246 research outputs found
Magnetic phase evolution in the spinel compounds ZnCoCrO
We present the magnetic properties of complete solid solutions of
ZnCrO and CoCrO: two well-studied oxide spinels with very
different magnetic ground states. ZnCrO, with non-magnetic
cations occupying the A site and magnetic cations on the B site, is a
highly frustrated antiferromagnet. CoCrO, with magnetic cations
(three unpaired electrons) on the A site as well, exhibits both N\'eel
ferrimagnetism as well as commensurate and incommensurate non-collinear
magnetic order. More recently, CoCrO has been studied extensively for
its polar behavior which arises from conical magnetic ordering. Gradually
introducing magnetism on the A site of ZnCrO results in a transition
from frustrated antiferromagnetism to glassy magnetism at low concentrations of
Co, and eventually to ferrimagnetic and conical ground states at higher
concentrations. Real-space Monte-Carlo simulations of the magnetic
susceptibility suggest that the first magnetic ordering transition and features
of the susceptibility across are captured by near-neighbor self- and
cross-couplings between the magnetic A and B atoms. We present as a part of
this study, a method for displaying the temperature dependence of magnetic
susceptibility in a manner which helps distinguish between compounds possessing
purely antiferromagnetic interactions from compounds where other kinds of
ordering are present.Comment: 10 pages, 5 figures, 1 tabl
Fluctuating nonlinear spring theory:Strength, deformability, and toughness of biological nanoparticles from theoretical reconstruction of force-deformation spectra
We developed the Fluctuating Nonlinear Spring (FNS) model to describe the dynamics of mechanical deformation of biological particles, such as virus capsids. The theory interprets the force-deformation spectra in terms of the “Hertzian stiffness” (non-linear regime of a particle's small-amplitude deformations), elastic constant (large-amplitude elastic deformations), and force range in which the particle's fracture occurs. The FNS theory enables one to quantify the particles’ elasticity (Young's moduli for Hertzian and bending deformations), and the limits of their strength (critical forces, fracture toughness) and deformability (critical deformations) as well as the probability distributions of these properties, and to calculate the free energy changes for the particle's Hertzian, elastic, and plastic deformations, and eventual fracture. We applied the FNS theory to describe the protein capsids of bacteriophage P22, Human Adenovirus, and Herpes Simplex virus characterized by deformations before fracture that did not exceed 10–19% of their size. These nanoshells are soft (~1–10-GPa elastic modulus), with low ~50–480-kPa toughness – a regime of material behavior that is not well understood, and with the strength increasing while toughness decreases with their size. The particles’ fracture is stochastic, with the average values of critical forces, critical deformations, and fracture toughness comparable with their standard deviations. The FNS theory predicts 0.7-MJ/mol free energy for P22 capsid maturation, and it could be extended to describe uniaxial deformation of cylindrical microtubules and ellipsoidal cellular organelles
Synthesis and characterization of Ca2CoTaO6, a new monoclinically distorted double perovskite
The new Ca2CoTaO6 double perovskite has been synthesized by a conventional solid state reaction and its unit cell parameters determined by X-ray powder diffractometry. It crystallizes in the monoclinic space group P2(1)/n. The unit cell parameters are: a = 5.507(2) Å; b = 5.564(3) Å; c = 7.798(3) Å; β = 89.99(4)° and Z = 2. The IR spectrum of the material was recorded and is briefly discussed. Some comparisons with Ca2CoNbO6 and other isostructural perovskites are also performed
Virgo Detector Characterization and Data Quality during the O3 run
The Advanced Virgo detector has contributed with its data to the rapid growth
of the number of detected gravitational-wave signals in the past few years,
alongside the two LIGO instruments. First, during the last month of the
Observation Run 2 (O2) in August 2017 (with, most notably, the compact binary
mergers GW170814 and GW170817) and then during the full Observation Run 3 (O3):
