-
Comparative Analysis of Black-Box Optimization Methods for Weather Intervention Design
Authors:
Yuta Higuchi,
Rikuto Nagai,
Atsushi Okazaki,
Masaki Ogura,
Naoki Wakamiya
Abstract:
As climate change increases the threat of weather-related disasters, research on weather control is gaining importance. The objective of weather control is to mitigate disaster risks by administering interventions with optimal timing, location, and intensity. However, the optimization process is highly challenging due to the vast scale and complexity of weather phenomena, which introduces two majo…
▽ More
As climate change increases the threat of weather-related disasters, research on weather control is gaining importance. The objective of weather control is to mitigate disaster risks by administering interventions with optimal timing, location, and intensity. However, the optimization process is highly challenging due to the vast scale and complexity of weather phenomena, which introduces two major challenges. First, obtaining accurate gradient information for optimization is difficult. In addition, numerical weather prediction (NWP) models demand enormous computational resources, necessitating parameter optimization with minimal function evaluations. To address these challenges, this study proposes a method for designing weather interventions based on black-box optimization, which enables efficient exploration without requiring gradient information. The proposed method is evaluated in two distinct control scenarios: one-shot initial value intervention and sequential intervention based on model predictive control. Furthermore, a comparative analysis is conducted among four representative black-box optimization methods in terms of total rainfall reduction. Experimental results show that Bayesian optimization achieves higher control effectiveness than the others, particularly in high-dimensional search spaces. These findings suggest that Bayesian optimization is a highly effective approach for weather intervention computation.
△ Less
Submitted 16 May, 2025;
originally announced May 2025.
-
Stable beam operation of approximately 1 mA beam under highly efficient energy recovery conditions at compact energy-recovery linac
Authors:
Hiroshi Sakai,
Dai Arakawa,
Takaaki Furuya,
Kaiichi Haga,
Masayuki Hagiwara,
Kentaro Harada,
Yosuke Honda,
Teruya Honma,
Eiji Kako,
Ryukou Kato,
Yuuji Kojima,
Taro Konomi,
Hiroshi Matsumura,
Taichi Miura,
Takako Miura,
Shinya Nagahashi,
Hirotaka Nakai,
Norio Nakamura,
Kota Nakanishi,
Kazuyuki Nigorikawa,
Takashi Nogami,
Takashi Obina,
Feng Qiu,
Hidenori Sagehashi,
Shogo Sakanaka
, et al. (15 additional authors not shown)
Abstract:
A compact energy-recovery linac (cERL) has been un-der construction at KEK since 2009 to develop key technologies for the energy-recovery linac. The cERL began operating in 2013 to create a high-current beam with a low-emittance beam with stable continuous wave (CW) superconducting cavities. Owing to the development of critical components, such as the DC gun, superconducting cavities, and the desi…
▽ More
A compact energy-recovery linac (cERL) has been un-der construction at KEK since 2009 to develop key technologies for the energy-recovery linac. The cERL began operating in 2013 to create a high-current beam with a low-emittance beam with stable continuous wave (CW) superconducting cavities. Owing to the development of critical components, such as the DC gun, superconducting cavities, and the design of ideal beam transport optics, we have successfully established approximately 1 mA stable CW operation with a small beam emittance and extremely small beam loss. This study presents the details of our key technologies and experimental results for achieving 100% energy recovery operation with extremely small beam loss during a stable, approximately 1 mA CW beam operation.
△ Less
Submitted 24 August, 2024;
originally announced August 2024.
-
Acceptance Tests of more than 10 000 Photomultiplier Tubes for the multi-PMT Digital Optical Modules of the IceCube Upgrade
Authors:
R. Abbasi,
M. Ackermann,
J. Adams,
S. K. Agarwalla,
J. A. Aguilar,
M. Ahlers,
J. M. Alameddine,
N. M. Amin,
K. Andeen,
C. Argüelles,
Y. Ashida,
S. Athanasiadou,
L. Ausborm,
S. N. Axani,
X. Bai,
A. Balagopal V.,
M. Baricevic,
S. W. Barwick,
S. Bash,
V. Basu,
R. Bay,
J. J. Beatty,
J. Becker Tjus,
J. Beise,
C. Bellenghi
, et al. (399 additional authors not shown)
Abstract:
More than 10,000 photomultiplier tubes (PMTs) with a diameter of 80 mm will be installed in multi-PMT Digital Optical Modules (mDOMs) of the IceCube Upgrade. These have been tested and pre-calibrated at two sites. A throughput of more than 1000 PMTs per week with both sites was achieved with a modular design of the testing facilities and highly automated testing procedures. The testing facilities…
▽ More
More than 10,000 photomultiplier tubes (PMTs) with a diameter of 80 mm will be installed in multi-PMT Digital Optical Modules (mDOMs) of the IceCube Upgrade. These have been tested and pre-calibrated at two sites. A throughput of more than 1000 PMTs per week with both sites was achieved with a modular design of the testing facilities and highly automated testing procedures. The testing facilities can easily be adapted to other PMTs, such that they can, e.g., be re-used for testing the PMTs for IceCube-Gen2. Single photoelectron response, high voltage dependence, time resolution, prepulse, late pulse, afterpulse probabilities, and dark rates were measured for each PMT. We describe the design of the testing facilities, the testing procedures, and the results of the acceptance tests.
