| |
Records |
Links |
|
Author |
Servant, G.; Simakachorn, P. |
|
| |
Title |
Constraining postinflationary axions with pulsar timing arrays |
Type |
Journal Article |
| |
Year |
2023 |
Publication |
Physical Review D |
Abbreviated Journal |
Phys. Rev. D |
|
| |
Volume |
108 |
Issue |
12 |
Pages |
123516 - 16pp |
|
| |
Keywords |
|
|
| |
Abstract |
Models that produce axionlike particles (ALPs) after cosmological inflation due to spontaneous U(1) symmetry breaking also produce cosmic-string networks. Those axionic strings lose energy through gravitational-wave emission during the whole cosmological history, generating a stochastic background of gravitational waves that spans many decades in frequency. We can therefore constrain the axion decay constant and axion mass from limits on the gravitational-wave spectrum and compatibility with dark matter abundance as well as dark radiation. We derive such limits from analyzing the most recent NANOGrav data from pulsar timing arrays (PTAs). The limits are similar to the Neff bounds on dark radiation for ALP masses ma less than or similar to 10-22 eV. On the other hand, for heavy ALPs with ma greater than or similar to 0.1 GeV and NDW not equal 1, new regions of parameter space can be probed by PTA data due to the dominant domain-wall contribution to the gravitational-wave background. |
|
| |
Address  |
[Servant, Geraldine] DESY, Notkestr 85, D-22607 Hamburg, Germany, Email: geraldine.servant@desy.de; |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
Amer Physical Soc |
Place of Publication |
|
Editor |
|
|
| |
Language |
English |
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
2470-0010 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
WOS:001155748800012 |
Approved |
no |
|
| |
Is ISI |
yes |
International Collaboration |
yes |
|
| |
Call Number |
IFIC @ pastor @ |
Serial |
5933 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Segarra, A.; Bernabeu, J. |
|
| |
Title |
Absolute neutrino mass and the Dirac/Majorana distinction from the weak interaction of aggregate matter |
Type |
Journal Article |
| |
Year |
2020 |
Publication |
Physical Review D |
Abbreviated Journal |
Phys. Rev. D |
|
| |
Volume |
101 |
Issue |
9 |
Pages |
093004 - 6pp |
|
| |
Keywords |
|
|
| |
Abstract |
The 2 nu-mediated force has a range of microns, well beyond the atomic scale. The effective potential is built from the t-channel absorptive part of the scattering amplitude and depends on neutrino properties on shell. We demonstrate that neutral aggregate matter has a weak charge and calculate the matrix of six coherent charges for its interaction with definite-mass neutrinos. Near the range of the potential the neutrino pair is nonrelativistic, leading to observable absolute mass and Dirac/Majorana distinction via different r-dependence and violation of the weak equivalence principle. |
|
| |
Address |
[Segarra, Alejandro] Univ Valencia, Dept Theoret Phys, E-46100 Valencia, Spain |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
Amer Physical Soc |
Place of Publication |
|
Editor |
|
|
| |
Language |
English |
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
2470-0010 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
WOS:000532654200001 |
Approved |
no |
|
| |
Is ISI |
yes |
International Collaboration |
no |
|
| |
Call Number |
IFIC @ pastor @ |
Serial |
4397 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Schreeck, H.; Paschen, B.; Wieduwilt, P.; Ahlburg, P.; Andricek, L.; Dingfelder, J.; Frey, A.; Lutticke, F.; Marinas, C.; Richter, R.; Schwenker, B. |
|
| |
Title |
Effects of gamma irradiation on DEPFET pixel sensors for the Belle II experiment |
Type |
Journal Article |
| |
Year |
2020 |
Publication |
Nuclear Instruments & Methods in Physics Research A |
Abbreviated Journal |
Nucl. Instrum. Methods Phys. Res. A |
|
| |
Volume |
959 |
Issue |
|
Pages |
163522 - 9pp |
|
| |
Keywords |
DEPFET; Radiation damage; Particle tracking detectors; Belle II |
|
| |
Abstract |
