Kinematic Evidence of an Embedded Protoplanet in HD 142666 Identified by Machine Learning

Observations of protoplanetary disks have shown that forming exoplanets leave characteristic imprints on the gas and dust of the disk. In the gas, these forming exoplanets cause deviations from Keplerian motion, which can be detected through molecular line observations. Our previous work has shown that machine learning can correctly determine if a planet is present in these disks. Using our machine learning models, we identify strong, localized non-Keplerian motion within the disk HD 142666. Subsequent hydrodynamics simulations of a system with a 5 Jupiter-mass planet at 75 au recreates the kinematic structure. By currently established standards in the field, we conclude that HD 142666 hosts a planet. This work represents a first step towards using machine learning to identify previously overlooked non-Keplerian features in protoplanetary disks.Comment: 7 pages, 3 figures, 1 table. Accepted to Ap

Measuring the Photon Helicity in Radiative B Decays

We propose a way of measuring the photon polarization in radiative B decays into K resonance states decaying to K\pi\pi, which can test the Standard Model and probe new physics. The photon polarization is shown to be measured by the up-down asymmetry of the photon direction relative to the K\pi\pi decay plane in the K resonance rest frame. The integrated asymmetry in K_1(1400)\to K\pi\pi, calculated to be 0.34\pm 0.05 in the Standard Model, is measurable at currently operating B factories.Comment: 4 pages, final version to appear in Physical Review Letter

What Have We Learned from RHIC?

In this talk, I present what I believe we have learned from the recent RHIC heavy ion experiments. The goal of these experiments is to make and study matter at very high energy densities, greater than an order of magnitude larger than that of nuclear matter. Have we made such matter? What have we learned about the properties of this matter? What do we hope and expect to learn in the future?Comment: 34 figure

Exclusive semileptonic B decays to radially excited D mesons

Exclusive semileptonic B decays to radially excited charmed mesons are investigated at the first order of the heavy quark expansion. The arising leading and subleading Isgur-Wise functions are calculated in the framework of the relativistic quark model. It is found that the 1/m_Q corrections play an important role and substantially modify results. An interesting interplay between different corrections is found. As a result the branching ratio for the B-> D'e\nu decay is essentially increased by 1/m_Q corrections, while the one for B-> D*'e\nu is only slightly influenced by them.Comment: 19 pages, revtex, 6 figures, uses rotating.st

Universal Behavior of Charged Particle Production in Heavy Ion Collisions

The PHOBOS experiment at RHIC has measured the multiplicity of primary charged particles as a function of centrality and pseudorapidity in Au+Au collisions at sqrt(s_NN) = 19.6, 130 and 200 GeV. Two kinds of universal behavior are observed in charged particle production in heavy ion collisions. The first is that forward particle production, over a range of energies, follows a universal limiting curve with a non-trivial centrality dependence. The second arises from comparisons with pp/pbar-p and e+e- data. N_tot/(N_part/2) in nuclear collisions at high energy scales with sqrt(s) in a similar way as N_tot in e+e- collisions and has a very weak centrality dependence. This feature may be related to a reduction in the leading particle effect due to the multiple collisions suffered per participant in heavy ion collisions.Comment: 4 Pages, 5 Figures, contributed to the Proceedings of Quark Matter 2002, Nantes, France, 18-24 July 200

Production and Decay of Scalar Stoponium Bound States

In this paper we discuss possible signatures for the production of scalar \stst\ (stoponium) bound states \sigst\ at hadron colliders, where \st\ is the lighter scalar top eigenstate. We first study the decay of \sigst; explicit expressions are given for all potentially important decay modes. If \st\ has unsuppressed two--body decays, they will always overwhelm the annihilation decays of \sigst. Among the latter, we find that usually either the $gg$ or $hh$ final state dominates, depending on the size of the off--diagonal entry of the stop mass matrix; $h$ is the lighter neutral scalar Higgs boson of the minimal supersymmetric model. If \msig\ happens to be close to the mass of one of the neutral scalar Higgs bosons, $Q \bar{Q}$ final states dominate ($Q=b$ or $t$). \ww\ and $ZZ$ final states are subdominant. We argue that \sigst \rightarrow \gamgam decays offer the best signal for stoponium production at hadron colliders. The tevatron should be able to close the light stop window left open by LEP searches, but its mass reach is limited to \msig \leq 90 GeV. In contrast, at the LHC one should ultimately be able to probe the region \msig \leq 700 GeV, if the $hh$ partial width is not too large. We also comment on the feasibility of searching for \sigst\ production at hadron colliders in the $ZZ, \ Z \gamma$ and \fourtau\ final states, and briefly mention \sigst\ production at \gamgam\ colliders.Comment: 31 pages plus 10 figures (available from DREES@WISCPHEN); LaTeX with equation.sty; MAD/PH/808, KEK-TH-37