△ Less
Submitted 20 June, 2024; v1 submitted 30 April, 2024;
originally announced April 2024.
-
Improved modeling of in-ice particle showers for IceCube event reconstruction
Authors:
R. Abbasi,
M. Ackermann,
J. Adams,
S. K. Agarwalla,
J. A. Aguilar,
M. Ahlers,
J. M. Alameddine,
N. M. Amin,
K. Andeen,
G. Anton,
C. Argüelles,
Y. Ashida,
S. Athanasiadou,
L. Ausborm,
S. N. Axani,
X. Bai,
A. Balagopal V.,
M. Baricevic,
S. W. Barwick,
S. Bash,
V. Basu,
R. Bay,
J. J. Beatty,
J. Becker Tjus,
J. Beise
, et al. (394 additional authors not shown)
Abstract:
The IceCube Neutrino Observatory relies on an array of photomultiplier tubes to detect Cherenkov light produced by charged particles in the South Pole ice. IceCube data analyses depend on an in-depth characterization of the glacial ice, and on novel approaches in event reconstruction that utilize fast approximations of photoelectron yields. Here, a more accurate model is derived for event reconstr…
▽ More
The IceCube Neutrino Observatory relies on an array of photomultiplier tubes to detect Cherenkov light produced by charged particles in the South Pole ice. IceCube data analyses depend on an in-depth characterization of the glacial ice, and on novel approaches in event reconstruction that utilize fast approximations of photoelectron yields. Here, a more accurate model is derived for event reconstruction that better captures our current knowledge of ice optical properties. When evaluated on a Monte Carlo simulation set, the median angular resolution for in-ice particle showers improves by over a factor of three compared to a reconstruction based on a simplified model of the ice. The most substantial improvement is obtained when including effects of birefringence due to the polycrystalline structure of the ice. When evaluated on data classified as particle showers in the high-energy starting events sample, a significantly improved description of the events is observed.
△ Less
Submitted 22 April, 2024; v1 submitted 4 March, 2024;
originally announced March 2024.
-
Measurement of Atmospheric Neutrino Mixing with Improved IceCube DeepCore Calibration and Data Processing
Authors:
IceCube Collaboration,
R. Abbasi,
M. Ackermann,
J. Adams,
S. K. Agarwalla,
J. A. Aguilar,
M. Ahlers,
J. M. Alameddine,
N. M. Amin,
K. Andeen,
G. Anton,
C. Argüelles,
Y. Ashida,
S. Athanasiadou,
S. N. Axani,
X. Bai,
A. Balagopal V.,
M. Baricevic,
S. W. Barwick,
V. Basu,
R. Bay,
J. J. Beatty,
K. -H. Becker,
J. Becker Tjus,
J. Beise
, et al. (383 additional authors not shown)
Abstract:
We describe a new data sample of IceCube DeepCore and report on the latest measurement of atmospheric neutrino oscillations obtained with data recorded between 2011-2019. The sample includes significant improvements in data calibration, detector simulation, and data processing, and the analysis benefits from a detailed treatment of systematic uncertainties, with significantly higher level of detai…
▽ More
We describe a new data sample of IceCube DeepCore and report on the latest measurement of atmospheric neutrino oscillations obtained with data recorded between 2011-2019. The sample includes significant improvements in data calibration, detector simulation, and data processing, and the analysis benefits from a detailed treatment of systematic uncertainties, with significantly higher level of detail since our last study. By measuring the relative fluxes of neutrino flavors as a function of their reconstructed energies and arrival directions we constrain the atmospheric neutrino mixing parameters to be $\sin^2θ_{23} = 0.51\pm 0.05$ and $Δm^2_{32} = 2.41\pm0.07\times 10^{-3}\mathrm{eV}^2$, assuming a normal mass ordering. The resulting 40\% reduction in the error of both parameters with respect to our previous result makes this the most precise measurement of oscillation parameters using atmospheric neutrinos. Our results are also compatible and complementary to those obtained using neutrino beams from accelerators, which are obtained at lower neutrino energies and are subject to different sources of uncertainties.
