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Author |
El-Neaj, Y.A. et al; Bernabeu, J. |
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Title |
AEDGE: Atomic Experiment for Dark Matter and Gravity Exploration in Space |
Type |
Journal Article |
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Year |
2020 |
Publication |
EPJ Quantum Technology |
Abbreviated Journal |
EPJ Quantum Technol. |
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Volume |
7 |
Issue |
1 |
Pages |
6 - 27pp |
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Abstract |
We propose in this White Paper a concept for a space experiment using cold atoms to search for ultra-light dark matter, and to detect gravitational waves in the frequency range between the most sensitive ranges of LISA and the terrestrial LIGO/Virgo/KAGRA/INDIGO experiments. This interdisciplinary experiment, called Atomic Experiment for Dark Matter and Gravity Exploration (AEDGE), will also complement other planned searches for dark matter, and exploit synergies with other gravitational wave detectors. We give examples of the extended range of sensitivity to ultra-light dark matter offered by AEDGE, and how its gravitational-wave measurements could explore the assembly of super-massive black holes, first-order phase transitions in the early universe and cosmic strings. AEDGE will be based upon technologies now being developed for terrestrial experiments using cold atoms, and will benefit from the space experience obtained with, e.g., LISA and cold atom experiments in microgravity. KCL-PH-TH/2019-65, CERN-TH-2019-126 |
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[El-Neaj, Yousef Abou] Harvard Univ, Phys Dept, Cambridge, MA 02138 USA, Email: o.buchmueller@imperial.ac.uk |
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Springeropen |
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English |
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2662-4400 |
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WOS:000519468200001 |
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no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
4325 |
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Author |
Alonso, I. et al; Bernabeu, J. |
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Title |
Cold atoms in space: community workshop summary and proposed road-map |
Type |
Journal Article |
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Year |
2022 |
Publication |
EPJ Quantum Technology |
Abbreviated Journal |
EPJ Quantum Technol. |
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Volume |
9 |
Issue |
1 |
Pages |
30 - 55pp |
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Abstract |
We summarise the discussions at a virtual Community Workshop on Cold Atoms in Space concerning the status of cold atom technologies, the prospective scientific and societal opportunities offered by their deployment in space, and the developments needed before cold atoms could be operated in space. The cold atom technologies discussed include atomic clocks, quantum gravimeters and accelerometers, and atom interferometers. Prospective applications include metrology, geodesy and measurement of terrestrial mass change due to, e.g., climate change, and fundamental science experiments such as tests of the equivalence principle, searches for dark matter, measurements of gravitational waves and tests of quantum mechanics. We review the current status of cold atom technologies and outline the requirements for their space qualification, including the development paths and the corresponding technical milestones, and identifying possible pathfinder missions to pave the way for missions to exploit the full potential of cold atoms in space. Finally, we present a first draft of a possible road-map for achieving these goals, that we propose for discussion by the interested cold atom, Earth Observation, fundamental physics and other prospective scientific user communities, together with the European Space Agency (ESA) and national space and research funding agencies. |
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[Alonso, Ivan] Univ Balearic Isl, Higher Polytech Sch, Valldemossa Rd, Palma De Mallorca 07122, Spain, Email: Oliver.Buchmueller@cern.ch; |
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Springer |
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2662-4400 |
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Notes |
WOS:000885839700002 |
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no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
5424 |
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Author |
Schaffter, T. et al; Albiol, F.; Caballero, L. |
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Title |
Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms |
Type |
Journal Article |
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Year |
2020 |
Publication |
JAMA Network Open |
Abbreviated Journal |
JAMA Netw. Open |
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Volume |
3 |
Issue |
3 |
Pages |
e200265 - 15pp |
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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. |
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[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 |
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Amer Medical Assoc |
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English |
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ISSN |
2574-3805 |
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Notes |
WOS:000519249800002 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
4683 |
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Author |
LHC BSM Reinterpretation Forum (Abdallah, W. et al); Mitsou, V.A.; Sanz, V. |
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Title |
Reinterpretation of LHC results for new physics: status and recommendations after run 2 |
Type |
Journal Article |
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Year |
2020 |
Publication |
Scipost Physics |
Abbreviated Journal |
SciPost Phys. |
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Volume |
9 |
Issue |
2 |
Pages |
022 - 45pp |
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Keywords |
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Abstract |
We report on the status of efforts to improve the reinterpretation of searches and measurements at the LHC in terms of models for new physics, in the context of the LHC Reinterpretation Forum. We detail current experimental offerings in direct searches for new particles, measurements, technical implementations and Open Data, and provide a set of recommendations for further improving the presentation of LHC results in order to better enable reinterpretation in the future. We also provide a brief description of existing software reinterpretation frameworks and recent global analyses of new physics that make use of the current data. |
