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This data-set contains diffraction data of the PR772 virus from four experiments (amo87215, amo06516, amo11416, amox3411) conducted at the LCLS. We provide one CXIDB file per experiment, with a total of 285,944 diffraction patterns, as well as the corresponding xtc files (the native format at the LCLS).
Spacecraft cabin air quality is of fundamental importance to crew health, with concerns encompassing both gaseous contaminants and airborne particles. Quantification of spacecraft indoor aerosols will increase our understanding of crew exposure and cabin cleanliness. Aerosols on the International Space Station (ISS) have been sampled and brought back to Earth for analysis to characterize the airborne particulate matter in the cabin. Microscopic analyses have been performed to determine morphology and particle size information, and Energy Dispersive X-ray Spectroscopy (EDS) provides information on the chemical elements present in the particles. With the use of IntelliSEM software for computer-controlled scanning electron microscopy (CCSEM), this data provides particle size distribution information and statistics on particle materials. Many of the particles collected were made up of multiple elements and had uncommon morphologies compared to typical indoor aerosols on Earth. These characteristics are thought to be from unique formation mechanisms in the microgravity environment. Several notable particle types are examined further in this work. Bromine-containing particles and cadmium-containing particles are discussed as they constitute a health hazard to crew members. Humans in indoor living and working spaces are typically the single largest particle emission source, and this was observed in the sampled aerosols in ISS as well.
The growth of NiCo particles at low coverages over reducible CeO 2 (111) thin films producing a triple interface between Ni-Co-Ce was investigated by scanning tunneling microscopy (STM) and X-ray photoelectron spectroscopy (XPS), which was compared to that of monometallic Ni and Co particles. XPS data show that deposition of either Ni or Co on CeO 2 at 300 K causes a partial reduction of Ce 4+ cations to Ce 3+ ions. At 0.3 monolayer (ML), XPS detects Co 2+ on CeO 2 . However, both Ni 0 and Ni 2+ are present as major species at 300 K and annealing causes a significant increase of Ni 2+ in Ni particles. Deposition of 0.3 ML Co over 0.3 ML Ni on CeO 2 at 300 K induces reduction of Ni 2+ to metallic Ni and Ni 0 was found as predominant species. Unlike for Co/CeO 2 , metallic Co was also present over the Co-Ni/CeO 2 surface in addition to Co 2+ . Further, this behavior indicates the formation of NiCo bimetallic particles with the possibility of Co diffusion to the interface of Ni/ceria. With heating, the intermixing of Ni and Co atoms in bimetallic particles on CeO 2 was facilitated. Furthermore, oxidation of both metals and ceria occurred as a result of the diffusion of lattice oxygen from the bulk of ceria to the surface. A slight increase in Ni 2+ was observed after heating Co-Ni/CeO 2 to 500 K or higher. Co became Co 2+ with heating to 800 K. Our STM results confirm the formation of NiCo bimetallic particles on CeO 2 at 300 K and further suggest that the addition of Co can help inhibit the sintering of Ni particles at higher temperatures. Bimetallic particles were also obtained by depositing Ni over existing Co particles on CeO 2 . However, our XPS data demonstrate that the deposition order of Co and Ni plays a role in the chemical state of these two metals in bimetallic particles, likely attributed to the difference in their compositions at the bimetallic particle surface as well as the metal-support interface.
The primary thrust of this investigation is to develop an algorithm to retrieve cloud particle sizes using ISCCP data. The research under this grant has been successful in obtaining initial results of global distribution of ice-particle sizes. Further research about possible problems caused by nonsphericity of ice particle sizes is currently underway. An algorithm of retrieving ice-cloud particle sizes using ISCCP CX data has been developed. The first survey of ice-particle size in a near-global scale has been completed. Comparison with in situ measurements of ice crystal sizes during FIRE I shows good agreement. The initial results show that the global mean size of ice crystals (De) is about 60 micron. This result is consistent with the range of in situ measurements all over the world if definitions of effective particle size are unified (see next section). The survey also shows that there is no distinct difference of ice-particle sizes between continental and maritime ice-clouds. There are many different definitions of effective particle size used in ice-cloud research. Simple comparisons between values of in situ measurement and satellite remote sensing are misleading and may lead to incorrect conclusions. We reviewed different definitions of effective particle sizes used in the literature and compared their relative magnitudes.
Particle flux data acquired by the ISEE 1 and 2 spacecraft reveal many of magnitude changes within the magnetopause boundary layer that occur during less than 1/4 s of time. These changes define small-scale particle structure, or microstructures within the region, many of which are apparently inclusions of plasma from adjacent regions. Particle structures resolved by the high time resolution data include groups with periods as short as 0.375 s. An estimate is derived for the motion of the microstructures with respect to an oscillatory inner boundary for four afternoon sector boundary layer crossings by measuring interspacecraft time differences as the features propagate past the satellites. On average the microstructures move toward the tail of the magnetosphere in concert with the wavy large-scale inner boundary. Furthermore, the orientations of the edges of the structures maintain an aspect similar to that of the boundary. These characteristics are expected for magnetohydrodynamic plasma flows that generate vorticity due to velocity shear stresses.
