Results for “New physics” — Page 99

NASA NTRS2010

The Spectral Energy Distribution of Fermi Bright Blazars

We have conducted a detailed investigation of the broadband spectral properties of the gamma-ray selected blazars of the Fermi LAT Bright AGN Sample (LBAS). By combining our accurately estimated Fermi gamma-ray spectra with Swift, radio, infra-red, optical, and other hard X-ray /gamma-ray data, collected within 3 months of the LBAS data taking period, we were able to assemble high-quality and quasi-simultaneous spectral energy distributions (SED) for 48 LBAS blazars. The SED of these gamma-ray sources is similar to that of blazars discovered at other wavelengths, clearly showing, in the usual log v-log v Fv representation, the typical broadband spectral signatures normally attributed to a combination of low-energy synchrotron radiation followed by inverse Compton emission of one or more components. We have used these SED to characterize the peak intensity of both the low- and the high-energy components. The results have been used to derive empirical relationships that estimate the position of the two peaks from the broadband colors (i.e., the radio to optical, alpha(sub ro) , and optical to X-ray, alpha(sub ox), spectral slopes) and from the gamma-ray spectral index. Our data show that the synchrotron peak frequency (v(sup S) (sub peak)) is positioned between 10(exp 12.5) and 10(exp 14) Hz in broad-lined flat spectrum radio quasars (FSRQs) and between 10(exp 13) and 10(exp 17) Hz in featureless BL Lacertae objects. We find that the gamma-ray spectral slope is strongly correlated with the synchrotron peak energy and with the X-ray spectral index, as expected at first order in synchrotron-inverse Compton scenarios. However, simple homogeneous, one-zone, synchrotron self-Compton (SSC) models cannot explain most of our SED, especially in the case of FSRQs and low energy peaked (LBL) BL Lacs. More complex models involving external Compton radiation or multiple SSC components are required to reproduce the overall SED and the observed spectral variability. While more than 50% of known radio bright high energy peaked (HBL) BL Lacs are detected in the LBAS sample, only less than 13% of known bright FSRQs and LBL BL Lacs are included. This suggests that the latter sources, as a class, may be much fainter gamma-ray emitters than LBAS blazars, and could in fact radiate close to the expectations of simple SSC models. We categorized all our sources according to a new physical classification scheme based on the generally accepted paradigm for Active Galactic Nuclei and on the results of this SED study. Since the LAT detector is more sensitive to flat spectrum gamma-ray sources, the correlation between v(sup S) (sub peak) and gamma-ray spectral index strongly favors the detection of high energy peaked blazars, thus explaining the Fermi overabundance of this type of sources compared to radio and EGRET samples. This selection effect is similar to that experienced in the soft X-ray band where HBL BL Lacs are the dominant type of blazars.

Abdo, A. A.↗

NASA NTRS2017

Si III OV Bright Line of Scattering Polarized Light That Has Been Observed in the CLASP and Its Center-to-Limb Variation

The CLASP (Chromospheric Lyman-Alpha Spectro- Polarimeter) rocket experiment, in addition to the ultraviolet region of the Ly alpha emission line (121.57 nm), emission lines of Si III (120.65 nm) and OV (121.83 nm) is can be observed. These are optically thin line compared to a Ly alpha line, if Rarere captured its polarization, there is a possibility that dripping even a new physical diagnosis chromosphere-transition layer. In particular, OV bright light is a release from the transition layer, further, three P one to one S(sub 0) is a forbidden line (cross-triplet transition between lines), it was not quite know whether to polarization.

spectropolarimeter↗

NASA NTRS2018

The Magellan Evolution of Galaxies Spectroscopic and Ultraviolet Reference Atlas (MegaSaura). II. Stacked Spectra

