Hierarchical fragmentation and differential star formation in the Galactic "Snake": infrared dark cloud G11.11-0.12

We present Submillimeter Array (SMA) $\lambda =$ 0.88 and 1.3 mm broad band observations, and the Jansky Very Large Array (VLA) observations in $\rm{NH_3}$ $(J,K) = (1,1)$ up to $(5,5)$, $\rm{H_2O}$ and $\rm{CH_3OH}$ maser lines toward the two most massive molecular clumps in infrared dark cloud (IRDC) G11.11-0.12. Sensitive high-resolution images reveal hierarchical fragmentation in dense molecular gas from the $\sim 1$ pc clump scale down to $\sim 0.01$ pc condensation scale. At each scale, the mass of the fragments is orders of magnitude larger than the Jeans mass. This is common to all four IRDC clumps we studied, suggesting that turbulence plays an important role in the early stages of clustered star formation. Masers, shock heated $\rm{NH_3}$ gas, and outflows indicate intense ongoing star formation in some cores while no such signatures are found in others. Furthermore, chemical differentiation may reflect the difference in evolutionary stages among these star formation seeds. We find $\rm{NH_3}$ ortho/para ratios of $1.1\pm0.4$, $2.0\pm0.4$, and $3.0\pm0.7$ associated with three outflows, and the ratio tends to increase along the outflows downstream. Our combined SMA and VLA observations of several IRDC clumps present the most in depth view so far of the early stages prior to the hot core phase, revealing snapshots of physical and chemical properties at various stages along an apparent evolutionary sequence.Comment: 21 pages, 11 figures, 8 tables, accepted to MNRAS; this version includes minor typo corrections from proo

Non-invasive Liver Fibrosis Screening on CT Images using Radiomics

Objectives: To develop and evaluate a radiomics machine learning model for detecting liver fibrosis on CT of the liver. Methods: For this retrospective, single-centre study, radiomic features were extracted from Regions of Interest (ROIs) on CT images of patients who underwent simultaneous liver biopsy and CT examinations. Combinations of contrast, normalization, machine learning model, and feature selection method were determined based on their mean test Area Under the Receiver Operating Characteristic curve (AUC) on randomly placed ROIs. The combination and selected features with the highest AUC were used to develop a final liver fibrosis screening model. Results: The study included 101 male and 68 female patients (mean age = 51.2 years $\pm$ 14.7 [SD]). When averaging the AUC across all combinations, non-contrast enhanced (NC) CT (AUC, 0.6100; 95% CI: 0.5897, 0.6303) outperformed contrast-enhanced CT (AUC, 0.5680; 95% CI: 0.5471, 0.5890). The combination of hyperparameters and features that yielded the highest AUC was a logistic regression model with inputs features of maximum, energy, kurtosis, skewness, and small area high gray level emphasis extracted from non-contrast enhanced NC CT normalized using Gamma correction with $\gamma$ = 1.5 (AUC, 0.7833; 95% CI: 0.7821, 0.7845), (sensitivity, 0.9091; 95% CI: 0.9091, 0.9091). Conclusions: Radiomics-based machine learning models allow for the detection of liver fibrosis with reasonable accuracy and high sensitivity on NC CT. Thus, these models can be used to non-invasively screen for liver fibrosis, contributing to earlier detection of the disease at a potentially curable stage

The essential value of long-term experimental data for hydrology and water management

We would like to thank the European Research Council ERC for funding the VeWa project and most of Tetzlaff's time (project GA 335910 VeWa). No data were used in producing this manuscript.Peer reviewedPublisher PD

Spitzer Infrared Array Camera (IRAC) Pipeline: final modifications and lessons learned

In more than ten years of operations, the Spitzer Space Telescope has conducted a wide range of investigations from observing nearby asteroids to probing atmospheric properties of exoplanets to measuring masses of the most distance galaxies. Observations using the Infrared Array Camera (IRAC) at 3.6 and 4.5um will continue through mid-2019 when the James Webb Space Telescope will succeed Spitzer. In anticipation of the eventual end of the mission, the basic calibrated data reduction pipeline designed to produce flux-calibrated images has been finalized and used to reprocess all the data taken during the Spitzer warm mission. We discuss all final modifications made to the pipeline

