Engineering topics

De, K.

Publications and source records attributed to De, K..

13 records

DOE OSTI2024

The complex circumstellar environment of supernova 2023ixf

The early evolution of a supernova (SN) can reveal information about the environment and the progenitor star. When a star explodes in vacuum, the first photons to escape from its surface appear as a brief, hours-long shock-breakout flare, followed by a cooling phase of emission. However, for stars exploding within a distribution of dense, optically thick circumstellar material (CSM), the first photons escape from the material beyond the stellar edge and the duration of the initial flare can extend to several days, during which the escaping emission indicates photospheric heating. Early serendipitous observations that lacked ultraviolet (UV) data were unable to determine whether the early emission is heating or cooling and hence the nature of the early explosion event. Here, in this study, we report UV spectra of the nearby SN 2023ixf in the galaxy Messier 101 (M101). Using the UV data as well as a comprehensive set of further multiwavelength observations, we temporally resolve the emergence of the explosion shock from a thick medium heated by the SN emission. We derive a reliable bolometric light curve that indicates that the shock breaks out from a dense layer with a radius substantially larger than typical supergiants.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

SN 2020bqj: A Type Ibn supernova with a long-lasting peak plateau

We report type Ibn supernovae (SNe Ibn) are a rare class of stripped envelope supernovae interacting with a helium-rich circumstellar medium (CSM). The majority of the SNe Ibn reported in the literature display a surprising homogeneity in their fast-evolving lightcurves and are typically found in actively starforming spiral galaxies. We present the discovery and the study of SN 2020bqj (ZTF20aalrqbu), a SN Ibn with a long-duration peak plateau lasting 40 days and hosted by a faint low-mass galaxy. We aim to explain its peculiar properties using an extensive photometric and spectroscopic data set. We compare the photometric and spectral evolution of SN 2020bqj with regular SNe Ibn from the literature, as well as with other outliers in the SN Ibn subclass. We fit the bolometric and multi-band lightcurves with powering mechanism models such as radioactive decay and CSM interaction. We also model the host galaxy of SN 2020bqj. The risetime, peak magnitude and spectral features of SN 2020bqj are consistent with those of most SNe Ibn, but the SN is a clear outlier in the subclass based on its bright, long-lasting peak plateau and the low mass of its faint host galaxy. We show through modeling that the lightcurve of SN 2020bqj can be powered predominantly by shock heating from the interaction of the SN ejecta and a dense CSM, combined with radioactive decay. The peculiar Type Ibn SN 2011hw is a close analog to SN 2020bqj in terms of lightcurve and spectral evolution, suggesting a similar progenitor and CSM scenario. In this scenario a very massive progenitor star in the transitional phase between a luminous blue variable and a compact Wolf-Rayet star undergoes core-collapse, embedded in a dense helium-rich CSM with an elevated opacity compared to normal SNe Ibn, due to the presence of residual hydrogen. This scenario is consistent with the observed properties of SN 2020bqj and the modeling results. SN 2020bqj is a compelling example of a transitional SN Ibn/IIn based on not only its spectral features, but also its lightcurve, host galaxy properties and the inferred progenitor properties. The strong similarity with SN 2011hw suggests this subclass may be the result of a progenitor in a stellar evolution phase that is distinct from those of progenitors of regular SNe Ibn.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

SN 2018ijp: the explosion of a stripped-envelope star within a dense H-rich shell?

In this work, we discuss the outcomes of the follow-up campaign of SN 2018ijp, discovered as part of the Zwicky Transient Facility survey for optical transients. Its first spectrum shows similarities to broad-lined Type Ic supernovae around maximum light, whereas later spectra display strong signatures of interaction between rapidly expanding ejecta and a dense H-rich circumstellar medium, coinciding with a second peak in the photometric evolution of the transient. This evolution, along with the results of modeling of the first light-curve peak, suggests a scenario where a stripped star exploded within a dense circumstellar medium. The two main phases in the evolution of the transient could be interpreted as a first phase dominated by radioactive decays, and a later interaction-dominated phase where the ejecta collide with a pre-existing shell. We therefore discuss SN 2018jp within the context of a massive star depleted of its outer layers exploding within a dense H-rich circumstellar medium.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