Particle accelerators are high power complex machines. To ensure uninterrupted operation of these machines, thousands of pieces of equipment need to be synchronized, which requires addressing many challenges including design, optimization and control, anomaly detection and machine protection. With recent advancements, machine learning (ML) holds promise to assist in more advance prognostics, optimization, and control. While ML based solutions have been developed for several applications in particle accelerators, only few have reached deployment and even fewer to long term usage, due to particle accelerator data distribution drifts caused by changes in both measurable and non-measurable parameters. In this paper, we identify some of the key areas within particle accelerators where continual learning can allow maintenance of ML model performance with distribution drifts. Particularly, we first discuss existing applications of ML in particle accelerators, and their limitations due to distribution drift. Next, we review existing continual learning techniques and investigate their potential applications to address data distribution drifts in accelerators. By identifying the opportunities and challenges in applying continual learning, this paper seeks to open up the new field and inspire more research efforts towards deployable continual learning for particle accelerators.
Langmuir emissions in space are reported to be clumpy and intermittent. The high-frequency wave power appears concentrated in spatial packets, whether amidst the solar wind or on auroral field lines. Due to the plasma motion relative to the spacecraft, determining the source for the wave free energy in the three-dimensional electron distribution function has always been difficult, since the unstable features pass by the detector in presumably too short time to be measured. The range of unstable phase velocities and growth rates have generally been estimated rather than determined by unequivocal measurements. The analysis of wave-particle interactions in a space environment has taken recently a new turn with the development of wave correlators on board rockets and satellites. Such instruments seek to identify correlations between the phase of the wave-field and the fluxes of energetic particles. The data interpretation is complex, however, it must be backed by a detailed theoretical understanding of the wave-particle interaction, including the phase relation for inhomogeneous packets. To this end Langmuir packets interacting with fast electrons can be studied in the appropriate regime by means of particle-in-cell simulations, provided that one succeeds in reducing the level of the fluctuations, enhancing the signal-to-noise ratio, and incorporating the appropriate boundary conditions. The first results of such simulations are presented here as a test and expansion of previous analysis.
Measurements of calibrated backscatter, using two continuous wave Doppler lidars operating at wavelengths 9.1 and 10.6 micrometers were obtained along with cloud particle size distributions in Hurricane Juliette on 21 September 1995 at altitude approximately 11.7 km. Agreement between backscatter from the two lidars and with the cloud particle size distribution is excellent. Features in backscatter and particle number density compare well with concurrent GOES-7 infrared images.
Introduction: We developed a new method that drastically speeds up radiobiological Monte Carlo radiation-track-structure (MC-RTS) calculations on a cell-by-cell basis. Methods: The technique is based on random sampling and superposition of single-particle track (SPT) standard DNA damage (SDD) files from a “pre-calculated” data library, constructed using the RTS code TOPAS-nBio, with “time stamps” manually added to incorporate dose-rate effects. This time-stamped SDD file can then be input into MEDRAS, a mechanistic kinetic model that calculates various radiation-induced biological endpoints, such as DNA double-strand breaks (DSBs), misrepairs and chromosomal aberrations, and cell death. As a benchmark validation of the approach, we calculated the predicted energy-dependent DSB yield and the ratio of direct-to-total DNA damage, both of which agreed with published in vitro experimental data. We subsequently applied the method to perform a superfast cell-by-cell simulation of an experimental in vitro system consisting of neuroendocrine tumor cells uniformly incubated with 177 Lu. Results and discussion: The results for residual DSBs, both at 24 and 48 h post-irradiation, are in line with the published literature values. Our work serves as a proof-of-concept demonstration of the feasibility of a cost-effective “in silico clonogenic cell survival assay” for the computational design and development of radiopharmaceuticals and novel radiotherapy treatments more generally.
Viruses modulate the function(s) of environmentally relevant microbial populations, yet considerations of the metabolic capabilities of individual virus particles themselves are rare. We used shotgun proteomics to quantitatively identify 43 virus-encoded proteins packaged within purified Aureococcus anophagefferens Virus (AaV) particles, normalizing data to the per-virion level using a 9.5-Å-resolution molecular reconstruction of the 1900-Å (AaV) particle that we generated with cryogenic electron microscopy. This packaged proteome was used to determine similarities and differences between members of different giant virus families. We noted that proteins involved in sugar degradation and binding (e.g., carbohydrate lyases) were unique to AaV among characterized giant viruses. To determine the extent to which this virally encoded metabolic capability was ecologically relevant, we examined the TARA Oceans dataset and identified genes and transcripts of viral origin. Our analyses demonstrated that putative giant virus carbohydrate lyases represented up to 17% of the marine pool for this function. In total, our observations suggest that the AaV particle has potential prepackaged metabolic capabilities and that these may be found in other giant viruses that are widespread and abundant in global oceans.
