X-Ray and Ray Astronomy At The Turning Point

Since 1962, approximately 40 point X-ray sources have been isolated, only about six of which have been identified with known radio or optical objects or have been studied over the 1 - 300 keV energy range. Data on the X-ray fluxes, positions, sizes and spectra of these sources have provided new information of considerable astrophysical significance and indicate that the exploratory stage of X-ray astronomy is now over. At higher energies, confirmation of a diffuse flux to 6 MeV and the successful detection of 100 MeV -y-rays from the galaxy indicate the necessity of further exploratory experiments. Satellite borne instruments on a larger scale than previously implemented will be needed to take advantage of the unique opportunity now available for advancing high-energy astrophysics

The Spectrum of Crab Nebula X-Rays to 120 Kev

Counting rate and pulse height distribution spectral data of Crab Nebula telemetered from balloon detector

Predictive Modeling Using Shape Statistics for Interpretable and Robust Quality Assurance of Automated Contours in Radiation Treatment Planning

Deep learning methods for image segmentation and contouring are gaining prominence as an automated approach for delineating anatomical structures in medical images during radiation treatment planning. These contours are used to guide radiotherapy treatment planning, so it is important that contouring errors are flagged before they are used for planning. This creates a need for effective quality assurance methods to enable the clinical use of automated contours in radiotherapy. We propose a novel method for contour quality assurance that requires only shape features, making it independent of the platform used to obtain the images. Our method uses a random forest classifier to identify low-quality contours. On a dataset of 312 kidney contours, our method achieved a cross-validated area under the curve of 0.937 in identifying unacceptable contours. We applied our method to an unlabeled validation dataset of 36 kidney contours. We flagged 6 contours which were then reviewed by a cervix contour specialist, who found that 4 of the 6 contours contained errors. We used Shapley values to characterize the specific shape features that contributed to each contour being flagged, providing a starting point for characterizing the source of the contouring error. These promising results suggest our method is feasible for quality assurance of automated radiotherapy contours

A Chandra study of the large-scale shock and cool filaments in Hydra A: Evidence for substantial gas dredge-up by the central outburst

We present the results of a Chandra study of the Hydra A galaxy cluster, where a powerful AGN outburst created a large-scale cocoon shock. We investigated possible azimuthal variations in shock strength and shape, finding indications for a weak shock with a Mach number in the range ~1.2-1.3. We measured the temperature change across the shock front. However, the detection of a temperature rise in the regions immediately inside of the front is complicated by the underlying temperature profile of the cluster atmosphere. We measured the global temperature profile of the cluster up to 700 kpc, which represents the farthest measurement obtained with Chandra for this cluster. A "plateau" in the temperature profile in the range ~70-150 kpc indicates the presence of cool gas, which is likely the result of uplift of material by the AGN outburst. After masking the cool filaments visible in the hardness ratio map, the plateau disappears and the temperature profile recovers a typical shape with a peak around 190 kpc, just inside the shock front. However, it is unlikely that such a temperature feature is produced by the shock as it is consistent with the general shape of the temperature profiles observed for relaxed galaxy clusters. We studied the spectral properties of the cool filaments finding evidence that ~10^11 M_sun of low-entropy material has been dredged up by the rising lobes from the central 30 kpc to the observed current position of 75-150 kpc. The energy required to lift the cool gas is >~2.2 x 10^60 erg, which is comparable to the work required to inflate the cavities and is ~25% of the total energy of the large-scale shock. Our results show that the AGN feedback in Hydra A is acting not only by directly heating the gas, but also by removing a substantial amount of potential fuel for the SMBH.Comment: 11 pages, 9 figures, accepted for publication in ApJ (version with full resolution figures available at http://www.bo.astro.it/~myriam/files/papers/gitti-hydra.pdf

Cavities and shocks in the galaxy group HCG 62 as revealed by Chandra, XMM and GMRT data