Deep learning augmented ECG analysis to identify biomarker-defined myocardial injury

Chest pain is a common clinical complaint for which myocardial injury is the primary concern and is associated with significant morbidity and mortality. To aid providers' decision-making, we aimed to analyze the electrocardiogram (ECG) using a deep convolutional neural network (CNN) to predict serum troponin I (TnI) from ECGs. We developed a CNN using 64,728 ECGs from 32,479 patients who underwent ECG within 2 h prior to a serum TnI laboratory result at the University of California, San Francisco (UCSF). In our primary analysis, we classified patients into groups of TnI < 0.02 or ≥ 0.02 µg/L using 12-lead ECGs. This was repeated with an alternative threshold of 1.0 µg/L and with single-lead ECG inputs. We also performed multiclass prediction for a set of serum troponin ranges. Finally, we tested the CNN in a cohort of patients selected for coronary angiography, including 3038 ECGs from 672 patients. Cohort patients were 49.0% female, 42.8% white, and 59.3% (19,283) never had a positive TnI value (≥ 0.02 µg/L). CNNs accurately predicted elevated TnI, both at a threshold of 0.02 µg/L (AUC = 0.783, 95% CI 0.780-0.786) and at a threshold of 1.0 µg/L (AUC = 0.802, 0.795-0.809). Models using single-lead ECG data achieved significantly lower accuracy, with AUCs ranging from 0.740 to 0.773 with variation by lead. Accuracy of the multi-class model was lower for intermediate TnI value-ranges. Our models performed similarly on the cohort of patients who underwent coronary angiography. Biomarker-defined myocardial injury can be predicted by CNNs from 12-lead and single-lead ECGs

Patient-Level Artificial Intelligence–Enhanced Electrocardiography in Hypertrophic Cardiomyopathy Longitudinal Treatment and Clinical Biomarker Correlations

BackgroundArtificial intelligence (AI) applied to 12-lead electrocardiographs (ECGs) can detect hypertrophic cardiomyopathy (HCM).ObjectivesThe purpose of this study was to determine if AI-enhanced ECG (AI-ECG) can track longitudinal therapeutic response and changes in cardiac structure, function, or hemodynamics in obstructive HCM during mavacamten treatment.MethodsWe applied 2 independently developed AI-ECG algorithms (University of California-San Francisco and Mayo Clinic) to serial ECGs (n = 216) from the phase 2 PIONEER-OLE trial of mavacamten for symptomatic obstructive HCM (n = 13 patients, mean age 57.8 years, 69.2% male). Control ECGs from 2,600 age- and sex-matched individuals without HCM were obtained. AI-ECG output was correlated longitudinally to echocardiographic and laboratory metrics of mavacamten treatment response.ResultsIn the validation cohorts, both algorithms exhibited similar performance for HCM diagnosis, and exhibited mean HCM score decreases during mavacamten treatment: patient-level score reduction ranged from approximately 0.80 to 0.45 for Mayo and 0.70 to 0.35 for USCF algorithms; 11 of 13 patients demonstrated absolute score reduction from start to end of follow-up for both algorithms. HCM scores were significantly associated with other HCM-relevant parameters, including left ventricular outflow tract gradient at rest, postexercise, and with Valsalva, and NT-proBNP level, independent of age and sex (all P < 0.01). For both algorithms, the strongest longitudinal correlation was between AI-ECG HCM score and left ventricular outflow tract gradient postexercise (slope estimate: University of California-San Francisco 0.70 [95% CI: 0.45-0.96], P < 0.0001; Mayo 0.40 [95% CI: 0.11-0.68], P = 0.007).ConclusionsAI-ECG analysis longitudinally correlated with changes in echocardiographic and laboratory markers during mavacamten treatment in obstructive HCM. These results provide early evidence for a potential paradigm for monitoring HCM therapeutic response