A search for events with one displaced vertex from long-lived particles using data collected by the ATLAS detector at the Large Hadron Collider is presented, using 140 fb −1 of proton-proton collision data at $\sqrt{𝑠}$ =13 TeV recorded in 2015–2018. The search employs techniques for reconstructing vertices of long-lived particles decaying into hadronic jets in the muon spectrometer displaced between 3 m and 14 m from the primary interaction vertex. The observed number of events is consistent with the expected background and limits for several benchmark signals are determined. A scalar-portal model and a Higgs-boson-portal baryogenesis model are considered. A dedicated analysis channel is employed to target Z-boson associated long-lived particle production, including an axionlike particle and a dark photon model. For the Higgs boson model, branching fractions above 1% are excluded at 95% confidence level for long-lived particle proper decay lengths ranging from 5 cm to 40 m. For the photophobic axionlike particle model considered, this search produces the strongest limits to date for proper decay lengths greater than 𝒪(10) cm.
Supersymmetry (SUSY) is one of most elegant extensions of the Standard Model (SM) and explains the puzzles of the SM by providing a candidate to explain the dark matter content of the universe, allowing scientists to understand the origin of the electroweak scale requiring the top mass to be around 170 GeV and leading to the unification of forces at a grand unified scale. Further the minimal supersymmetric standard model (MSSM) predicts the Higgs boson mass to be less than 135 GeV. The discovery of the Higgs Boson with mass around 125 GeV at the LHC has provided a major support to SUSY ideas. Searches for SUSY are ongoing at the Large Hadron Collider (LHC). Direct and indirect dark matter experiments are searching for a particle dark matter candidate which arises most naturally in SUSY models. Proton decay predicted by SUSY grand unified theories is being searched for at deep underground experiments. In addition, recent advances in neutrino and dark matter physics, observational astrophysics, precision cosmology and the promising new window into the cosmos opened by the direct detection of gravitational waves, have brought new ideas on the potential connections between new fundamental particles and our understanding of their impact on the early universe and its evolution. At present, the major questions include: Is SUSY still the best candidate for models beyond the SM? Do we have any well motivated alternative to SUSY? Have we exhausted all possibilities to search for new physics at high and low energy scales? XXVIIth International Conference on Supersymmetry and Unification of Fundamental Interactions (SUSY 2019), hosted by Texas A&M University – Corpus Christi during May 20-24, 2019, provided a unique venue to discus and understand the status of SUSY, connection between particle physics and cosmology, supersymmetry and its alternative, Higgs sector, neutrino sector, flavor sector, dark matter, electroweak phase transition, astroparticle physics, gravitational waves and string theory. Discussion of results from the LHC, recent neutrino experiments and observations, direct and indirect dark matter detection experiments, detection of gravitational waves, data from particle colliders, as well as measurements of the CMB and Large Scale Structure were an integral part of SUSY 2019. To ensure the younger participants will benefit from the conference the most, the conference was preceded by the 4 day long pre-SUSY summer school for graduate students and postdocs. The invited speakers were leading scientists in the fields of SUSY interest. The school took place on Texas A&M University – Corpus Christi campus during the week prior the SUSY 2019 conference (May 15 – 18, 2019). Since its inception in 1993, SUSY has become one of the most important and widely attended international meetings in high energy physics, devoted to new ideas in fundamental particle physics. SUSY 2019 brought together approximately 250 scientists, theorists, phenomenologists, experimentalists and cosmologists, (including over 60 graduate students and 70 postdocs) representing 22 nations: Australia, Belgium, Canada, Chile, China, Colombia, France, Germany, India, Italy, Japan, Mexico, Peru, Portugal, Romania, South Korea, Spain, Sweden, Switzerland, Taiwan, United Kingdom and United States. SUSY 2019 provided a stimulating venue for the exchange of scientific ideas among experts in dark matter, neutrino physics, particle physics, astrophysics and cosmology. The following scientific topics were delivered during SUSY 2019 in form of 44 plenary talks and over 200 parallel talks: Unification of Forces; Electroweak, Top and Higgs Physics; Precision Calculations and MC tools; BSM in Flavor Physics; Neutrino Masses: Models and Phenomenology; Cosmology and Gravitational Waves; Dark Matter, Astroparticle Physics; Formal Field Theory and Strings; Alternatives to Supersymmetry; Quantum Information: Machine Learning/Big Data. 28 talks were given during the pre-SUSY program related to the following topics: Neutrino Physics; Big Data; Collider Physics & SUSY; String Phenomenology; Cosmology; Dark Matter; SUSY Models and Phenomenology
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We use data from Helios, IMP-8, and other spacecraft (e.g. ISEE) to systematically investigate solar energetic particle (SEP) events from different longitudes and distances in the heliosphere. The purpose of the project is to assess empirically the connection between the morphology of the travelling shock and strength with observed enhancements in the flow of energized particles in shock accelerated particle (SEP) events (also often identified as "gradual" solar energetic particle events). Activities during this first year centered on the organization of the SEPs events and their correlation with solar wind observations at multiple spacecraft locations. From an identified list of more than 30 SEPs events at multiple spacecraft locations, currently four single cases for detailed study were selected and are in an advance phase of preparation for publication. Preliminary results of these four cases were presented at AGU Spring and Fall 2003 meetings, and other meetings on SEPs.
