Magnetically-driven explosions of rapidly-rotating white dwarfs following Accretion-Induced Collapse

We present 2D multi-group flux-limited diffusion magnetohydrodynamics (MHD) simulations of the Accretion-Induced Collapse (AIC) of a rapidly-rotating white dwarf. We focus on the dynamical role of MHD processes after the formation of a millisecond-period protoneutron star. We find that including magnetic fields and stresses can lead to a powerful explosion with an energy of a few Bethe, rather than a weak one of at most 0.1 Bethe, with an associated ejecta mass of ~0.1Msun, instead of a few 0.001Msun. The core is spun down by ~30% within 500ms after bounce, and the rotational energy extracted from the core is channeled into magnetic energy that generates a strong magnetically-driven wind, rather than a weak neutrino-driven wind. Baryon loading of the ejecta, while this wind prevails, precludes it from becoming relativistic. This suggests that a GRB is not expected to emerge from such AICs during the early protoneutron star phase, except in the unlikely event that the massive white dwarf has sufficient mass to lead to black hole formation. In addition, we predict both negligible 56Ni-production (that should result in an optically-dark, adiabatically-cooled explosion) and the ejection of 0.1Msun of material with an electron fraction of 0.1-0.2. Such pollution by neutron-rich nuclei puts strong constraints on the possible rate of such AICs. Moreover, being free from ``fallback,'' such highly-magnetized millisecond-period protoneutron stars may later become magnetars, and the magnetically-driven winds may later transition to Poynting-flux-dominated, relativistic winds, eventually detectable as GRBs at cosmological distances. However, the low expected event rate of AICs will constrain them to be, at best, a small subset of GRB and/or magnetar progenitors.Comment: 16 pages, 8 figures, paper accepted to ApJ; High resolution version available at http://hermes.as.arizona.edu/~luc/aic_mhd/aic_mhd.htm

Parallel Wavelet Tree Construction

We present parallel algorithms for wavelet tree construction with polylogarithmic depth, improving upon the linear depth of the recent parallel algorithms by Fuentes-Sepulveda et al. We experimentally show on a 40-core machine with two-way hyper-threading that we outperform the existing parallel algorithms by 1.3--5.6x and achieve up to 27x speedup over the sequential algorithm on a variety of real-world and artificial inputs. Our algorithms show good scalability with increasing thread count, input size and alphabet size. We also discuss extensions to variants of the standard wavelet tree.Comment: This is a longer version of the paper that appears in the Proceedings of the IEEE Data Compression Conference, 201

The Proto-neutron Star Phase of the Collapsar Model and the Route to Long-soft Gamma-ray Bursts and Hypernovae

Recent stellar evolutionary calculations of low-metallicity massive fast-rotating main-sequence stars yield iron cores at collapse endowed with high angular momentum. It is thought that high angular momentum and black hole formation are critical ingredients of the collapsar model of long-soft gamma-ray bursts (GRBs). Here, we present 2D multi-group, flux-limited-diffusion MHD simulations of the collapse, bounce, and immediate post-bounce phases of a 35-Msun collapsar-candidate model of Woosley & Heger. We find that, provided the magneto-rotational instability (MRI) operates in the differentially-rotating surface layers of the millisecond-period neutron star, a magnetically-driven explosion ensues during the proto-neutron star phase, in the form of a baryon-loaded non-relativistic jet, and that a black hole, central to the collapsar model, does not form. Paradoxically, and although much uncertainty surrounds stellar mass loss, angular momentum transport, magnetic fields, and the MRI, current models of chemically homogeneous evolution at low metallicity yield massive stars with iron cores that may have too much angular momentum to avoid a magnetically-driven, hypernova-like, explosion in the immediate post-bounce phase. We surmise that fast rotation in the iron core may inhibit, rather than enable, collapsar formation, which requires a large angular momentum not in the core but above it. Variations in the angular momentum distribution of massive stars at core collapse might explain both the diversity of Type Ic supernovae/hypernovae and their possible association with a GRB. A corollary might be that, rather than the progenitor mass, the angular momentum distribution, through its effect on magnetic field amplification, distinguishes these outcomes.Comment: 5 pages, 1 table, 2 figures, accepted to ApJ

GPU-Accelerated BWT Construction for Large Collection of Short Reads

Advances in DNA sequencing technology have stimulated the development of algorithms and tools for processing very large collections of short strings (reads). Short-read alignment and assembly are among the most well-studied problems. Many state-of-the-art aligners, at their core, have used the Burrows-Wheeler transform (BWT) as a main-memory index of a reference genome (typical example, NCBI human genome). Recently, BWT has also found its use in string-graph assembly, for indexing the reads (i.e., raw data from DNA sequencers). In a typical data set, the volume of reads is tens of times of the sequenced genome and can be up to 100 Gigabases. Note that a reference genome is relatively stable and computing the index is not a frequent task. For reads, the index has to computed from scratch for each given input. The ability of efficient BWT construction becomes a much bigger concern than before. In this paper, we present a practical method called CX1 for constructing the BWT of very large string collections. CX1 is the first tool that can take advantage of the parallelism given by a graphics processing unit (GPU, a relative cheap device providing a thousand or more primitive cores), as well as simultaneously the parallelism from a multi-core CPU and more interestingly, from a cluster of GPU-enabled nodes. Using CX1, the BWT of a short-read collection of up to 100 Gigabases can be constructed in less than 2 hours using a machine equipped with a quad-core CPU and a GPU, or in about 43 minutes using a cluster with 4 such machines (the speedup is almost linear after excluding the first 16 minutes for loading the reads from the hard disk). The previously fastest tool BRC is measured to take 12 hours to process 100 Gigabases on one machine; it is non-trivial how BRC can be parallelized to take advantage a cluster of machines, let alone GPUs.Comment: 11 page

Better quality score compression through sequence-based quality smoothing

Current NGS techniques are becoming exponentially cheaper. As a result, there is an exponential growth of genomic data unfortunately not followed by an exponential growth of storage, leading to the necessity of compression. Most of the entropy of NGS data lies in the quality values associated to each read. Those values are often more diversified than necessary. Because of that, many tools such as Quartz or GeneCodeq, try to change (smooth) quality scores in order to improve compressibility without altering the important information they carry for downstream analysis like SNP calling