Now showing items 6837-6856 of 12955


      Kastner, Joel H.; Principe, David A.; Punzi, Kristina; Stelzer, Beate; Gorti, Uma; Pascucci, Ilaria; Argiroffi, Costanza; Univ Arizona, Lunar & Planetary Lab (IOP PUBLISHING LTD, 2016-06-13)
      To investigate the potential connection between the intense X-ray emission from young low-mass stars and the lifetimes of their circumstellar planet-forming disks, we have compiled the X-ray luminosities (L-X) of M stars in the similar to 8 Myr old TW Hya Association (TWA) for which X-ray data are presently available. Our investigation includes analysis of archival Chandra data for the TWA binary systems TWA 8, 9, and 13. Although our study suffers from poor statistics for stars later than M3, we find a trend of decreasing L-X/L-bol with decreasing T-eff for TWA M stars, wherein the earliest-type (M0-M2) stars cluster near log(L-X/L-bol) approximate to -3.0 and then log(L-X/L-bol) decreases, and its distribution broadens, for types M4 and later. The fraction of TWA stars that display evidence for residual primordial disk material also sharply increases in this same (mid-M) spectral type regime. This apparent anticorrelation between the relative X-ray luminosities of low-mass TWA stars and the longevities of their circumstellar disks suggests that primordial disks orbiting early-type M stars in the TWA have dispersed rapidly as a consequence of their persistent large X-ray fluxes. Conversely, the disks orbiting the very lowest-mass pre-MS stars and pre-MS brown dwarfs in the Association may have survived because their X-ray luminosities and, hence, disk photoevaporation rates are very low to begin with, and then further decline relatively early in their pre-MS evolution.
    • The M101 Satellite Luminosity Function and the Halo–Halo Scatter among Local Volume Hosts

      Bennet, P.; Sand, D. J.; Crnojević, D.; Spekkens, K.; Karunakaran, A.; Zaritsky, D.; Mutlu-Pakdil, B.; Univ Arizona, Steward Observ (IOP PUBLISHING LTD, 2019-11-11)
      We have obtained deep Hubble Space Telescope (HST) imaging of 19 dwarf galaxy candidates in the vicinity of M101. Advanced Camera for Surveys HST photometry for two of these objects showed resolved stellar populations and tip of the red giant branch derived distances (D & xfffd;?& xfffd;7 Mpc) consistent with M101 group membership. The remaining 17 were found to have no resolved stellar populations, meaning they are either part of the background NGC 5485 group or are distant low surface brightness (LSB) galaxies. It is noteworthy that many LSB objects that had previously been assumed to be M101 group members based on projection have been shown to be background objects, indicating the need for future diffuse dwarf surveys to be very careful in drawing conclusions about group membership without robust distance estimates. In this work we update the satellite luminosity function of M101 based on the presence of these new objects down to M-V & xfffd;=& xfffd;?8.2. M101 is a sparsely populated system with only nine satellites down to M-V ?8, as compared with 26 for M31 and 24.5 & xfffd;& xfffd;7.7 for the median host in the Local Volume. This makes M101 by far the sparsest group probed to this depth, although M94 is even sparser to the depth at which it has been examined (M-V & xfffd;=& xfffd;?9.1). M101 and M94 share several properties that mark them as unusual compared with the other Local Volume galaxies examined: they have a very sparse satellite population but also have high star-forming fractions among these satellites; such properties are also found in the galaxies examined as part of the Satellites around Galactic Analogs survey. We suggest that these properties appear to be tied to the wider galactic environment, with more isolated galaxies showing sparse satellite populations that are more likely to have had recent star formation, while those in dense environments have more satellites that tend to have no ongoing star formation. Overall, our results show a level of halo-to-halo scatter between galaxies of similar mass that is larger than is predicted in the lambda cold dark matter model.
    • Machine learning can accurately predict pre-admission baseline hemoglobin and creatinine in intensive care patients