Abstract. Tikhonov regularization is a tool for reducing noise amplification during data inversion. This work introduces RegularizationTools.jl, a general-purpose software package for applying Tikhonov regularization to data. The package implements well-established numerical algorithms and is suitable for systems of up to ∼ 1000 equations. Included is an abstraction to systematically categorize specific inversion configurations and their associated hyperparameters. A generic interface translates arbitrary linear forward models defined by a computer function into the corresponding design matrix. This obviates the need to explicitly write out and discretize the Fredholm integral equation, thus facilitating fast prototyping of new regularization schemes associated with measurement techniques. Example applications include the inversion involving data from scanning mobility particle sizers (SMPSs) and humidified tandem differential mobility analyzers (HTDMAs). Inversion of SMPS size distributions reported in this work builds upon the freely available software DifferentialMobilityAnalyzers.jl. The speed of inversion is improved by a factor of ∼ 200, now requiring between 2 and 5 ms per SMPS scan when using 120 size bins. Previously reported occasional failure to converge to a valid solution is reduced by switching from the L-curve method to generalized cross-validation as the metric to search for the optimal regularization parameter. Higher-order inversions resulting in smooth, denoised reconstructions of size distributions are now included in DifferentialMobilityAnalyzers.jl. This work also demonstrates that an SMPS-style matrix-based inversion can be applied to find the growth factor frequency distribution from raw HTDMA data while also accounting for multiply charged particles. The outcome of the aerosol-related inversion methods is showcased by inverting multi-week SMPS and HTDMA datasets from ground-based observations, including SMPS data obtained at Bodega Marine Laboratory during the CalWater 2/ACAPEX campaign and co-located SMPS and HTDMA data collected at the US Department of Energy observatory located at the Southern Great Plains site in Oklahoma, USA. Results show that the proposed approaches are suitable for unsupervised, nonparametric inversion of large-scale datasets as well as inversion in real time during data acquisition on low-cost reduced-instruction-set architectures used in single-board computers. The included software implementation of Tikhonov regularization is freely available, general, and domain-independent and thus can be applied to many other inverse problems arising in atmospheric measurement techniques and beyond.
Cloud microphysical parameterizations have attracted a great deal of attention in recent years due to their effect on cloud radiative properties and cloud-related hydrological processes in large-scale models. The parameterization of cirrus particle size has been demonstrated as an indispensable component in the climate feedback analysis. Therefore, global-scale, long-term observations of cirrus particle sizes are required both as a basis of and as a validation of parameterizations for climate models. While there is a global scale, long-term survey of water cloud droplet sizes (Han et al. 1994), there is no comparable study for cirrus ice crystals. In this paper a near-global survey of cirrus ice crystal sizes is conducted using ISCCP satellite data analysis. The retrieval scheme uses phase functions based upon hexagonal crystals calculated by a ray tracing technique. The results show that global mean values of D(e) are about 60 micro-m. This study also investigates the possible reasons for the significant difference between satellite retrieved effective radii (approx. 60 micro-m) and aircraft measured particle sizes (approx. 200 micro-m) during the FIRE I IFO experiment. They are (1) vertical inhomogeneity of cirrus particle sizes; (2) lower limit of the instrument used in aircraft measurements; (3) different definitions of effective particle sizes; and (4) possible inappropriate phase functions used in satellite retrieval.
In order to visualize and investigate spray structures, computed tomography technique is applied to analyze droplet information. From the transmitted light intensity through the spray and/or the data of particle size distribution obtained from a Fraunhofer diffraction principle, the quantitative volume of spray droplet or local particle size was calculated and the reconstruction of spray structures was made. The background of computed tomography is described along with some experimental results of the structure of intermittent spray such as diesel spray.