PopCORN: Hunting down the differences between binary population synthesis codes

Binary population synthesis (BPS) modelling is a very effective tool to study the evolution and properties of close binary systems. The uncertainty in the parameters of the model and their effect on a population can be tested in a statistical way, which then leads to a deeper understanding of the underlying physical processes involved. To understand the predictive power of BPS codes, we study the similarities and differences in the predicted populations of four different BPS codes for low- and intermediate-mass binaries. We investigate whether the differences are caused by different assumptions made in the BPS codes or by numerical effects. To simplify the complex problem of comparing BPS codes, we equalise the inherent assumptions as much as possible. We find that the simulated populations are similar between the codes. Regarding the population of binaries with one WD, there is very good agreement between the physical characteristics, the evolutionary channels that lead to the birth of these systems, and their birthrates. Regarding the double WD population, there is a good agreement on which evolutionary channels exist to create double WDs and a rough agreement on the characteristics of the double WD population. Regarding which progenitor systems lead to a single and double WD system and which systems do not, the four codes agree well. Most importantly, we find that for these two populations, the differences in the predictions from the four codes are not due to numerical differences, but because of different inherent assumptions. We identify critical assumptions for BPS studies that need to be studied in more detail.Comment: 13 pages, +21 pages appendix, 35 figures, accepted for publishing in A&A, Minor change to match published version, most important the added link to the website http://www.astro.ru.nl/~silviato/popcorn for more detailed figures and informatio

Progenitors of Supernovae Type Ia

Despite the significance of Type Ia supernovae (SNeIa) in many fields in astrophysics, SNeIa lack a theoretical explanation. The standard scenarios involve thermonuclear explosions of carbon/oxygen white dwarfs approaching the Chandrasekhar mass; either by accretion from a companion or by a merger of two white dwarfs. We investigate the contribution from both channels to the SNIa rate with the binary population synthesis (BPS) code SeBa in order to constrain binary processes such as the mass retention efficiency of WD accretion and common envelope evolution. We determine the theoretical rates and delay time distribution of SNIa progenitors and in particular study how assumptions affect the predicted rates.Comment: 6 pages, 6 figures, appeared in proceedings for "The 18th European White Dwarf Workshop

USCT data challenge 2019

Recent years have witnessed the active development of scanning systems and reconstructionalgorithms for ultrasound computed tomography (USCT) with applications to breast imagingfor early cancer detection. Despite these advances in hardware and software development,we encounter the need for reference data to develop, test and compare different imagingmethods. With the goals of encouraging scientific exchange and collaborations, providingbenchmarks of reconstruction algorithms, and standardizing USCT data formats, we havereleased open-source data sets of simulated waveforms that mimic measurements of a USCTscanning aperture using numerical breast phantoms. This is part of ongoing efforts centeredaround the USCT platform for data exchange and collaboration

Fast Auto-adaptive Gain Adaption for Improved Signal Dynamics

In our 3D Ultrasound Computer Tomography system (USCT), the 12 bit ADC and factor 10 VGA are insufficient to resolve the smallest interesting signals. An adaptive front-end gain can solve this by object specific adaptions during the measurement. The 3D USCT II of the KIT device contains 157 Transmitter Array System (TAS). Each TAS has 13 piezoelectric transducers, corresponding analog signal front end (AFE) and an MSP430FG66xx series microcontroller (MCU). All TAS are connected to a control board through a two-wire serial bus system. Direct Memory Access (DMA) was used in the hardware to control the interrupt of the Universal Serial Communication Interfaces module (USCI). To complete the data transfer without occupying the MCUs of the TAS. A location-based general call was developed in the control system. The host transmits one frame long message to all TAS in a general call mode. This message contains the configurations of all TAS for the next measurement step. The address of each TAS corresponds to the location of each configuration in the long message. Thus, in the broadcast mode, each TAS only obtains the configuration information required by itself. With these two improvements, to configure all of the TAS can be reduced to less than 3 ms, which is the shortest measurement interval. The here proposed solution allows a fast dynamic control of the front-end electronics during measurement without extending the measurement time significantly

X-ray Synthesis Based on Triangular Mesh Models Using GPU-Accelerated Ray Tracing for Multi-modal Breast Image Registration

For image registration of breast MRI and X-ray mammography we apply detailed biomechanical models. Synthesizing X-ray mammograms from these models is an important processing step for optimizing registration parameters and deriving images for multi-modal diagnosis. A fast computation time for creating synthetic images is essential to enable a clinically relevant application. In this paper we present a method to create synthetic X-ray attenuation images with an hardware-optimized ray tracing algorithm on recent graphics processing units’ (GPU) ray tracing (RT) cores. The ray tracing algorithm is able to calculate the attenuation of the X-rays by tracing through a triangular polygon-mesh. We use the Vulkan API, which enables access to RT cores. One frame for a triangle mesh with over 5 million triangles in the mesh and a detector resolution of 1080×1080 can be calculated and transferred to and from the GPU in about 0.76 s on NVidia RTX 2070 Super GPU. Calculation duration of an interactive application without the transfer overhead allows real time application with more than 30 frames per second (fps) even for very large polygon models. The presented method is able to calculate synthetic X-ray images in a short time and has the potential for real-time applications. Also it is the very first implementation using RT cores for this purpose. The toolbox will be available as an open source

Sowing Date Affects Dry Matter Yield of Fodder Beet (\u3ci\u3eBeta vulgaris\u3c/i\u3e L.) Crops and Farm Profitability

Fodder beet (Beta vulgaris L.) is a high yielding and high energy supplementary ruminant feed. Including the crop in a pasture system means loss in production and additional costs during crop establishment, but economic benefits may be recovered with increased seasonal productivity and feed quality. In this study, the Agricultural Production Systems sIMulator was used to estimate herbage production of a typical dairy farm in the Canterbury region of New Zealand based on using a ryegrass (Lolium perenne L.)-clover (Trifolium repens L.) pasture (“Pasture only”) or pasture in combination with fodder beet as winter feed (“Pasture+Fodder beet (FB)”). Mean yields of pasture were used to estimate the potential pasture yield lost from spraying out paddocks 1 month before establishing fodder beet. Fodder beet (‘Rivage’) yields from a 2014 sowing date trial: 19 September (Sep-FB), 17 October (Oct-FB), 17 November (Nov-FB), and 15 December (Dec-FB) were used. Dry matter (DM) yield was determined on 15 June 2015. Yield was 27 t DM/ha for both Sep-FB and Oct-FB and was reduced by 23 and 32% in Nov-FB and Dec-FB, respectively. The total annual yield for “Pasture only” was 16.7 t DM/ha compared with adjusted yield of 29.5, 30.2, 25.7 and 24.9 t DM/ha for “Pasture+Sep-FB”, “Pasture+Oct-FB”, “Pasture+Nov-FB” and “Pasture+Dec-FB”, respectively. Production cost was NZ$0.08/kg DM each for “Pasture+Sep-FB” and “Pasture+Oct-FB”, which was lower than NZ$0.09/kg DM for “Pasture only”. Production costs increased to NZ$0.11/kg DM for “Pasture+Nov-FB” and NZ$0.12/kg DM for “Pasture+Dec-FB”, but revenue from sale of surplus feed partially offset these costs. Our results show that sowing in October was the most profitable option. Yield gains from sowing fodder beet in September are unlikely because of low temperatures limiting crop growth. Delaying sowing can increase production costs and yield penalty, but potential returns are greater, compared with “Pasture only”