We report on the results of an analysis of Chandra, XMM-Newton and new GMRT data of the X-ray bright compact group of galaxies HCG 62, which is one of the few groups known to possess clear, small X-ray cavities in the inner regions. This is part of an ongoing X-ray/low-frequency radio study of 18 groups, initially chosen for the availability of good-quality X-ray data and evidence for AGN/hot gas interaction. At higher frequency (1.4 GHz), the HCG 62 cavity system shows minimal if any radio emission, but the new GMRT observations at 235 MHz and 610 MHz clearly detect extended low-frequency emission from radio lobes corresponding to the cavities. By means of the synergy of X-ray and low-frequency radio observations, we compare and discuss the morphology, luminosity and pressure of the gas and of the radio source. We find that the radio source is radiatively inefficient, with a ratio of radio luminosity to mechanical cavity power of $\sim 10^{-4}$, and that the radio pressure of the lobes is about one order of magnitude lower than the X-ray pressure of the surrounding thermal gas. Thanks to the high spatial resolution of the Chandra surface brightness and temperature profiles, we also identify a shock front located at 36 kpc to the south-west of the group center, close to the southern radio lobe, with a Mach number $\sim 1.5$ and a total power which is about one order of magnitude higher than the cavity power. Such a shock may have heated the gas in the southern region, as indicated by the temperature map. The shock may also explain the arc-like region of enriched gas seen in the iron abundance map, as this may be produced by a non-Maxwellian electron distribution near its front.Comment: 14 pages, 8 figures, accepted for publication in ApJ. Revised version including minor comments and expanded discussion (version with full resolution figures available at http://hea-www.harvard.edu/~mgitti/hcg62-gitti.pdf

Growing a Sustainable Biofuels Industry: Economics, Environmental Considerations, and the Role of the Conservation Reserve Program

Biofuels are expected to be a major contributor to renewable energy in the coming decades under the Renewable Fuel Standard (RFS). These fuels have many attractive properties including the promotion of energy independence, rural development, and the reduction of national carbon emissions. However, several unresolved environmental and economic concerns remain. Environmentally, much of the biomass is expected to come from agricultural expansion and/or intensification, which may greatly affect the net environmental impact, and economically, the lack of a developed infrastructure and bottlenecks along the supply chain may affect the industry\u27s economic vitality. The approximately 30 million acres (12 million hectares) under the Conservation Reserve Program (CRP) represent one land base for possible expansion. Here, we examine the potential role of the CRP in biofuels industry development, by (1) assessing the range of environmental effects on six end points of concern, and (2) simulating differences in potential industry growth nationally using a systems dynamics model. The model examines seven land-use scenarios (various percentages of CRP cultivation for biofuel) and five economic scenarios (subsidy schemes) to explore the benefits of using the CRP. The environmental assessment revealed wide variation in potential impacts. Lignocellulosic feedstocks had the greatest potential to improve the environmental condition relative to row crops, but the most plausible impacts were considered to be neutral or slightly negative. Model simulations revealed that industry growth was much more sensitive to economic scenarios than land-use scenarios—similar volumes of biofuels could be produced with no CRP as with 100% utilization. The range of responses to economic policy was substantial, including long-term market stagnation at current levels of first-generation biofuels under minimal policy intervention, or RFS-scale quantities of biofuels if policy or market conditions were more favorable. In total, the combination of the environmental assessment and the supply chain model suggests that large-scale conversion of the CRP to row crops would likely incur a significant environmental cost, without a concomitant benefit in terms of biofuel production

Analyzing the Relationship between Dose and Geometric Agreement Metrics for Auto-Contouring in Head and Neck Normal Tissues

This study aimed to determine the relationship between geometric and dosimetric agreement metrics in head and neck (H&N) cancer radiotherapy plans. A total 287 plans were retrospectively analyzed, comparing auto-contoured and clinically used contours using a Dice similarity coefficient (DSC), surface DSC (sDSC), and Hausdorff distance (HD). Organs-at-risk (OARs) with ≥200 cGy dose differences from the clinical contour in terms of Dmax (D0.01cc) and Dmean were further examined against proximity to the planning target volume (PTV). A secondary set of 91 plans from multiple institutions validated these findings. For 4995 contour pairs across 19 OARs, 90% had a DSC, sDSC, and HD of at least 0.75, 0.86, and less than 7.65 mm, respectively. Dosimetrically, the absolute difference between the two contour sets was \u3c200 cGy for 95% of OARs in terms of Dmax and 96% in terms of Dmean. In total, 97% of OARs exhibiting significant dose differences between the clinically edited contour and auto-contour were within 2.5 cm PTV regardless of geometric agreement. There was an approximately linear trend between geometric agreement and identifying at least 200 cGy dose differences, with higher geometric agreement corresponding to a lower fraction of cases being identified. Analysis of the secondary dataset validated these findings. Geometric indices are approximate indicators of contour quality and identify contours exhibiting significant dosimetric discordance. For a small subset of OARs within 2.5 cm of the PTV, geometric agreement metrics can be misleading in terms of contour quality

Deep Learning-Based Dose Prediction To Improve the Plan Quality of Volumetric Modulated Arc Therapy for Gynecologic Cancers