an 11 months data taking period, between April 2019 and March 2020, that led to
the addition of about 80 events to the catalog of transient gravitational-wave
sources maintained by LIGO, Virgo and KAGRA. These discoveries and the manifold
exploitation of the detected waveforms require an accurate characterization of
the quality of the data, such as continuous study and monitoring of the
detector noise. These activities, collectively named {\em detector
characterization} or {\em DetChar}, span the whole workflow of the Virgo data,
from the instrument front-end to the final analysis. They are described in
details in the following article, with a focus on the associated tools, the
results achieved by the Virgo DetChar group during the O3 run and the main
prospects for future data-taking periods with an improved detector.Comment: 86 pages, 33 figures. This paper has been divided into two articles
which supercede it and have been posted to arXiv on October 2022. Please use
these new preprints as references: arXiv:2210.15634 (tools and methods) and
arXiv:2210.15633 (results from the O3 run
Virgo Detector Characterization and Data Quality: results from the O3 run
The Advanced Virgo detector has contributed with its data to the rapid growth
of the number of detected gravitational-wave (GW) signals in the past few
years, alongside the two Advanced LIGO instruments. First during the last month
of the Observation Run 2 (O2) in August 2017 (with, most notably, the compact
binary mergers GW170814 and GW170817), and then during the full Observation Run
3 (O3): an 11-months data taking period, between April 2019 and March 2020,
that led to the addition of about 80 events to the catalog of transient GW
sources maintained by LIGO, Virgo and now KAGRA. These discoveries and the
manifold exploitation of the detected waveforms require an accurate
characterization of the quality of the data, such as continuous study and
monitoring of the detector noise sources. These activities, collectively named
{\em detector characterization and data quality} or {\em DetChar}, span the
whole workflow of the Virgo data, from the instrument front-end hardware to the
final analyses. They are described in details in the following article, with a
focus on the results achieved by the Virgo DetChar group during the O3 run.
Concurrently, a companion article describes the tools that have been used by
the Virgo DetChar group to perform this work.Comment: 57 pages, 18 figures. To be submitted to Class. and Quantum Grav.
This is the "Results" part of preprint arXiv:2205.01555 [gr-qc] which has
been split into two companion articles: one about the tools and methods, the
other about the analyses of the O3 Virgo dat
Virgo Detector Characterization and Data Quality: tools
Detector characterization and data quality studies -- collectively referred
to as {\em DetChar} activities in this article -- are paramount to the
scientific exploitation of the joint dataset collected by the LIGO-Virgo-KAGRA
global network of ground-based gravitational-wave (GW) detectors. They take
place during each phase of the operation of the instruments (upgrade, tuning
and optimization, data taking), are required at all steps of the dataflow (from
data acquisition to the final list of GW events) and operate at various
latencies (from near real-time to vet the public alerts to offline analyses).
This work requires a wide set of tools which have been developed over the years
to fulfill the requirements of the various DetChar studies: data access and
bookkeeping; global monitoring of the instruments and of the different steps of
the data processing; studies of the global properties of the noise at the
detector outputs; identification and follow-up of noise peculiar features
(whether they be transient or continuously present in the data); quick
processing of the public alerts. The present article reviews all the tools used
by the Virgo DetChar group during the third LIGO-Virgo Observation Run (O3,
from April 2019 to March 2020), mainly to analyse the Virgo data acquired at
EGO. Concurrently, a companion article focuses on the results achieved by the
DetChar group during the O3 run using these tools.Comment: 44 pages, 16 figures. To be submitted to Class. and Quantum Grav.