The Spectacular Ultraviolet Flash from the Peculiar Type Ia Supernova 2019yvq

Early observations of Type Ia supernovae (SNe Ia) provide essential clues for understanding the progenitor system that gave rise to the terminal thermonuclear explosion. Here, we present exquisite observations of SN 2019yvq, the second observed SN Ia, after iPTF 14atg, to display an early flash of emission in the ultraviolet (UV) and optical. Our analysis finds that SN 2019yvq was unusual, even when ignoring the initial flash, in that it was moderately underluminous for a SN Ia ($M_g \approx -18.5$ mag at peak) yet featured very high absorption velocities ($v \approx 15,0000$ km s -1 for Si II λ6355 at peak). We find that many of the observational features of SN 2019yvq, aside from the flash, can be explained if the explosive yield of radioactive 56 Ni is relatively low (we measure $M_{^{56}\mathrm{Ni}} = 0.31 \pm 0.05\,M_\odot$) and it and other iron-group elements are concentrated in the innermost layers of the ejecta. To explain both the UV/optical flash and peak properties of SN 2019yvq we consider four different models: interaction between the SN ejecta and a nondegenerate companion, extended clumps of 56 Ni in the outer ejecta, a double-detonation explosion, and the violent merger of two white dwarfs. Each of these models has shortcomings when compared to the observations; it is clear additional tuning is required to better match SN 2019yvq. In closing, we predict that the nebular spectra of SN 2019yvq will feature either H or He emission, if the ejecta collided with a companion, strong [Ca II] emission, if it was a double detonation, or narrow [O I] emission, if it was due to a violent merger.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2018

A hot and fast ultra-stripped supernova that likely formed a compact neutron star binary

Compact neutron star binary systems are produced from binary massive stars through stellar evolution involving up to two supernova explosions. The final stages in the formation of these systems have not been directly observed. Here, we report the discovery of iPTF 14gqr (SN 2014ft), a type Ic supernova with a fast-evolving light curve indicating an extremely low ejecta mass (≈0.2 solar masses) and low kinetic energy (≈2 × 10 50 ergs). Early photometry and spectroscopy reveal evidence of shock cooling of an extended helium-rich envelope, likely ejected in an intense pre-explosion mass-loss episode of the progenitor. Taken together, we interpret iPTF 14gqr as evidence for ultra-stripped supernovae that form neutron stars in compact binary systems.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2018

Supercomputers, Clouds and Grids powered by BigPanDA for Brain studies

The PanDA WMS - Production and Distributed Analysis Workload Management System - has been developed and used by the ATLAS experiment at the LHC (Large Hadron Collider) for all data processing and analysis challenges. BigPanDA is an extension of the PanDA WMS to run ATLAS and non-ATLAS applications on Leadership Class Facilities and supercomputers, as well as traditional grid and cloud resources. The success of the BigPanDA project has drawn attention from other compute intensive sciences such as biology. In 2017, a pilot project was started between BigPanDA and the Blue Brain Project (BBP) of the Ecole Polytechnique Federal de Lausanne (EPFL) located in Lausanne, Switzerland. This proof of concept project is aimed at demonstrating the efficient application of the BigPanDA system to support the complex scientific workflow of the BBP, which relies on using a mix of desktop, cluster and supercomputers to reconstruct and simulate accurate models of brain tissue.

Computer Science↗

DOE OSTI2017

Illuminating gravitational waves: A concordant picture of photons from a neutron star merger

Merging neutron stars offer an excellent laboratory for simultaneously studying strong-field gravity and matter in extreme environments. We establish the physical association of an electromagnetic counterpart (EM170817) with gravitational waves (GW170817) detected from merging neutron stars. By synthesizing a panchromatic data set, we demonstrate that merging neutron stars are a long-sought production site forging heavy elements by r-process nucleosynthesis. The weak gamma rays seen in EM170817 are dissimilar to classical short gamma-ray bursts with ultrarelativistic jets. Instead, we suggest that breakout of a wide-angle, mildly relativistic cocoon engulfing the jet explains the low-luminosity gamma rays, the high-luminosity ultraviolet-optical-infrared, and the delayed radio and x-ray emission. We posit that all neutron star mergers may lead to a wide-angle cocoon breakout, sometimes accompanied by a successful jet and sometimes by a choked jet.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2017