For the Belle II experiment at KEK (Tsukuba, Japan) the KEKB accelerator was upgraded to deliver a 40 times larger instantaneous luminosity than before, which requires an increased radiation hardness of the detector components. As the innermost part of the Belle II detector, the pixel detector (PXD), based on DEPFET (DEpleted P-channel Field Effect Transistor) technology, is most exposed to radiation from the accelerator. An irradiation campaign was performed to verify that the PXD can cope with the expected amount of radiation. We present the results of this measurement campaign in which an X-ray machine was used to irradiate a single PXD half-ladder to a total dose of 266 kGy. The half-ladder is from the same batch as the half-ladders used for Belle II. According to simulations, the total accumulated dose corresponds to 7-10 years of Belle II operation. While individual components have been irradiated before, this campaign is the first full system irradiation. We discuss the effects on the DEPFET sensors, as well as the performance of the front-end electronics. In addition, we present efficiency studies of the half-ladder from beam tests performed before and after the irradiation. |
|
| |
Address |
[Schreeck, Harrison; Wieduwilt, Philipp; Frey, Ariane; Schwenker, Benjamin] Georg August Univ Gottingen, Phys Inst 2, Friedrich Hund Pl 1, D-37077 Gottingen, Germany, Email: harrison.schreeck@phys.uni-goettingen.de |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
Elsevier |
Place of Publication |
|
Editor |
|
|
| |
Language |
English |
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
0168-9002 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
WOS:000518368800016 |
Approved |
no |
|
| |
Is ISI |
yes |
International Collaboration |
yes |
|
| |
Call Number |
IFIC @ pastor @ |
Serial |
4316 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Schiavone, T.; Montani, G.; Bombacigno, F. |
|
| |
Title |
f(R) gravity in the Jordan frame as a paradigm for the Hubble tension |
Type |
Journal Article |
| |
Year |
2023 |
Publication |
Monthly Notices of the Royal Astronomical Society |
Abbreviated Journal |
Mon. Not. Roy. Astron. Soc. |
|
| |
Volume |
522 |
Issue |
1 |
Pages |
L72-L77 |
|
| |
Keywords |
supernovae: general; galaxies: distances and redshifts; cosmological parameters; dark energy; cosmology: theory |
|
| |
Abstract |
We analyse the f(R) gravity in the so-called Jordan frame, as implemented to the isotropic Universe dynamics. The goal of the present study is to show that according to recent data analyses of the supernovae Ia Pantheon sample, it is possible to account for an effective redshift dependence of the Hubble constant. This is achieved via the dynamics of a non-minimally coupled scalar field, as it emerges in the f(R) gravity. We face the question both from an analytical and purely numerical point of view, following the same technical paradigm. We arrive to establish that the expected decay of the Hubble constant with the redshift z is ensured by a form of the scalar field potential, which remains essentially constant for z less than or similar to 0.3, independently if this request is made a priori, as in the analytical approach, or obtained a posteriori, when the numerical procedure is addressed. Thus, we demonstrate that an f(R) dark energy model is able to account for an apparent variation of the Hubble constant due to the rescaling of the Einstein constant by the f(R) scalar mode. |
|
| |
Address |
[Schiavone, Tiziano] Univ Pisa, Dept Phys Fermi, Largo Bruno Pontecorvo 3, I-56127 Pisa, Italy, Email: tschiavone@fc.ul.pt |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
Oxford Univ Press |
Place of Publication |
|
Editor |
|
|
| |
Language |
English |
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
0035-8711 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
WOS:001066034100015 |
Approved |
no |
|
| |
Is ISI |
yes |
International Collaboration |
yes |
|
| |
Call Number |
IFIC @ pastor @ |
Serial |
5672 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Schaffter, T. et al; Albiol, F.; Caballero, L. |
|
| |
Title |
Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms |
Type |
Journal Article |
| |
Year |
2020 |
Publication |
JAMA Network Open |
Abbreviated Journal |
JAMA Netw. Open |
|
| |
Volume |
3 |
Issue |
3 |
Pages |
e200265 - 15pp |
|
| |
Keywords |
|
|
| |
Abstract |