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[Abdallah, Waleed; Dutta, Juhi] Harish Chandra Res Inst HBNI, Allahabad 211019, Uttar Pradesh, India, Email: Andy.Buckley@glasgow.ac.uk; |
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Scipost Foundation |
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English |
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2542-4653 |
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WOS:000573102600007 |
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no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
4547 |
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Author |
Barenboim, G.; Hirn, J.; Sanz, V. |
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Title |
Symmetry meets AI |
Type |
Journal Article |
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Year |
2021 |
Publication |
Scipost Physics |
Abbreviated Journal |
SciPost Phys. |
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Volume |
11 |
Issue |
1 |
Pages |
014 - 11pp |
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Abstract |
We explore whether Neural Networks (NNs) can discover the presence of symmetries as they learn to perform a task. For this, we train hundreds of NNs on a decoy task based on well-controlled Physics templates, where no information on symmetry is provided. We use the output from the last hidden layer of all these NNs, projected to fewer dimensions, as the input for a symmetry classification task, and show that information on symmetry had indeed been identified by the original NN without guidance. As an interdisciplinary application of this procedure, we identify the presence and level of symmetry in artistic paintings from different styles such as those of Picasso, Pollock and Van Gogh. |
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Address |
[Barenboim, Gabriela; Hirn, Johannes; Sanz, Veronica] Univ Valencia, CSIC, Dept Fis Teor, E-46100 Burjassot, Spain |
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Scipost Foundation |
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English |
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2542-4653 |
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Notes |
WOS:000680039500002 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
4920 |
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Author |
Khosa, C.K.; Sanz, V.; Soughton, M. |
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Title |
Using machine learning to disentangle LHC signatures of Dark Matter candidates |
Type |
Journal Article |
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Year |
2021 |
Publication |
Scipost Physics |
Abbreviated Journal |
SciPost Phys. |
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Volume |
10 |
Issue |
6 |
Pages |
151 - 26pp |
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Abstract |
We study the prospects of characterising Dark Matter at colliders using Machine Learning (ML) techniques. We focus on the monojet and missing transverse energy (MET) channel and propose a set of benchmark models for the study: a typical WIMP Dark Matter candidate in the form of a SUSY neutralino, a pseudo-Goldstone impostor in the shape of an Axion-Like Particle, and a light Dark Matter impostor whose interactions are mediated by a heavy particle. All these benchmarks are tensioned against each other, and against the main SM background (Z+jets). Our analysis uses both the leading-order kinematic features as well as the information of an additional hard jet. We explore different representations of the data, from a simple event data sample with values of kinematic variables fed into a Logistic Regression algorithm or a Fully Connected Neural Network, to a transformation of the data into images related to probability distributions, fed to Deep and Convolutional Neural Networks. We also study the robustness of our method against including detector effects, dropping kinematic variables, or changing the number of events per image. In the case of signals with more combinatorial possibilities (events with more than one hard jet), the most crucial data features are selected by performing a Principal Component Analysis. We compare the performance of all these methods, and find that using the 2D images of the combined information of multiple events significantly improves the discrimination performance. |
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Address |
[Khosa, Charanjit Kaur; Sanz, Veronica; Soughton, Michael] Univ Sussex, Dept Phys & Astron, Brighton BN1 9QH, E Sussex, England, Email: Charanjit.Kaur@sussex.ac.uk; |
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Scipost Foundation |
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English |
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2542-4653 |
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WOS:000680038800002 |
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no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
4927 |
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Author |
Doring, C.; Centelles Chulia, S.; Lindner, M.; Schaefer, B.M.; Bartelmann, M. |
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Title |
Gravitational wave induced baryon acoustic oscillations |
Type |
Journal Article |
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Year |
2022 |
Publication |
Scipost Physics |
Abbreviated Journal |
SciPost Phys. |
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Volume |
12 |
Issue |
3 |
Pages |
114 - 47pp |
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Abstract |
We study the impact of gravitational waves originating from a first order phase transition on structure formation. To do so, we perform a second order perturbation analysis in the 1 + 3 covariant framework and derive a wave equation in which second order, adiabatic density perturbations of the photon-baryon fluid are sourced by the gravitational wave energy density during radiation domination and on sub-horizon scales. The scale on which such waves affect the energy density perturbation spectrum is found to be proportional to the horizon size at the time of the phase transition times its inverse duration. Consequently, structure of the size of galaxies and bigger can only be affected in this way by relatively late phase transitions at >= 10(6) s. Using cosmic variance as a bound we derive limits on the strength a and the relative duration (beta/H-*)(-1) of phase transitions as functions of the time of their occurrence which results in a new exclusion region for the energy density in gravitational waves today. We find that the cosmic variance bound forbids only relative long lasting phase transitions, e.g. beta/H-* less than or similar to 6.8 for t(*) approximate to 5 x 10(11 )s, which exhibit a substantial amount of supercooling alpha > 20 to affect the matter power spectrum. |