      Dauvin, Antonin; Donado, Carolina; Bachtiger, Patrik; Huang, Ke-Chun; Sauer, Christopher Martin; Ramazzotti, Daniele; Bonvini, Matteo; Celi, Leo Anthony; Douglas, Molly J; Univ Arizona, Coll Med (NATURE PUBLISHING GROUP, 2019-11-29)
      Patients admitted to the intensive care unit frequently have anemia and impaired renal function, but often lack historical blood results to contextualize the acuteness of these findings. Using data available within two hours of ICU admission, we developed machine learning models that accurately (AUC 0.86–0.89) classify an individual patient’s baseline hemoglobin and creatinine levels. Compared to assuming the baseline to be the same as the admission lab value, machine learning performed significantly better at classifying acute kidney injury regardless of initial creatinine value, and significantly better at predicting baseline hemoglobin value in patients with admission hemoglobin of <10 g/dl.
    • Machine learning classification of bacterial species using mix-and-match reagents on paper microfluidic chips and smartphone-based capillary flow analysis

      Kim, Sangsik; Day, Alexander S.; Yoon, Jeong-Yeol; Department of Biomedical Engineering, The University of Arizona (Springer Science and Business Media LLC, 2022-03-28)
      Traditionally, specific bioreceptors such as antibodies have rapidly identified bacterial species in environmental water samples. However, this method has the disadvantages of requiring an additional process to conjugate or immobilize bioreceptors on the assay platform, which becomes unstable at room temperature. Here, we demonstrate a novel mix-and-match method to identify bacteria species by loading the bacterial samples with simple bacteria interacting components (not bioreceptors), such as lipopolysaccharides, peptidoglycan, and bovine serum albumin, and carboxylated particles, all separately on multiple channels. Neither covalent conjugation nor surface immobilization was necessary. Interactions between bacteria and the above bacteria interacting components resulted in varied surface tension and viscosity, leading to various flow velocities of capillary action through the paper fibers. The smartphone camera and a custom Python code recorded multiple channel flow velocity, each loaded with different bacteria interacting components. A multi-dimensional data set was obtained for a given bacterial species and concentration and used as a machine learning training model. A support vector machine was applied to classify the six bacterial species: Escherichia coli, Salmonella Typhimurium, Pseudomonas aeruginosa, Staphylococcus aureus, Enterococcus faecium, and Bacillus subtilis. Under optimized conditions, the training model predicts the bacterial species with an accuracy of > 85% of the six bacteria species.
    • Machine learning classification of Kuiper belt populations

      Smullen, Rachel A; Volk, Kathryn; Univ Arizona, Dept Astron; Univ Arizona, Lunar & Planetary Lab (OXFORD UNIV PRESS, 2020-07-06)
      In the outer Solar system, the Kuiper belt contains dynamical subpopulations sculpted by a combination of planet formation and migration and gravitational perturbations from the present-day giant planet configuration. The subdivision of observed Kuiper belt objects (KBOs) into different dynamical classes is based on their current orbital evolution in numerical integrations of their orbits. Here, we demonstrate that machine learning algorithms are a promising tool for reducing both the computational time and human effort required for this classification. Using a Gradient Boosting Classifier, a type of machine learning regression tree classifier trained on features derived from short numerical simulations, we sort observed KBOs into four broad, dynamically distinct populations - classical, resonant, detached, and scattering - with a >97 per cent accuracy for the testing set of 542 securely classified KBOs. Over 80 per cent of these objects have a >3 sigma probability of class membership, indicating that the machine learning method is classifying based on the fundamental dynamical features of each population. We also demonstrate how, by using computational savings over traditional methods, we can quickly derive a distribution of class membership by examining an ensemble of object clones drawn from the observational errors. We find two major reasons for misclassification: inherent ambiguity in the orbit of the object - for instance, an object that is on the edge of resonance - and a lack of representative examples in the training set. This work provides a promising avenue to explore for fast and accurate classification of the thousands of new KBOs expected to be found by surveys in the coming decade.
    • Machine Learning for Prediction of Stable Warfarin Dose in US Latinos and Latin Americans