Integration of Titan supercomputer at OLCF with ATLAS Production System

The PanDA (Production and Distributed Analysis) workload management system was developed to meet the scale and complexity of distributed computing for the ATLAS experiment. PanDA managed resources are distributed worldwide, on hundreds of computing sites, with thousands of physicists accessing hundreds of Petabytes of data and the rate of data processing already exceeds Exabyte per year. While PanDA currently uses more than 200,000 cores at well over 100 Grid sites, future LHC data taking runs will require more resources than Grid computing can possibly provide. Additional computing and storage resources are required. Therefore ATLAS is engaged in an ambitious program to expand the current computing model to include additional resources such as the opportunistic use of supercomputers. In this paper we will describe a project aimed at integration of ATLAS Production System with Titan supercomputer at Oak Ridge Leadership Computing Facility (OLCF). Current approach utilizes modified PanDA Pilot framework for job submission to Titan's batch queues and local data management, with lightweight MPI wrappers to run single node workloads in parallel on Titan's multi-core worker nodes. It provides for running of standard ATLAS production jobs on unused resources (backfill) on Titan. The system already allowed ATLAS to collect on Titan millions of core-hours per month, execute hundreds of thousands jobs, while simultaneously improving Titans utilization efficiency. We will discuss the details of the implementation, current experience with running the system, as well as future plans aimed at improvements in scalability and efficiency.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

Integration Of PanDA Workload Management System With Supercomputers for ATLAS and Data Intensive Science

The.LHC, operating at CERN, is leading Big Data driven scientific explorations. Experiments at the LHC explore the fundamental nature of matter and the basic forces that shape our universe. ATLAS, one of the largest collaborations ever assembled in the sciences, is at the forefront of research at the LHC. To address an unprecedented multi-petabyte data processing challenge, the ATLAS experiment is relying on a heterogeneous distributed computational infrastructure. The ATLAS experiment uses PanDA (Production and Data Analysis) Workload Management System for managing the workflow for all data processing on over 150 data centers. Through PanDA, ATLAS physicists see a single computing facility that enables rapid scientific breakthroughs for the experiment, even though the data centers are physically scattered all over the world. While PanDA currently uses more than 250,000 cores with a peak performance of 0.3 petaFLOPS, LHC data taking runs require more resources than grid can possibly provide. To alleviate these challenges, LHC experiments are engaged in an ambitious program to expand the current computing model to include additional resources such as the opportunistic use of supercomputers. We will describe a project aimed at integration of PanDA WMS with supercomputers in United States, in particular with Titan supercomputer at Oak Ridge Leadership Computing Facility. Current approach utilizes modified PanDA pilot framework for job submission to the supercomputers batch queues and local data management, with light-weight MPI wrappers to run single threaded workloads in parallel on LCFs multi-core worker nodes. This implementation was tested with a variety of Monte-Carlo workloads on several supercomputing platforms for ALICE and ATLAS experiments and it is in full pro duction for the ATLAS since September 2015. We will present our current accomplishments with running PanDA at supercomputers and demonstrate our ability to use PanDA as a portal independent of the computing facilities infrastructure for High Energy and Nuclear Physics as well as other data-intensive science applications, such as bioinformatics and astro-particle physics.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

Accelerating Science Impact through Big Data Workflow Management and Supercomputing

The Large Hadron Collider (LHC), operating at the international CERN Laboratory in Geneva, Switzerland, is leading Big Data driven scientific explorations. ATLAS, one of the largest collaborations ever assembled in the the history of science, is at the forefront of research at the LHC. To address an unprecedented multi-petabyte data processing challenge, the ATLAS experiment is relying on a heterogeneous distributed computational infrastructure. To manage the workflow for all data processing on hundreds of data centers the PanDA (Production and Distributed Analysis)Workload Management System is used. An ambitious program to expand PanDA to all available computing resources, including opportunistic use of commercial and academic clouds and Leadership Computing Facilities (LCF), is realizing within BigPanDA and megaPanDA projects. These projects are now exploring how PanDA might be used for managing computing jobs that run on supercomputers including OLCF’s Titan and NRC-KI HPC2. The main idea is to reuse, as much as possible, existing components of the PanDA system that are already deployed on the LHC Grid for analysis of physics data. The next generation of PanDA will allow many data-intensive sciences employing a variety of computing platforms to benefit from ATLAS experience and proven tools in highly scalable processing.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

Integration of Russian Tier-1 Grid Center with High Performance Computers at NRC-KI for LHC experiments and beyond HENP

