymphocytes, plasma cells, <it>E. coli </it>and Tamm-Horsfall protein (THP) in renal sections was performed. The number of <it>E. coli </it>and the proportion of immunohistochemically visualized leukocytes out of the total number of infiltrating leukocytes were scored semi-quantitatively.</p> <p>Results</p> <p>Lesions in finishing pigs and sows were similar. Macroscopically, multiple unevenly distributed foci of inflammation mostly affecting the renal poles were observed. Histologically, tubulointerstitial infiltration with neutrophils and mononuclear cells and tubular destruction was the main findings. The significant highest scores of L1 antigen⁺neutrophils were in inflammatory stage 1 while the significant highest scores of CD79αcy⁺B-lymphocytes, IgG⁺and IgA⁺plasma cells were in stage 3 or 4. Neutrophils were the dominant leukocytes in stage 1 while CD3ε⁺T-lymphocytes dominated in stage 2, 3 and 4. Interstitially THP was seen in 82% and 98% of kidneys with pyelonephritis from finishing pigs and sows, respectively. <it>E. coli </it>was demonstrated in monoculture and/or identified by immunohistochemistry in relation to inflammation in four kidneys from finishing pigs and in 34 kidneys from sows.</p> <p>Conclusions</p> <p><it>E. coli </it>played a significant role in the aetiology of pyelonephritis. Neutrophils were involved in the first line of defence. CD3ε⁺T-lymphocytes were involved in both the acute and chronic inflammatory response while a humoral immune response was most pronounced in later inflammatory stages. The observed renal lesions correspond with an ascending bacterial infection with presence of intra-renal reflux.</p

The male fetal biomarker INSL3 reveals substantial hormone exchange between fetuses in early pig gestation

The peptide hormone INSL3 is uniquely produced by the fetal testis to promote the transabdominal phase of testicular descent. Because it is fetal sex specific, and is present in only very low amounts in the maternal circulation, INSL3 acts as an ideal biomarker with which to monitor the movement of fetal hormones within the pregnant uterus of a polytocous species, the pig. INSL3 production by the fetal testis begins at around GD30. At GD45 of the ca.114 day gestation, a time at which testicular descent is promoted, INSL3 evidently moves from male to female allantoic compartments, presumably impacting also on the female fetal circulation. At later time-points (GD63, GD92) there is less inter-fetal transfer, although there still appears to be significant INSL3, presumably of male origin, in the plasma of female fetuses. This study thus provides evidence for substantial transfer of a peptide hormone between fetuses, and probably also across the placenta, emphasizing the vulnerability of the fetus to extrinsic hormonal influences within the uterus