      Steiner, H.E.; Giles, J.B.; Patterson, H.K.; Feng, J.; El Rouby, N.; Claudio, K.; Marcatto, L.R.; Tavares, L.C.; Galvez, J.M.; Calderon-Ospina, C.-A.; et al. (Frontiers Media S.A., 2021)
      Populations used to create warfarin dose prediction algorithms largely lacked participants reporting Hispanic or Latino ethnicity. While previous research suggests nonlinear modeling improves warfarin dose prediction, this research has mainly focused on populations with primarily European ancestry. We compare the accuracy of stable warfarin dose prediction using linear and nonlinear machine learning models in a large cohort enriched for US Latinos and Latin Americans (ULLA). Each model was tested using the same variables as published by the International Warfarin Pharmacogenetics Consortium (IWPC) and using an expanded set of variables including ethnicity and warfarin indication. We utilized a multiple linear regression model and three nonlinear regression models: Bayesian Additive Regression Trees, Multivariate Adaptive Regression Splines, and Support Vector Regression. We compared each model’s ability to predict stable warfarin dose within 20% of actual stable dose, confirming trained models in a 30% testing dataset with 100 rounds of resampling. In all patients (n = 7,030), inclusion of additional predictor variables led to a small but significant improvement in prediction of dose relative to the IWPC algorithm (47.8 versus 46.7% in IWPC, p = 1.43 × 10−15). Nonlinear models using IWPC variables did not significantly improve prediction of dose over the linear IWPC algorithm. In ULLA patients alone (n = 1,734), IWPC performed similarly to all other linear and nonlinear pharmacogenetic algorithms. Our results reinforce the validity of IWPC in a large, ethnically diverse population and suggest that additional variables that capture warfarin dose variability may improve warfarin dose prediction algorithms. Copyright © 2021 Steiner, Giles, Patterson, Feng, El Rouby, Claudio, Marcatto, Tavares, Galvez, Calderon-Ospina, Sun, Hutz, Scott, Cavallari, Fonseca-Mendoza, Duconge, Botton, Santos and Karnes.
    • Machine learning in detecting schizophrenia: An overview

      Suri, G.S.; Kaur, G.; Moein, S.; Center for Innovation in Brain Science, University of Arizona (Tech Science Press, 2021)
      Schizophrenia (SZ) is a mental heterogeneous psychiatric disorder with unknown cause. Neuroscientists postulate that it is related to brain networks. Recently, scientists applied machine learning (ML) and artificial intelligence for the detection, monitoring, and prognosis of a range of diseases, including SZ, because these techniques show a high performance in discovering an association between disease symptoms and disease. Regions of the brain have significant connections to the symptoms of SZ. ML has the power to detect these associations. ML interests researchers because of its ability to reduce the number of input features when the data are high dimensional. In this paper, an overview of ML models for detecting SZ disorder is provided. Studies are presented that applied magnetic resonance imaging data and physiological signals as input data. ML is utilized to extract significant features for predicting and monitoring SZ. Reviewing a large number of studies shows that a support vector machine, deep neural network, and random forest predict SZ with a high accuracy of 70%–90%. Finally, the collected results show that ML methods provide reliable answers for clinicians when making decisions about SZ patients. © 2021 Mark A. Lemley & Bryan Casey.
    • Machine Learning Methods-based Modeling and Optimization of 3-D-Printed Dielectrics around Monopole Antenna