Importance Mammography screening currently relies on subjective human interpretation. Artificial intelligence (AI) advances could be used to increase mammography screening accuracy by reducing missed cancers and false positives. Objective To evaluate whether AI can overcome human mammography interpretation limitations with a rigorous, unbiased evaluation of machine learning algorithms. Design, Setting, and Participants In this diagnostic accuracy study conducted between September 2016 and November 2017, an international, crowdsourced challenge was hosted to foster AI algorithm development focused on interpreting screening mammography. More than 1100 participants comprising 126 teams from 44 countries participated. Analysis began November 18, 2016. Main Outcomes and Measurements Algorithms used images alone (challenge 1) or combined images, previous examinations (if available), and clinical and demographic risk factor data (challenge 2) and output a score that translated to cancer yes/no within 12 months. Algorithm accuracy for breast cancer detection was evaluated using area under the curve and algorithm specificity compared with radiologists' specificity with radiologists' sensitivity set at 85.9% (United States) and 83.9% (Sweden). An ensemble method aggregating top-performing AI algorithms and radiologists' recall assessment was developed and evaluated. Results Overall, 144231 screening mammograms from 85580 US women (952 cancer positive <= 12 months from screening) were used for algorithm training and validation. A second independent validation cohort included 166578 examinations from 68008 Swedish women (780 cancer positive). The top-performing algorithm achieved an area under the curve of 0.858 (United States) and 0.903 (Sweden) and 66.2% (United States) and 81.2% (Sweden) specificity at the radiologists' sensitivity, lower than community-practice radiologists' specificity of 90.5% (United States) and 98.5% (Sweden). Combining top-performing algorithms and US radiologist assessments resulted in a higher area under the curve of 0.942 and achieved a significantly improved specificity (92.0%) at the same sensitivity. Conclusions and Relevance While no single AI algorithm outperformed radiologists, an ensemble of AI algorithms combined with radiologist assessment in a single-reader screening environment improved overall accuracy. This study underscores the potential of using machine learning methods for enhancing mammography screening interpretation. Question How do deep learning algorithms perform compared with radiologists in screening mammography interpretation? Findings In this diagnostic accuracy study using 144231 screening mammograms from 85580 women from the United States and 166578 screening mammograms from 68008 women from Sweden, no single artificial intelligence algorithm outperformed US community radiologist benchmarks; including clinical data and prior mammograms did not improve artificial intelligence performance. However, combining best-performing artificial intelligence algorithms with single-radiologist assessment demonstrated increased specificity. Meaning Integrating artificial intelligence to mammography interpretation in single-radiologist settings could yield significant performance improvements, with the potential to reduce health care system expenditures and address resource scarcity experienced in population-based screening programs. This diagnostic accuracy study evaluates whether artificial intelligence can overcome human mammography interpretation limits with a rigorous, unbiased evaluation of machine learning algorithms. |
|
| |
Address |
[Schaffter, Thomas; Hoff, Bruce; Yu, Thomas; Neto, Elias Chaibub; Friend, Stephen; Guinney, Justin] Sage Bionetworks, Computat Oncol, Seattle, WA USA, Email: gustavo@us.ibm.com |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
Amer Medical Assoc |
Place of Publication |
|
Editor |
|
|
| |
Language |
English |
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
2574-3805 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
WOS:000519249800002 |
Approved |
no |
|
| |
Is ISI |
yes |
International Collaboration |
yes |
|
| |
Call Number |
IFIC @ pastor @ |
Serial |
4683 |
|
| Permanent link to this record |