The LHC experiments are preparing for the precision measurements and further discoveries that will be made possible by higher LHC energies from April 2015 (LHC Run2). The need for simulation, data processing and analysis would overwhelm the expected capacity of grid infrastructure computing facilities deployed by the Worldwide LHC Computing Grid (WLCG). To meet this challenge the integration of the opportunistic resources into LHC computing model is highly important. The Tier-1 facility at Kurchatov Institute (NRC-KI) in Moscow is a part of WLCG and it will process, simulate and store up to 10% of total data obtained from ALICE, ATLAS and LHCb experiments. In addition Kurchatov Institute has supercomputers with peak performance 0.12 PFLOPS. The delegation of even a fraction of supercomputing resources to the LHC Computing will notably increase total capacity. In 2014 the development a portal combining a Tier-1 and a supercomputer in Kurchatov Institute was started to provide common interfaces and storage. The portal will be used not only for HENP experiments, but also by other data- and compute-intensive sciences like biology with genome sequencing analysis; astrophysics with cosmic rays analysis, antimatter and dark matter search, etc.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

Integration of PanDA workload management system with Titan supercomputer at OLCF

The PanDA (Production and Distributed Analysis) workload management system (WMS) was developed to meet the scale and complexity of LHC distributed computing for the ATLAS experiment. While PanDA currently distributes jobs to more than 100,000 cores at well over 100 Grid sites, the future LHC data taking runs will require more resources than Grid computing can possibly provide. To alleviate these challenges, ATLAS is engaged in an ambitious program to expand the current computing model to include additional resources such as the opportunistic use of supercomputers. We will describe a project aimed at integration of PanDA WMS with Titan supercomputer at Oak Ridge Leadership Computing Facility (OLCF). The current approach utilizes a modified PanDA pilot framework for job submission to Titan’s batch queues and local data management, with light-weight MPI wrappers to run single threaded workloads in parallel on Titan’s multi-core worker nodes. It also gives PanDA new capability to collect, in real time, information about unused worker nodes on Titan, which allows precise definition of the size and duration of jobs submitted to Titan according to available free resources. This capability significantly reduces PanDA job wait time while improving Titan’s utilization efficiency. This implementation was tested with a variety of Monte-Carlo workloads on Titan and is being tested on several other supercomputing platforms.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

Next Generation Workload Management System For Big Data on Heterogeneous Distributed Computing

The Large Hadron Collider (LHC), operating at the international CERN Laboratory in Geneva, Switzerland, is leading Big Data driven scientific explorations. Experiments at the LHC explore the fundamental nature of matter and the basic forces that shape our universe, and were recently credited for the discovery of a Higgs boson. ATLAS and ALICE are the largest collaborations ever assembled in the sciences and are at the forefront of research at the LHC. To address an unprecedented multi-petabyte data processing challenge, both experiments rely on a heterogeneous distributed computational infrastructure. The ATLAS experiment uses PanDA (Production and Data Analysis) Workload Management System (WMS) for managing the workflow for all data processing on hundreds of data centers. Through PanDA, ATLAS physicists see a single computing facility that enables rapid scientific breakthroughs for the experiment, even though the data centers are physically scattered all over the world. The scale is demonstrated by the following numbers: PanDA manages O(10 2 ) sites, O(10 5 ) cores, O(10 8 ) jobs per year, O(10 3 ) users, and ATLAS data volume is O(10 17 ) bytes. In 2013 we started an ambitious program to expand PanDA to all available computing resources, including opportunistic use of commercial and academic clouds and Leadership Computing Facilities (LCF). The project titled 'Next Generation Workload Management and Analysis System for Big Data' (BigPanDA) is funded by DOE ASCR and HEP. Extending PanDA to clouds and LCF presents new challenges in managing heterogeneity and supporting workflow. The BigPanDA project is underway to setup and tailor PanDA at the Oak Ridge Leadership Computing Facility (OLCF) and at the National Research Center "Kurchatov Institute" together with ALICE distributed computing and ORNL computing professionals. Our approach to integration of HPC platforms at the OLCF and elsewhere is to reuse, as much as possible, existing components of the PanDA system. Finally, we will present our current accomplishments with running the PanDA WMS at OLCF and other supercomputers and demonstrate our ability to use PanDA as a portal independent of the computing facilities infrastructure for High Energy and Nuclear Physics as well as other data-intensive science applications.

97 MATHEMATICS AND COMPUTING↗