This is the "Tools" part of preprint arXiv:2205.01555 [gr-qc] which has been
split into two companion articles: one about the tools and methods, the other
about the analyses of the O3 Virgo dat
Common variants in Alzheimer’s disease and risk stratification by polygenic risk scores
Funder: Funder: Fundación bancaria ‘La Caixa’ Number: LCF/PR/PR16/51110003 Funder: Grifols SA Number: LCF/PR/PR16/51110003 Funder: European Union/EFPIA Innovative Medicines Initiative Joint Number: 115975 Funder: JPco-fuND FP-829-029 Number: 733051061Genetic discoveries of Alzheimer's disease are the drivers of our understanding, and together with polygenetic risk stratification can contribute towards planning of feasible and efficient preventive and curative clinical trials. We first perform a large genetic association study by merging all available case-control datasets and by-proxy study results (discovery n = 409,435 and validation size n = 58,190). Here, we add six variants associated with Alzheimer's disease risk (near APP, CHRNE, PRKD3/NDUFAF7, PLCG2 and two exonic variants in the SHARPIN gene). Assessment of the polygenic risk score and stratifying by APOE reveal a 4 to 5.5 years difference in median age at onset of Alzheimer's disease patients in APOE ɛ4 carriers. Because of this study, the underlying mechanisms of APP can be studied to refine the amyloid cascade and the polygenic risk score provides a tool to select individuals at high risk of Alzheimer's disease
Constraints on dark photon dark matter using data from LIGO's and Virgo's third observing run
We present a search for dark photon dark matter that could couple to
gravitational-wave interferometers using data from Advanced LIGO and Virgo's
third observing run. To perform this analysis, we use two methods, one based on
cross-correlation of the strain channels in the two nearly aligned LIGO
detectors, and one that looks for excess power in the strain channels of the
LIGO and Virgo detectors. The excess power method optimizes the Fourier
Transform coherence time as a function of frequency, to account for the
expected signal width due to Doppler modulations. We do not find any evidence
of dark photon dark matter with a mass between eV/, which corresponds to frequencies between 10-2000
Hz, and therefore provide upper limits on the square of the minimum coupling of
dark photons to baryons, i.e. dark matter. For the
cross-correlation method, the best median constraint on the squared coupling is
at eV/; for the
other analysis, the best constraint is at eV/. These limits improve upon those obtained
in direct dark matter detection experiments by a factor of for
eV/, and are, in absolute terms, the
most stringent constraint so far in a large mass range eV/.Comment: 20 pages, 7 figure
Search of the early O3 LIGO data for continuous gravitational waves from the Cassiopeia A and Vela Jr. supernova remnants