Background: In recent years, deep‐learning models have been used to predict entire three‐dimensional dose distributions. However, the usability of dose predictions to improve plan quality should be further investigated. Purpose: To develop a deep‐learning model to predict high‐quality dose distributions for volumetric modulated arc therapy (VMAT) plans for patients with gynecologic cancer and to evaluate their usability in driving plan quality improvements. Methods: A total of 79 VMAT plans for the female pelvis were used to train (47 plans), validate (16 plans), and test (16 plans) 3D dense dilated U‐Net models to predict 3D dose distributions. The models received the normalized CT scan, dose prescription, and target and normal tissue contours as inputs. Three models were used to predict the dose distributions for plans in the test set. A radiation oncologist specializing in the treatment of gynecologic cancers scored the test set predictions using a 5‐point scale (5, acceptable as‐is; 4, prefer minor edits; 3, minor edits needed; 2, major edits needed; and 1, unacceptable). The clinical plans for which the dose predictions indicated that improvements could be made were reoptimized with constraints extracted from the predictions. Results: The predicted dose distributions in the test set were of comparable quality to the clinical plans. The mean voxel‐wise dose difference was −0.14 ± 0.46 Gy. The percentage dose differences in the predicted target metrics of D1% and D98% were −1.05% ± 0.59% and 0.21% ± 0.28%, respectively. The dose differences in the predicted organ at risk mean and maximum doses were −0.30 ± 1.66 Gy and −0.42 ± 2.07 Gy, respectively. A radiation oncologist deemed all of the predicted dose distributions clinically acceptable; 12 received a score of 5, and four received a score of 4. Replanning of flagged plans (five plans) showed that the original plans could be further optimized to give dose distributions close to the predicted dose distributions. Conclusions: Deep‐learning dose prediction can be used to predict high‐quality and clinically acceptable dose distributions for VMAT female pelvis plans, which can then be used to identify plans that can be improved with additional optimization

A combined low-radio frequency/X-ray study of galaxy groups I. Giant Metrewave Radio Telescope observations at 235 MHz and 610 MHz

We present new Giant Metrewave Radio Telescope observations at 235 MHz and 610 MHz of 18 X-ray bright galaxy groups. These observations are part of an extended project, presented here and in future papers, which combines low-frequency radio and X-ray data to investigate the interaction between central active galactic nuclei (AGN) and the intra-group medium (IGM). The radio images show a very diverse population of group-central radio sources, varying widely in size, power, morphology and spectral index. Comparison of the radio images with Chandra and XMM-Newton X-ray images shows that groups with significant substructure in the X-ray band and marginal radio emission at >= 1 GHz host low-frequency radio structures that correlate with substructures in IGM. Radio-filled X-ray cavities, the most evident form of AGN/IGM interaction in our sample, are found in half of the systems, and are typically associated with small, low- or mid-power double radio sources. Two systems, NGC5044 and NGC4636, possess multiple cavities, which are isotropically distributed around the group center, possibly due to group weather. In other systems the radio/X-ray correlations are less evident. However, the AGN/IGM interaction can manifest itself through the effects of the high-pressure medium on the morphology, spectral properties and evolution of the radio-emitting plasma. In particular, the IGM can confine fading radio lobes in old/dying radio galaxies and prevent them from dissipating quickly. Evidence for radio emission produced by former outbursts that coexist with current activity is found in six groups of the sample.Comment: Accepted for publication in the Astrophysical Journal Supplement Series, 26 pages, 18 figures. A version with high-quality figures is http://www.astro.umd.edu/~simona/giacintucci_hr.pd

Deep Learning-Based Dose Prediction for Automated, Individualized Quality Assurance of Head and Neck Radiation Therapy Plans

PURPOSE: This study aimed to use deep learning-based dose prediction to assess head and neck (HN) plan quality and identify suboptimal plans. METHODS AND MATERIALS: A total of 245 volumetric modulated arc therapy HN plans were created using RapidPlan knowledge-based planning (KBP). A subset of 112 high-quality plans was selected under the supervision of an HN radiation oncologist. We trained a 3D Dense Dilated U-Net architecture to predict 3-dimensional dose distributions using 3-fold cross-validation on 90 plans. Model inputs included computed tomography images, target prescriptions, and contours for targets and organs at risk (OARs). The model\u27s performance was assessed on the remaining 22 test plans. We then tested the application of the dose prediction model for automated review of plan quality. Dose distributions were predicted on 14 clinical plans. The predicted versus clinical OAR dose metrics were compared to flag OARs with suboptimal normal tissue sparing using a 2 Gy dose difference or 3% dose-volume threshold. OAR flags were compared with manual flags by 3 HN radiation oncologists. RESULTS: The predicted dose distributions were of comparable quality to the KBP plans. The differences between the predicted and KBP-planned D CONCLUSIONS: Deep learning can predict high-quality dose distributions, which can be used as comparative dose distributions for automated, individualized assessment of HN plan quality