      Sharma, Yashika; Chen, Xi; Wu, Junqiang; Zhou, Qiang; Zhang, Hao Helen; Xin, Hao; Department of Electrical and Computer Engineering, University of Arizona; Department of Systems and Industrial Engineering, University of Arizona; Department of Physics, University of Arizona (Institute of Electrical and Electronics Engineers (IEEE), 2022)
      In this paper, we propose using new Machine Learning (ML)-based optimization methods, as an alternative to traditional optimization methods, for complex antenna designs. This is an efficient methodology to tackle computational challenges, as it is capable of handling a large number of design parameters and is more efficient as well as informative. The proposed technique is applied for modeling gain performance in the principal plane of a monopole antenna when its radiation properties are modified by placing spatially dependent dielectric material around it. Using the proposed methodology, the dielectric constant values are mapped to the gain pattern of the design. We use two ML techniques for this purpose, namely, Gaussian Process (GP) regression and Artificial Neural Network (ANN). Once each of these models is obtained, they are further used for estimating the dielectric constant values that can suggest optimal directions to modify gain patterns for single-beam and multiple-beam patterns rather than the conventional omnidirectional pattern of a monopole antenna. The performance of this technique is compared with heuristic optimization techniques such as genetic algorithms. The proposed method proves to be quite accurate in spite of being a high-dimensional non-linear problem. A prototype of a monopole design with three-beam gain pattern is fabricated and tested. The measurement results agree well with the simulation results. The proposed methodology can provide useful and scalable optimization tools for computationally intensive antenna design problems.
    • Machine Learning Mid-Infrared Spectral Models for Predicting Modal Mineralogy of CI/CM Chondritic Asteroids and Bennu

      Breitenfeld, L.B.; Rogers, A.D.; Glotch, T.D.; Hamilton, V.E.; Christensen, P.R.; Lauretta, D.S.; Gemma, M.E.; Howard, K.T.; Ebel, D.S.; Kim, G.; et al. (John Wiley and Sons Inc, 2021)
      Planetary surfaces can be complex mixtures of coarse and fine particles that exhibit linear and nonlinear mixing behaviors at mid-infrared (MIR) wavelengths. Machine learning multivariate analysis can estimate modal mineralogy of mixtures and is favorable because it does not assume linear mixing across wavelengths. We used partial least squares (PLS) and least absolute shrinkage and selection operator (lasso), two types of machine learning, to build MIR spectral models to determine the surface mineralogy of the asteroid (101955) Bennu using OSIRIS-REx Thermal Emission Spectrometer (OTES) data. We find that PLS models outperform lasso models. The cross-validated root-mean-square error of our final PLS models (consisting of 317 unique spectra of samples derived from 13 analog mineral samples and eight meteorites) range from ∼4 to 13 vol% depending on the mineral group. PLS predictions in vol% of Bennu's average global composition are 78% phyllosilicate, 9% olivine, 11% carbonates, and 6% magnetite. Pyroxene is not predicted for the global average spectrum, though it has been detected in small amounts on Bennu. These mineral abundances confirm previous findings that the composition of Bennu is consistent with CI/CM chondrites with high degrees of aqueous alteration. The predicted mineralogy of two previously identified OTES spectral types vary minimally from the global average. In agreement with previous work, we interpret OTES spectral differences as primarily caused by relative abundances of fine particulates rather than major compositional variations. © 2021. American Geophysical Union. All Rights Reserved.
    • Machine learning techniques for chemical and type analysis of ocean oil samples via handheld spectrophotometer device

      Sosnowski, K.; Loh, A.; Zubler, A.V.; Shir, H.; Ha, S.Y.; Yim, U.H.; Yoon, J.-Y.; Department of Biomedical Engineering, The University of Arizona (Elsevier Ltd, 2022)
      We designed and constructed a handheld, sturdy fluorescence spectrometry device for identifying samples from ocean oil spills. Two large training databases of autofluorescence spectra from raw oil samples (538 samples/1614 spectra and 767 samples/2301 spectra) were cross validated using support vector machine (SVM) to identify oil type and SARA (saturate, aromatic, resin, and asphaltene) contents. The device's performance was then validated on an independent set of 79 ocean oil samples, which were added to and then collected from ocean water during outdoor exposure to hot, humid weather to represent an actual oil spill. It successfully classified oil types with 92%–100% sensitivity and specificity and F1 scores of 85.7–100%. Further classification of light fuel oils into marine gas oil (MGO)-like and Bunker A (BA)-like categories was successful with the training set (raw oil samples), while less successful with the independent validation set (ocean oil samples). SARA content classification models performed well in training for the saturate (80.8% accuracy) and asphaltene (90.7%) contents. The developed training model was validated using ocean oil samples, and the resulting accuracies were 62.0% (saturate) and 93.7% (asphaltene). These results indicate the difficulties in classifying volatile light fuel oils with a low molecular weight that have experienced weathering effects, while high molecular weight compounds and general oil type can be analyzed. © 2022 The Authors
    • Machine-learning-based Brokers for Real-time Classification of the LSST Alert Stream