partially_open1412sìWe present directed searches for continuous gravitational waves from the neutron stars in the Cassiopeia A (Cas A) and Vela Jr. supernova remnants. We carry out the searches in the LIGO detector data from the first six months of the third Advanced LIGO and Virgo observing run using the weave semicoherent method, which sums matched-filter detection-statistic values over many time segments spanning the observation period. No gravitational wave signal is detected in the search band of 20–976 Hz for assumed source ages greater than 300 years for Cas A and greater than 700 years for Vela Jr. Estimates from simulated continuous wave signals indicate we achieve the most sensitive results to date across the explored parameter space volume, probing to strain magnitudes as low as
∼6.3×10^−26 for Cas A and ∼5.6×10^−26 for Vela Jr. at frequencies near 166 Hz at 95% efficiency.openAbbott, R.; Abbott, T. D.; Acernese, F.; Ackley, K.; Adams, C.; Adhikari, N.; Adhikari, R. X.; Adya, V. B.; Affeldt, C.; Agarwal, D.; Agathos, M.; Agatsuma, K.; Aggarwal, N.; Aguiar, O. D.; Aiello, L.; Ain, A.; Ajith, P.; Albanesi, S.; Allocca, A.; Altin, P. A.; Amato, A.; Anand, C.; Anand, S.; Ananyeva, A.; Anderson, S. B.; Anderson, W. G.; Andrade, T.; Andres, N.; Andrić, T.; Angelova, S. V.; Ansoldi, S.; Antelis, J. M.; Antier, S.; Appert, S.; Arai, K.; Araya, M. C.; Areeda, J. S.; Arène, M.; Arnaud, N.; Aronson, S. M.; Arun, K. G.; Asali, Y.; Ashton, G.; Assiduo, M.; Aston, S. M.; Astone, P.; Aubin, F.; Austin, C.; Babak, S.; Badaracco, F.; Bader, M. K. M.; Badger, C.; Bae, S.; Baer, A. M.; Bagnasco, S.; Bai, Y.; Baird, J.; Ball, M.; Ballardin, G.; Ballmer, S. W.; Balsamo, A.; Baltus, G.; Banagiri, S.; Bankar, D.; Barayoga, J. C.; Barbieri, C.; Barish, B. 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T.; Cousins, B.; Couvares, P.; Coward, D. M.; Cowart, M. J.; Coyne, D. C.; Coyne, R.; Creighton, J. D. E.; Creighton, T. D.; Criswell, A. W.; Croquette, M.; Crowder, S. G.; Cudell, J. R.; Cullen, T. J.; Cumming, A.; Cummings, R.; Cunningham, L.; Cuoco, E.; Curyło, M.; Dabadie, P.; Canton, T. Dal; Dall’Osso, S.; Dálya, G.; Dana, A.; DaneshgaranBajastani, L. M.; D’Angelo, B.; Danilishin, S.; D’Antonio, S.; Danzmann, K.; Darsow-Fromm, C.; Dasgupta, A.; Datrier, L. E. H.; Datta, S.; Dattilo, V.; Dave, I.; Davier, M.; Davies, G. S.; Davis, D.; Davis, M. C.; Daw, E. J.; Dean, R.; DeBra, D.; Deenadayalan, M.; Degallaix, J.; De Laurentis, M.; Deléglise, S.; Del Favero, V.; De Lillo, F.; De Lillo, N.; Del Pozzo, W.; DeMarchi, L. M.; De Matteis, F.; D’Emilio, V.; Demos, N.; Dent, T.; Depasse, A.; De Pietri, R.; De Rosa, R.; De Rossi, C.; DeSalvo, R.; De Simone, R.; Dhurandhar, S.; Díaz, M. C.; Diaz-Ortiz, M.; Didio, N. A.; Dietrich, T.; Di Fiore, L.; Di Fronzo, C.; Di Giorgio, C.; Di Giovanni, F.; Di Giovanni, M.; Di Girolamo, T.; Di Lieto, A.; Ding, B.; Di Pace, S.; Di Palma, I.; Di Renzo, F.; Divakarla, A. K.; Dmitriev, A.; Doctor, Z.; D’Onofrio, L.; Donovan, F.; Dooley, K. L.; Doravari, S.; Dorrington, I.; Drago, M.; Driggers, J. C.; Drori, Y.; Ducoin, J.-G.; Dupej, P.; Durante, O.; D’Urso, D.; Duverne, P.