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Address |
[Doering, Christian; Lindner, Manfred] Max Planck Inst Kernphys, Saupfercheckweg 1, D-69117 Heidelberg, Germany, Email: cdoering@mpi-hd.mpg.de; |
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Scipost Foundation |
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English |
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2542-4653 |
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WOS:000782238100035 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
5201 |
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Author |
Cranmer, K. et al; Sanz, V. |
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Title |
Publishing statistical models: Getting the most out of particle physics experiments |
Type |
Journal Article |
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Year |
2022 |
Publication |
Scipost Physics |
Abbreviated Journal |
SciPost Phys. |
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Volume |
12 |
Issue |
1 |
Pages |
037 - 55pp |
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Abstract |
The statistical models used to derive the results of experimental analyses are of incredible scientific value and are essential information for analysis preservation and reuse. In this paper, we make the scientific case for systematically publishing the full statistical models and discuss the technical developments that make this practical. By means of a variety of physics cases – including parton distribution functions, Higgs boson measurements, effective field theory interpretations, direct searches for new physics, heavy flavor physics, direct dark matter detection, world averages, and beyond the Standard Model global fits – we illustrate how detailed information on the statistical modelling can enhance the short- and long-term impact of experimental results. |
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Address |
[Cranmer, Kyle; Held, Alexander] NYU, New York, NY 10003 USA, Email: kyle.cranmer@nyu.edu; |
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Scipost Foundation |
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English |
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2542-4653 |
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Notes |
WOS:000807448000032 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
5255 |
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Author |
Aarrestad, T. et al; Mamuzic, J.; Ruiz de Austri, R. |
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Title |
Benchmark data and model independent event classification for the large hadron collider |
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Journal Article |
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Year |
2022 |
Publication |
Scipost Physics |
Abbreviated Journal |
SciPost Phys. |
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Volume |
12 |
Issue |
1 |
Pages |
043 - 57pp |
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Abstract |
We describe the outcome of a data challenge conducted as part of the Dark Machines (https://www.darkmachines.org) initiative and the Les Houches 2019 workshop on Physics at TeV colliders. The challenged aims to detect signals of new physics at the Large Hadron Collider (LHC) using unsupervised machine learning algorithms. First, we propose how an anomaly score could be implemented to define model-independent signal regions in LHC searches. We define and describe a large benchmark dataset, consisting of > 1 billion simulated LHC events corresponding to 10 fb(-1) of proton-proton collisions at a center-of-mass energy of 13 TeV. We then review a wide range of anomaly detection and density estimation algorithms, developed in the context of the data challenge, and we measure their performance in a set of realistic analysis environments. We draw a number of useful conclusions that will aid the development of unsupervised new physics searches during the third run of the LHC, and provide our benchmark dataset for future studies at https://www.phenoMLdata.org. Code to reproduce the analysis is provided at https://github.com/bostdiek/DarkMachines-UnsupervisedChallenge. |
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Address |
[Aarrestad, Thea; Heinrich, Lukas A.; Jawahar, Pratik; Pierini, Maurizio; Touranakou, Mary; Wozniak, Kinga A.] European Org Nucl Res CERN, CH-1211 Geneva 23, Switzerland |
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Scipost Foundation |
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English |
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2542-4653 |
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WOS:000807448000038 |
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no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
5256 |
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Author |
Horak, J.; Ihssen, F.; Papavassiliou, J.; Pawlowski, J.M.; Weber, A.; Wetterich, C. |
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Title |
Gluon condensates and effective gluon mass |
Type |
Journal Article |
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Year |
2022 |
Publication |
Scipost Physics |
Abbreviated Journal |
SciPost Phys. |
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Volume |
13 |
Issue |
2 |
Pages |
042 - 40pp |
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Abstract |
Lattice simulations along with studies in continuum QCD indicate that non-perturbative quantum fluctuations lead to an infrared regularisation of the gluon propagator in covariant gauges in the form of an effective mass-like behaviour. In the present work we propose an analytic understanding of this phenomenon in terms of gluon condensation through a dynamical version of the Higgs mechanism, leading to the emergence of color condensates. Within the functional renormalisation group approach we compute the effective potential of covariantly constant field strengths, whose non-trivial minimum is related to the color condensates. In the physical case of an SU(3) gauge group this is an octet condensate. The value of the gluon mass obtained through this procedure compares very well to lattice results and the mass gap arising from alternative dynamical scenarios. |
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Address |
[Horak, Jan; Ihssen, Friederike; Pawlowski, Jan M.; Weber, Axel; Wetterich, Christof] Heidelberg Univ, Inst Theoret Phys, Philosophenweg 16, D-69120 Heidelberg, Germany |
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Scipost Foundation |
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2542-4653 |
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Notes |
WOS:000863121000008 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
5379 |
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