      Narayan, Gautham; Zaidi, Tayeb; Soraisam, Monika D.; Wang, Zhe; Lochner, Michelle; Matheson, Thomas; Saha, Abhijit; Yang, Shuo; Zhao, Zhenge; Kececioglu, John; et al. (IOP PUBLISHING LTD, 2018-05)
      The unprecedented volume and rate of transient events that will be discovered by the Large Synoptic Survey Telescope (LSST) demand that the astronomical community update its follow-up paradigm. Alert-brokers-automated software system to sift through, characterize, annotate, and prioritize events for follow-up-will be critical tools for managing alert streams in the LSST era. The Arizona-NOAO Temporal Analysis and Response to Events System (ANTARES) is one such broker. In this work, we develop a machine learning pipeline to characterize and classify variable and transient sources only using the available multiband optical photometry. We describe three illustrative stages of the pipeline, serving the three goals of early, intermediate, and retrospective classification of alerts. The first takes the form of variable versus transient categorization, the second a multiclass typing of the combined variable and transient data set, and the third a purity-driven subtyping of a transient class. Although several similar algorithms have proven themselves in simulations, we validate their performance on real observations for the first time. We quantitatively evaluate our pipeline on sparse, unevenly sampled, heteroskedastic data from various existing observational campaigns, and demonstrate very competitive classification performance. We describe our progress toward adapting the pipeline developed in this work into a real-time broker working on live alert streams from time-domain surveys.
    • Macroeconomic dynamics and the IS puzzle

      Hawkins, Raymond J.; Nguyen, Chau N.; Univ Arizona, Coll Opt Sci (KIEL INST WORLD ECONOMY, 2018-09-18)
      The authors solve the IS puzzle for the G7 countries. They find that five of the G7 countries have the expected significant negative relationship between the output gap and the realrate gap; the time series of the remaining two show material deviation from expected IS-curve behavior. The authors show that the observed time dependence of the interaction between the output and real-rate gaps can be represented in a parsimonious and practical manner using the theory of anelasticity that unifies partial-adjustment specifications of the IS curve.
    • Macroeconomic dynamics and the IS puzzle

      Hawkins, R.J.; Nguyen, C.N.; College of Optical Sciences, University of Arizona (Walter de Gruyter GmbH, 2018)
      The authors solve the IS puzzle for the G7 countries. They find that five of the G7 countries have the expected significant negative relationship between the output gap and the realrate gap; the time series of the remaining two show material deviation from expected IScurve behavior. The authors show that the observed time dependence of the interaction between the output and real-rate gaps can be represented in a parsimonious and practical manner using the theory of anelasticity that unifies partial-adjustment specifications of the IS curve. © 2018 Raymond J. Hawkins et al., published by Sciendo.
    • Macrophage migration inhibitory factor: controller of systemic inflammation

      Larson, Douglas; Horak, Katherine; Sarver Heart Center and Departments of Surgery and Medical Pharmacology, College of Medicine, The University of Arizona, Tucson, Arizona, USA (BioMed Central, 2006)
      Macrophage migration inhibitory factor (MIF) is a cytokine that is secreted by the anterior pituitary and immune cells in response to surgical stress, injury, and sepsis. This cytokine appears to be a critical regulator of the inflammatory pathways, leading to systemic inflammatory response syndrome and subsequent multiple organ dysfunction syndrome. This report provides an integrated scheme describing the manner by which MIF controls the neurohormonal response and the adaptive immune system, namely the T-helper (Th)1 and Th2 lymphocytes, which results in the release of pro-inflammatory cytokines and the anti-inflammatory cytokine interleukin-10. The development of systemic inflammatory response syndrome and subsequent development of multiple organ dysfunction syndrome appear to be related to MIF levels and the balance of Th1 and Th2 function.