-A.; Dwyer, S. E.; Eassa, C.; Easter, P. J.; Ebersold, M.; Eckhardt, T.; Eddolls, G.; Edelman, B.; Edo, T. B.; Edy, O.; Effler, A.; Eichholz, J.; Eikenberry, S. S.; Eisenmann, M.; Eisenstein, R. A.; Ejlli, A.; Engelby, E.; Errico, L.; Essick, R. C.; Estellés, H.; Estevez, D.; Etienne, Z.; Etzel, T.; Evans, M.; Evans, T. M.; Ewing, B. E.; Fafone, V.; Fair, H.; Fairhurst, S.; Farah, A. M.; Farinon, S.; Farr, B.; Farr, W. M.; Farrow, N. W.; Fauchon-Jones, E. J.; Favaro, G.; Favata, M.; Fays, M.; Fazio, M.; Feicht, J.; Fejer, M. M.; Fenyvesi, E.; Ferguson, D. L.; Fernandez-Galiana, A.; Ferrante, I.; Ferreira, T. A.; Fidecaro, F.; Figura, P.; Fiori, I.; Fishbach, M.; Fisher, R. P.; Fittipaldi, R.; Fiumara, V.; Flaminio, R.; Floden, E.; Fong, H.; Font, J. A.; Fornal, B.; Forsyth, P. W. F.; Franke, A.; Frasca, S.; Frasconi, F.; Frederick, C.; Freed, J. P.; Frei, Z.; Freise, A.; Frey, R.; Fritschel, P.; Frolov, V. V.; Fronzé, G. G.; Fulda, P.; Fyffe, M.; Gabbard, H. A.; Gadre, B. U.; Gair, J. R.; Gais, J.; Galaudage, S.; Gamba, R.; Ganapathy, D.; Ganguly, A.; Gaonkar, S. G.; Garaventa, B.; García-Núñez, C.; García-Quirós, C.; Garufi, F.; Gateley, B.; Gaudio, S.; Gayathri, V.; Gemme, G.; Gennai, A.; George, J.; Gerberding, O.; Gergely, L.; Gewecke, P.; Ghonge, S.; Ghosh, Abhirup; Ghosh, Archisman; Ghosh, Shaon; Ghosh, Shrobana; Giacomazzo, B.; Giacoppo, L.; Giaime, J. A.; Giardina, K. D.; Gibson, D. R.; Gier, C.; Giesler, M.; Giri, P.; Gissi, F.; Glanzer, J.; Gleckl, A. E.; Godwin, P.; Goetz, E.; Goetz, R.; Gohlke, N.; Goncharov, B.; González, G.; Gopakumar, A.; Gosselin, M.; Gouaty, R.; Gould, D. W.; Grace, B.; Grado, A.; Granata, M.; Granata, V.; Grant, A.; Gras, S.; Grassia, P.; Gray, C.; Gray, R.; Greco, G.; Green, A. C.; Green, R.; Gretarsson, A. M.; Gretarsson, E. M.; Griffith, D.; Griffiths, W.; Griggs, H. L.; Grignani, G.; Grimaldi, A.; Grimm, S. J.; Grote, H.; Grunewald, S.; Gruning, P.; Guerra, D.; Guidi, Gianluca; Guimaraes, A. R.; Guixé, G.; Gulati, H. K.; Guo, H.-K.; Guo, Y.; Gupta, Anchal; Gupta, Anuradha; Gupta, P.; Gustafson, E. K.; Gustafson, R.; Guzman, F.; Haegel, L.; Halim, O.; Hall, E. D.; Hamilton, E. Z.; Hammond, G.; Haney, M.; Hanks, J.; Hanna, C.; Hannam, M. D.; Hannuksela, O.; Hansen, H.; Hansen, T. J.; Hanson, J.; Harder, T.; Hardwick, T.; Haris, K.; Harms, J.; Harry, G. M.; Harry, I. W.; Hartwig, D.; Haskell, B.; Hasskew, R. K.; Haster, C.-J.; Haughian, K.; Hayes, F. J.; Healy, J.; Heidmann, A.; Heidt, A.; Heintze, M. C.; Heinze, J.; Heinzel, J.; Heitmann, H.; Hellman, F.; Hello, P.; Helmling-Cornell, A. F.; Hemming, G.; Hendry, M.; Heng, I. S.; Hennes, E.; Hennig, J.; Hennig, M. H.; Hernandez, A. G.; Vivanco, F. Hernandez; Heurs, M.; Hild, S.; Hill, P.; Hines, A. S.; Hochheim, S.; Hofman, D.; Hohmann, J. N.; Holcomb, D. G.; Holland, N. A.; Hollows, I. J.; Holmes, Z. J.; Holt, K.; Holz, D. E.; Hopkins, P.; Hough, J.; Hourihane, S.; Howell, E. J.; Hoy, C. G.; Hoyland, D.; Hreibi, A.; Hsu, Y.; Huang, Y.; Hübner, M. T.; Huddart, A. D.; Hughey, B.; Hui, V.; Husa, S.; Huttner, S. H.; Huxford, R.; Huynh-Dinh, T.; Idzkowski, B.; Iess, A.; Ingram, C.; Isi, M.; Isleif, K.; Iyer, B. R