We stack the rest-frame ultraviolet spectra of N = 14 highly magnified gravitationally lensed galaxies at redshifts 1.6 < z < 3.6. The resulting new composite spans 900 < lambda rest < 3000 Angstrom, with a peak signal-to-noise ratio of 103 per spectral resolution element (approximately 100 km s (exp -1)). It is the highest signalto-noise ratio, highest spectral resolution composite spectrum of z approximately 2-3 galaxies yet published. The composite reveals numerous weak nebular emission lines and stellar photospheric absorption lines that can serve as new physical diagnostics, particularly at high redshift with the James Webb Space Telescope (JWST). We report equivalent widths to aid in proposing for and interpreting JWST spectra. We examine the velocity profiles of strong absorption features in the composite, and in a matched composite of z approximately 0 COS/HST galaxy spectra. We find remarkable similarity in the velocity profiles at z approximately 0 and z approximately 2, suggesting that similar physical processes control the outflows across cosmic time. While the maximum outflow velocity depends strongly on ionization potential, the absorptionweighted mean velocity does not. As such, the bulk of the high-ionization absorption traces the low-ionization gas, with an additional blueshifted absorption tail extending to at least approximately 2000 km s (exp -1). We interpret this tail as arising from the stellar wind and photospheres of massive stars. Starburst99 models are able to replicate this high-velocity absorption tail. However, these theoretical models poorly reproduce several of the photospheric absorption features, indicating that improvements are needed to match observational constraints on the massive stellar content of star-forming galaxies at z approximately 2. We publicly release our composite spectra.

Rigby, J. R.↗

NASA NTRS2020

The NANOGrav 11 yr data set: Limits on Gravitational Wave Memory

The mergers of supermassive black hole binaries (SMBHBs) promise to be incredible sources of gravitational waves (GWs).While the oscillatory part of the merger gravitational waveform will be outside the frequency sensitivity range of pulsar timing arrays, the nonoscillatory GW memory effect is detectable. Further, any burst of GWs will produce GW memory, making memory a useful probe of unmodeled exotic sources and new physics. We searched the North American Nanohertz Observatory for Gravitational Waves (NANOGrav) 11 yr data set for GW memory. This data set is sensitive to very low-frequency GWs of ∼3 to 400 nHz (periods of ∼11 yr–1 month). Finding no evidence for GWs, we placed limits on the strain amplitude of GW memory events during the observation period. We then used the strain upper limits to place limits on the rate of GW memory causing events. At a strain of 2.5 × 10−14, corresponding to the median upper limit as a function of source sky position, we set a limit on the rate of GW memory events at <0.4 yr−1. That strain corresponds to an SMBHB merger with reduced mass of ηM ~2 × 1010Mmoonand inclination of ι = π/3 at a distance of 1 Gpc. As a test of our analysis, we analyzed the NANOGrav 9 yr data set as well. This analysis found an anomolous signal, which does not appear in the 11 yr data set. This signal is not a GW, and its origin remains unknown.

K Aggarwal↗

NASA NTRS2021

Tests of General Relativity with Binary Black Holes from the second LIGO–Virgo Gravitational-Wave Transient Catalog

Gravitational waves enable tests of general relativity in the highly dynamical and strong-field regime. Using events detected by LIGO-Virgo up to 1 October 2019, we evaluate the consistency of the data with predictions from the theory. We first establish that residuals from the best-fit waveform are consistent with detector noise, and that the low- and high-frequency parts of the signals are in agreement. We then consider parametrized modifications to the waveform by varying post-Newtonian and phenomenological coefficients, improving past constraints by factors of ∼2; we also find consistency with Kerr black holes when we specifically target signatures of the spin-induced quadrupole moment. Looking for gravitational-wave dispersion, we tighten constraints on Lorentz-violating coefficients by a factor of ∼2.6 and bound the mass of the graviton to m(g)≤1.76 x 10^(-23) eV/sq. c with 90% credibility. We also analyze the properties of the merger remnants by measuring ringdown frequencies and damping times, constraining fractional deviations away from the Kerr frequency to δ{\hat {f}}(220)=0.03(+0.38,-0.35) for the fundamental quadrupolar mode, and δ{\hat {f}}(221)=0.04(+0.27,-0.32) for the first overtone; additionally, we find no evidence for postmerger echoes. Finally, we determine that our data are consistent with tensorial polarizations through a template-independent method. When possible, we assess the validity of general relativity based on collections of events analyzed jointly. We find no evidence for new physics beyond general relativity, for black hole mimickers, or for any unaccounted systematics.

R. Abbott↗

NASA NTRSDate not supplied

ArcjetCV: a new machine learning application for extracting time-resolved recession measurements from arc jet test videos

Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession, sting arm motion, and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials.

machine learning↗

NASA NTRSDate not supplied

ArcjetCV: A New Machine Learning Application for Extracting Time-Resolved Recession Measurements From Arc Jet Test Videos

Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession, sting arm motion, and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials.

machine learning↗

NASA NTRS2022

Clumped-Isotope Constraint on Upper-Tropospheric Cooling During the Last Glacial Maximum