      Rodigas, Timothy J.; Bergeron, P.; Simon, Amélie; Arriagada, Pamela; Faherty, Jacqueline K.; Anglada-Escudé, Guillem; Mamajek, Eric E.; Weinberger, A. J.; Butler, R. Paul; Males, Jared R.; et al. (IOP PUBLISHING LTD, 2016-11-04)
      HD 11112 is an old, Sun-like star that has a long-term radial velocity (RV) trend indicative of a massive companion on a wide orbit. Here we present direct images of the source responsible for the trend using the Magellan Adaptive Optics system. We detect the object (HD 11112B) at a separation of 2 2 (100 au) at multiple wavelengths spanning 0.6-4 mu m. and show that it is most likely a gravitationally bound cool white dwarf. Modeling its spectral energy distribution suggests that its mass is 0.9-1.1M(circle dot), which corresponds to very high eccentricity, near edge-on orbits from a. Markov chain Monte Carlo analysis of the RV and imaging data together. The total age of the white dwarf is > 2 sigma, which is discrepant with that of the primary star under most assumptions. The problem can be resolved if the white dwarf progenitor was initially a double white dwarf binary that then merged into the observed high-mass white dwarf. HD 11112B is a unique and intriguing benchmark object that can be used to calibrate atmospheric and evolutionary models of cool white dwarfs and should thus continue to be monitored by RV and direct imaging over the coming years.
    • MagAO Observations of the binary microlens OGLE-2014-BLG-1050 prefer the higher-mass solution

      Xie, X.; Dong, S.; Zhu, W.; Gould, A.; Udalski, A.; Beaulieu, J.-P.; Close, L.M.; Males, J.R.; Marquette, J.-B.; Morzinski, K.M.; et al. (American Astronomical Society, 2021)
      We report adaptive optics (AO) follow-up imaging of OGLE-2014-BLG-1050, which is the second binary microlensing event with space-based parallax measurements. The degeneracy in microlens parallax πEled to two sets of solutions, either a ~ (0.9, 0.35)M⊙binary at ~3.5 kpc or a ~(0.2, 0.07)M⊙binary at ~1.1 kpc. We measure the flux blended with the microlensed source by conducting Magellan AO observations, and find that the blending is consistent with the predicted lens flux from the higher-mass solution. From the combination of the AO flux measurement together with previous lensing constraints, it is estimated that the lens system consists of a -1.05+0.08-0.07primary and a 0.38+0.07-0.06M⊙secondary at - 3.43+0.19-0.21kpc. © 2021 Institute of Physics Publishing. All rights reserved.
    • MagAO-X first light

      Males, Jared R.; Close, Laird M.; Guyon, Olivier; Hedglen, Alexander D.; Van Gorkom, Kyle; Long, Joseph D.; Kautz, Maggie Y.; Lumbres, Jennifer; Schatz, Lauren; Rodack, Alexander T.; et al. (SPIE, 2020-12-13)
      MagAO-X is a new "extreme" adaptive optics system for the Magellan Clay 6.5 m telescope which began commissioning in December, 2019. MagAO-X is based around a 2040 actuator deformable mirror, controlled by a pyramid wavefront sensor operating at up to 3.6 kHz. When fully optimized, MagAO-X will deliver high Strehls (< 70%), high resolution (19 mas), and high contrast (< 1 × 10-4) at Ha (656 nm). We present a brief review of the instrument design and operations, and then report on the results of the first-light run. ©2020 SPIE.
    • MagAO: status and science