Calibration trending in the Spitzer beyond era

The Spitzer Space Telescope currently operates in the "Beyond Era", over nine years past an original cryogenic mission. As the astronomy community continues to advance scientific boundaries and push beyond original specifications, the stability of the Infrared Array Camera (IRAC) instrument is paramount. The Instrument Team (IST) monitors the pointing accuracy, temperature, and calibration and provides the information in a timely manner to observers. The IRAC IST created a calibration trending web page, available to the general astronomy community, where the team posts updates of three most pertinent scientific stability measures of the IRAC data: calibration, bias, and bad pixels. In addition, photometry and telescope properties from all the staring observations (>1500 as of April 2018) are trended to examine correlations with changes in the age or thermal properties of the telescope. A long, well-sampled baseline established by consistent monitoring outside anomalies and space weather events allows even the smallest changes to be detected

Intra-pixel gain variations and high-precision photometry with the Infrared Array Camera (IRAC)

The Infrared Array Camera (IRAC) on the Spitzer Space Telescope has been used to measure < 10^(-4) temporal variations in point sources (such as transiting extrasolar planets) at 3.6 and 4.5 μm. Due to the under-sampled nature of the PSF, the warm IRAC arrays show variations of as much as 8% in sensitivity as the center of the PSF moves across a pixel due to normal spacecraft pointing wobble and drift. These intra-pixel gain variations are the largest source of correlated noise in IRAC photometry. Usually this effect is removed by fitting a model to the science data themselves (self-calibration), which could result in the removal of astrophysically interesting signals. We describe a new technique for significantly reducing the gain variations and improving photometric precision in a given observation, without using the data to be corrected. This comprises: (1) an adaptive centroiding and repositioning method ("Peak-Up") that uses the Spitzer Pointing Control Reference Sensor (PCRS) to repeatedly position a target to within 0.1 IRAC pixels of an area of minimal gain variation; and (2) the high-precision, high-resolution measurement of the pixel gain structure using non-variable stars. We show that the technique currently allows the reduction of correlated noise by almost an order of magnitude over raw data, which is comparable to the improvement due to self-calibration. We discuss other possible sources of correlated noise, and proposals for reducing their impact on photometric precision

Spitzer/IRAC precision photometry: a machine learning approach

The largest source of noise in exoplanet and brown dwarf photometric time series made with Spitzer/IRAC is the coupling between intra-pixel gain variations and spacecraft pointing fluctuations. Observers typically correct for this systematic in science data by deriving an instrumental noise model simultaneously with the astrophysical light curve and removing the noise model. Such techniques for self-calibrating Spitzer photometric datasets have been extremely successful, and in many cases enabled near-photon-limited precision on exoplanet transit and eclipse depths. Self-calibration, however, can suffer from certain limitations: (1) temporal astrophysical signals can become aliased as part of the instrument model; (2) for some techniques adequate model estimation often requires a high degree of intra-pixel positional redundancy (multiple samples with nearby centroids) over long time spans; (3) many techniques do not account for sporadic high frequency telescope vibrations that smear out the point spread function. We have begun to build independent general-purpose intra-pixel systematics removal algorithms using three machine learning techniques: K-Nearest Neighbors (with kernel regression), Random Decision Forests, and Artificial Neural Networks. These methods remove many of the limitations of self-calibration: (1) they operate on a dedicated calibration database of approximately one million measurements per IRAC waveband (3.6 and 4.5 microns) of non-variable stars, and thus are independent of the time series science data to be corrected; (2) the database covers a large area of the "Sweet Spot, so the methods do not require positional redundancy in the science data; (3) machine learning techniques in general allow for flexibility in training with multiple, sometimes unorthodox, variables, including those that trace PSF smear. We focus in this report on the K-Nearest Neighbors with Kernel Regression technique. (Additional communications are in preparation describing Decision Forests and Neural Networks.

Photometry using the Infrared Array Camera on the Spitzer Space Telescope

We present several corrections for point source photometry to be applied to data from the Infrared Array Camera (IRAC) on the Spitzer Space Telescope. These corrections are necessary because of characteristics of the IRAC arrays and optics and the way the instrument is calibrated in-flight. When these corrections are applied, it is possible to achieve a ~2% relative photometric accuracy for sources of adequate signal to noise in an IRAC image.Comment: 16 pages, 13 figures. Accepted for publication in the Publications of the Astronomical Society of the Pacifi