Ice cores and other paleotemperature proxies, together with general circulation models, have provided information on past surface temperatures and the atmosphere's composition in different climates. Little is known, however, about past temperatures at high altitudes, which play a crucial role in Earth's radiative energy budget. Paleoclimate records at high-altitude sites are sparse, and the few that are available show poor agreement with climate model predictions. These disagreements could be due to insufficient spatial coverage, spatiotemporal biases, or model physics; new records that can mitigate or avoid these uncertainties are needed. Here, we constrain the change in upper-tropospheric temperature at the global scale during the Last Glacial Maximum (LGM) using the clumped-isotope composition of molecular oxygen trapped in polar ice cores. Aided by global three-dimensional chemical transport modeling, we exploit the intrinsic temperature sensitivity of the clumped-isotope composition of atmospheric oxygen to infer that the upper troposphere (effective mean altitude 10–11 km) was 6–9°C cooler during the LGM than during the late preindustrial Holocene. A complementary energy balance approach supports a minor or negligible steepening of atmospheric lapse rates during the LGM, which is consistent with a range of climate model simulations. Proxy-model disagreements with other high-altitude records may stem from inaccuracies in regional hydroclimate simulation, possibly related to land-atmosphere feedbacks.

clumped isotope↗

NASA NTRSDate not supplied

ArcjetCV: A New Machine Learning Application for Extracting Time-Resolved Recession Measurements From Arc Jet Test Videos

Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

machine learning↗

NASA NTRSDate not supplied

arcjetCV: A New Machine Learning Application for Extracting Time-Resolved Recession Measurements From Arc Jet Test Videos

Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

machine learning↗

NASA NTRS2023

Understanding the Deflection of the “Cartwheel CME”: Data Analysis and Modeling

We study the low corona evolution of the “Cartwheel” coronal mass ejection (CME; 2008 April 9) by- reconstructing its three-dimensional path and modeling it with magnetohydrodynamic simulations. This event exhibited a double deflection that has been reported and analyzed in previous works but whose underlying cause remained unclear. The Cartwheel CME traveled toward a coronal hole (CH) and against the magnetic gradients. Using a high-cadence, full-trajectory reconstruction, we accurately determine the location of the magnetic flux rope (MFR) and, consequently, the magnetic environment in which it is immersed. We find a pseudostreamer (PS) structure whose null point may be responsible for the complex evolution of the MFR at the initial phase. From the preeruptive magnetic field reconstruction, we estimate the dynamic forces acting on the MFR and provide a new physical insight into the motion exhibited by the 2008 April 9 event. By setting up a similar magnetic configuration in a 2.5D numerical simulation we are able to reproduce the observed behavior, confirming the importance of the PS null point. We find that the magnetic forces directed toward the null point cause the first deflection, directing the MFR toward the CH. Later, the magnetic pressure gradient of the CH produces the reversal motion of the MFR.

Solar prominences↗

NASA NTRSDate not supplied

arcjetCV: automating recession extraction from video

Arc jet Computer Vision (arcjetCV)[1][2] is a software application built to automate time-resolved recession tracking of edges in test videos, specifically for tracking material recession and the shock-material standoff. This provides a new capability to resolve and validate new physics associated with non-linear processes and an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcticCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

Recession tracking↗

NASA NTRSDate not supplied

ArcjetCV: Automating Recession Tracking

Arc jet Computer Vision (arcjetCV) is a software application built to automate time-resolved recession tracking of edges in test videos, specifically for tracking material recession and the shock-material standoff. This provides a new capability to resolve and validate new physics associated with non-linear processes and an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcticCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

Recession tracking↗

NASA NTRSDate not supplied

ArcjetCV: Automating Arc Jet Analysis

Arc jet Computer Vision (arcjetCV) is a software application built to automate time-resolved recession tracking of edges in test videos, specifically for tracking material recession and the shock-material standoff. This provides a new capability to resolve and validate new physics associated with non-linear processes and an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcticCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