      Morzinski, Katie M.; Close, Laird M.; Males, Jared R.; Hinz, Phil M.; Esposito, Simone; Riccardi, Armando; Briguglio, Runa; Follette, Katherine B.; Pinna, Enrico; Puglisi, Alfio; et al. (SPIE-INT SOC OPTICAL ENGINEERING, 2016-07-26)
      MagAO is the adaptive optics instrument at the Magellan Clay telescope at Las Campanas Observatory, Chile. MagAO has a 585-actuator adaptive secondary mirror and 1000-Hz pyramid wavefront sensor, operating on natural guide stars from R-magnitudes of -1 to 15. MagAO has been in on-sky operation for 166 nights since installation in 2012. MagAO's unique capabilities are simultaneous imaging in the visible and infrared with VisAO and Clio, excellent performance at an excellent site, and a lean operations model. Science results from MagAO include the first ground-based CCD image of an exoplanet, demonstration of the first accreting protoplanets, discovery of a new wide-orbit exoplanet, and the first empirical bolometric luminosity of an exoplanet. We describe the status, report the AO performance, and summarize the science results. New developments reported here include color corrections on red guide stars for the wavefront sensor; a new field stop stage to facilitate VisAO imaging of extended sources; and eyepiece observing at the visible-light diffraction limit of a 6.5-m telescope. We also discuss a recent hose failure that led to a glycol coolant leak, and the recovery of the adaptive secondary mirror (ASM) after this recent (Feb. 2016) incident.
    • Magellan Adaptive Optics Imaging of PDS 70: Measuring the Mass Accretion Rate of a Young Giant Planet within a Gapped Disk

      Wagner, Kevin; Follete, Katherine B.; Close, Laird M.; Apai, Dániel; Gibbs, Aidan; Keppler, Miriam; Müller, André; Henning, Thomas; Kasper, Markus; Wu, Ya-Lin; et al. (IOP PUBLISHING LTD, 2018-08-10)
      PDS 70b is a recently discovered and directly imaged exoplanet within the wide (greater than or similar to 40 au) cavity around PDS 70. Ongoing accretion onto the central star suggests that accretion onto PDS 70b may also be ongoing. We present the first high-contrast images at H alpha (656 nm) and nearby continuum (643 nm) of PDS 70 utilizing the MagAO system. The combination of these filters allows for the accretion rate of the young planet to be inferred, as hot infalling hydrogen gas will emit strongly at H alpha over the optical continuum. We detected a source in H alpha at the position of PDS 70b on two sequential nights in 2018 May, for which we establish a false positive probability of <0.1%. We conclude that PDS 70b is a young, actively accreting planet. We utilize the H alpha line luminosity to derive a mass accretion rate of (M) over dot = 10(-8 +/- 1) M-Jup yr(-1), where the large uncertainty is primarily due to the unknown amount of optical extinction from the circumstellar and circumplanetary disks. PDS 70b represents the second case of an accreting planet interior to a disk gap, and is among the early examples of a planet observed during its formation.

      Wu, Ya-Lin; Close, Laird M.; Bailey, Vanessa P.; Rodigas, Timothy J.; Males, Jared R.; Morzinski, Katie M.; Follette, Katherine B.; Hinz, Philip M.; Puglisi, Alfio; Briguglio, Runa; et al. (IOP PUBLISHING LTD, 2016-05-17)
      We analyze archival data from Bailey and co-workers from the Magellan adaptive optics system and present the first 0.9 mu m detection (z' = 20.3 +/- 0.4 mag; Delta z' = 13.0 +/- 0.4 mag) of the 11 M-Jup circumbinary planet HD 106906AB b, as well as 1 and 3.8 mu m detections of the debris disk around the binary. The disk has an east-west asymmetry in length and surface brightness, especially at 3.8 mu m where the disk appears to be one-sided. The spectral energy distribution of b, when scaled to the K-S-band photometry, is consistent with 1800 K atmospheric models without significant dust reddening, unlike some young, very red, low-mass companions such as CT Cha B and 1RXS 1609 B. Therefore, the suggested circumplanetary disk of Kalas and co-workers might not contain much material, or might be closer to face-on. Finally, we suggest that the widest (a greater than or similar to 100 AU) low mass ratio (M-p/M-star = q less than or similar to 0.01) companions may have formed inside protoplanetary disks but were later scattered by binary/planet interactions. Such a scattering event may have occurred for HD 106906AB b with its central binary star, but definitive proof at this time is elusive.