.; JaberianHamedan, V.; Jacqmin, T.; Jadhav, S. J.; Jadhav, S. P.; James, A. L.; Jan, A. Z.; Jani, K.; Janquart, J.; Janssens, K.; Janthalur, N. N.; Jaranowski, P.; Jariwala, D.; Jaume, R.; Jenkins, A. C.; Jenner, K.; Jeunon, M.; Jia, W.; Johns, G. R.; Jones, A. W.; Jones, D. I.; Jones, J. D.; Jones, P.; Jones, R.; Jonker, R. J. G.; Ju, L.; Junker, J.; Juste, V.; Kalaghatgi, C. V.; Kalogera, V.; Kamai, B.; Kandhasamy, S.; Kang, G.; Kanner, J. B.; Kao, Y.; Kapadia, S. J.; Kapasi, D. P.; Karat, S.; Karathanasis, C.; Karki, S.; Kashyap, R.; Kasprzack, M.; Kastaun, W.; Katsanevas, S.; Katsavounidis, E.; Katzman, W.; Kaur, T.; Kawabe, K.; Kéfélian, F.; Keitel, D.; Key, J. S.; Khadka, S.; Khalili, F. Y.; Khan, S.; Khazanov, E. A.; Khetan, N.; Khursheed, M.; Kijbunchoo, N.; Kim, C.; Kim, J. C.; Kim, K.; Kim, W. S.; Kim, Y.-M.; Kimball, C.; Kinley-Hanlon, M.; Kirchhoff, R.; Kissel, J. S.; Kleybolte, L.; Klimenko, S.; Knee, A. M.; Knowles, T. D.; Knyazev, E.; Koch, P.; Koekoek, G.; Koley, S.; Kolitsidou, P.; Kolstein, M.; Komori, K.; Kondrashov, V.; Kontos, A.; Koper, N.; Korobko, M.; Kovalam, M.; Kozak, D. B.; Kringel, V.; Krishnendu, N. V.; Królak, A.; Kuehn, G.; Kuei, F.; Kuijer, P.; Kumar, A.; Kumar, P.; Kumar, Rahul; Kumar, Rakesh; Kuns, K.; Kuwahara, S.; Lagabbe, P.; Laghi, D.; Lalande, E.; Lam, T. 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The population of merging compact binaries inferred using gravitational waves through GWTC-3
We report on the population properties of 76 compact binary mergers detected with gravitational waves below a false alarm rate of 1 per year through GWTC-3. The catalog contains three classes of binary mergers: BBH, BNS, and NSBH mergers. We infer the BNS merger rate to be between 10 and 1700 and the NSBH merger rate to be between 7.8 and 140 , assuming a constant rate density versus comoving volume and taking the union of 90% credible intervals for methods used in this work. Accounting for the BBH merger rate to evolve with redshift, we find the BBH merger rate to be between 17.9 and 44 at a fiducial redshift (z=0.2). We obtain a broad neutron star mass distribution extending from to . We can confidently identify a rapid decrease in merger rate versus component mass between neutron star-like masses and black-hole-like masses, but there is no evidence that the merger rate increases again before 10 . We also find the BBH mass distribution has localized over- and under-densities relative to a power law distribution. While we continue to find the mass distribution of a binary's more massive component strongly decreases as a function of primary mass, we observe no evidence of a strongly suppressed merger rate above . The rate of BBH mergers is observed to increase with redshift at a rate proportional to with for . Observed black hole spins are small, with half of spin magnitudes below . We observe evidence of negative aligned spins in the population, and an increase in spin magnitude for systems with more unequal mass ratio
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