Recession tracking↗

DOE OSTI2011

Setting the Renormalization Scale in QCD: The Principle of Maximum Conformality

A key problem in making precise perturbative QCD predictions is the uncertainty in determining the renormalization scale {mu} of the running coupling {alpha}{sub s}({mu}{sup 2}): The purpose of the running coupling in any gauge theory is to sum all terms involving the {beta} function; in fact, when the renormalization scale is set properly, all non-conformal {beta} {ne} 0 terms in a perturbative expansion arising from renormalization are summed into the running coupling. The remaining terms in the perturbative series are then identical to that of a conformal theory; i.e., the corresponding theory with {beta} = 0. The resulting scale-fixed predictions using the 'principle of maximum conformality' (PMC) are independent of the choice of renormalization scheme - a key requirement of renormalization group invariance. The results avoid renormalon resummation and agree with QED scale-setting in the Abelian limit. The PMC is also the theoretical principle underlying the BLM procedure, commensurate scale relations between observables, and the scale-setting method used in lattice gauge theory. The number of active flavors nf in the QCD {beta} function is also correctly determined. We discuss several methods for determining the PMC/BLM scale for QCD processes. We show that a single global PMC scale, valid at leading order, can be derived from basic properties of the perturbative QCD cross section. The elimination of the renormalization scheme ambiguity using the PMC will not only increase the precision of QCD tests, but it will also increase the sensitivity of collider experiments to new physics beyond the Standard Model.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2011

Multi-jet Merging with NLO Matrix Elements

In the algorithm presented here, the ME+PS approach to merge samples of tree-level matrix elements into inclusive event samples is combined with the POWHEG method, which includes exact next-to-leading order matrix elements in the parton shower. The advantages of the method are discussed and the quality of its implementation in SHERPA is exemplified by results for e{sup +}e{sup -} annihilation into hadrons at LEP, for deep-inelastic lepton-nucleon scattering at HERA, for Drell-Yan lepton-pair production at the Tevatron and for W{sup +}W{sup -}-production at LHC energies. The simulation of hard QCD radiation in parton-shower Monte Carlos has seen tremendous progress over the last years. It was largely stimulated by the need for more precise predictions at LHC energies where the large available phase space allows additional hard QCD radiation alongside known Standard Model processes or even signals from new physics. Two types of algorithms have been developed, which allow to improve upon the soft-collinear approximations made in the parton shower, such that hard radiation is simulated according to exact matrix elements. In the ME+PS approach [1] higher-order tree-level matrix elements for different final-state jet multiplicity are merged with each other and with subsequent parton shower emissions to generate an inclusive sample. Such a prescription is invaluable for analyses which are sensitive to final states with a large jet multiplicity. The only remaining deficiency of such tree-level calculations is the large uncertainty stemming from scale variations. The POWHEG method [2] solves this problem for the lowest multiplicity subprocess by combining full NLO matrix elements with the parton shower. While this leads to NLO accuracy in the inclusive cross section and the exact radiation pattern for the first emission, it fails to describe higher-order emissions with improved accuracy. Thus it is not sufficient if final states with high jet multiplicities are considered. With the complementary advantages of these two approaches, the question arises naturally whether it would be possible to combine them into an even more powerful one. Such a combined algorithm was independently developed in [5] and [6]. Here a summary of the algorithm is given and predictions from corresponding Monte-Carlo predictions are presented.

43 PARTICLE ACCELERATORS↗

DOE OSTI2011

Leptonic B Decays at BaBar

The authors will present the most recent results on leptonic B decays B{sup {+-}(0)} {yields} K*{sup {+-}(0)} {nu}{bar {nu}} and B{sup {+-}} {yields} {mu}{sup {+-}}{nu}, based on the data collected by the BaBar detector at PEP-II, an asymmetric e{sup +}e{sup -} collider at the center of mass energy of the {Upsilon}(4S) resonance. Rare B decays have always been a standard probe for New Physics (NP) searches. The very low Standard Model (SM) rate of these decays often make them unaccessible with the present experimental datasets, unless NP effects enhance the rate up to the current experimental sensitivity. Moreover, as NP effects can modify the decay kinematic, particular attention must be payed in order to perform a model independent analysis. A B-Factory provides an unique environment where to investigate these processes. The high number of B{bar B} pairs produced by a B-Factory often allows to approach the needed experimental sensitivity. Moreover, the clean environment and the closed kinematic of the initial state enable to obtaining a very pure sample where to look for these decays.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2011

Indirect Charged Higgs Constraints from BaBar

The high-statistics data samples from the BABAR and Belle B-Factory experiments provide stringent constraints on charged Higgs bosons within the context of specific New Physics models. These constraints are obtained by comparing Standard Model predictions with experimental observations in rare B decays with potential sensitivity to contributions mediated by a virtual H{sup {+-}} in tree or loop diagrams. Recent experimental results on the decays B{sup +} {yields} {mu}{sup +}{nu}, B{sup +} {yields} {tau}{sup +}{nu} and inclusive B {yields} X{sub s}{gamma} are described and the implications of these measurements for charged Higgs bosons is discussed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