△ Less
Submitted 8 August, 2023; v1 submitted 24 April, 2023;
originally announced April 2023.
-
Graph Neural Networks for Low-Energy Event Classification & Reconstruction in IceCube
Authors:
R. Abbasi,
M. Ackermann,
J. Adams,
N. Aggarwal,
J. A. Aguilar,
M. Ahlers,
M. Ahrens,
J. M. Alameddine,
A. A. Alves Jr.,
N. M. Amin,
K. Andeen,
T. Anderson,
G. Anton,
C. Argüelles,
Y. Ashida,
S. Athanasiadou,
S. Axani,
X. Bai,
A. Balagopal V.,
M. Baricevic,
S. W. Barwick,
V. Basu,
R. Bay,
J. J. Beatty,
K. -H. Becker
, et al. (359 additional authors not shown)
Abstract:
IceCube, a cubic-kilometer array of optical sensors built to detect atmospheric and astrophysical neutrinos between 1 GeV and 1 PeV, is deployed 1.45 km to 2.45 km below the surface of the ice sheet at the South Pole. The classification and reconstruction of events from the in-ice detectors play a central role in the analysis of data from IceCube. Reconstructing and classifying events is a challen…
▽ More
IceCube, a cubic-kilometer array of optical sensors built to detect atmospheric and astrophysical neutrinos between 1 GeV and 1 PeV, is deployed 1.45 km to 2.45 km below the surface of the ice sheet at the South Pole. The classification and reconstruction of events from the in-ice detectors play a central role in the analysis of data from IceCube. Reconstructing and classifying events is a challenge due to the irregular detector geometry, inhomogeneous scattering and absorption of light in the ice and, below 100 GeV, the relatively low number of signal photons produced per event. To address this challenge, it is possible to represent IceCube events as point cloud graphs and use a Graph Neural Network (GNN) as the classification and reconstruction method. The GNN is capable of distinguishing neutrino events from cosmic-ray backgrounds, classifying different neutrino event types, and reconstructing the deposited energy, direction and interaction vertex. Based on simulation, we provide a comparison in the 1-100 GeV energy range to the current state-of-the-art maximum likelihood techniques used in current IceCube analyses, including the effects of known systematic uncertainties. For neutrino event classification, the GNN increases the signal efficiency by 18% at a fixed false positive rate (FPR), compared to current IceCube methods. Alternatively, the GNN offers a reduction of the FPR by over a factor 8 (to below half a percent) at a fixed signal efficiency. For the reconstruction of energy, direction, and interaction vertex, the resolution improves by an average of 13%-20% compared to current maximum likelihood techniques in the energy range of 1-30 GeV. The GNN, when run on a GPU, is capable of processing IceCube events at a rate nearly double of the median IceCube trigger rate of 2.7 kHz, which opens the possibility of using low energy neutrinos in online searches for transient events.
△ Less
Submitted 11 October, 2022; v1 submitted 7 September, 2022;
originally announced September 2022.
-
Low Energy Event Reconstruction in IceCube DeepCore
Authors:
R. Abbasi,
M. Ackermann,
J. Adams,
J. A. Aguilar,
M. Ahlers,
M. Ahrens,
J. M. Alameddine,
A. A. Alves Jr.,
N. M. Amin,
K. Andeen,
T. Anderson,
G. Anton,
C. Argüelles,
Y. Ashida,
S. Axani,
X. Bai,
A. Balagopal V.,
S. W. Barwick,
B. Bastian,
V. Basu,
S. Baur,
R. Bay,
J. J. Beatty,
K. -H. Becker,
J. Becker Tjus
, et al. (360 additional authors not shown)
Abstract:
The reconstruction of event-level information, such as the direction or energy of a neutrino interacting in IceCube DeepCore, is a crucial ingredient to many physics analyses. Algorithms to extract this high level information from the detector's raw data have been successfully developed and used for high energy events. In this work, we address unique challenges associated with the reconstruction o…
▽ More
The reconstruction of event-level information, such as the direction or energy of a neutrino interacting in IceCube DeepCore, is a crucial ingredient to many physics analyses. Algorithms to extract this high level information from the detector's raw data have been successfully developed and used for high energy events. In this work, we address unique challenges associated with the reconstruction of lower energy events in the range of a few to hundreds of GeV and present two separate, state-of-the-art algorithms. One algorithm focuses on the fast directional reconstruction of events based on unscattered light. The second algorithm is a likelihood-based multipurpose reconstruction offering superior resolutions, at the expense of larger computational cost.
△ Less
Submitted 4 March, 2022;
originally announced March 2022.
-
Performance of the D-Egg Optical Sensor for the IceCube Upgrade
Authors:
Colton Hill,
Maximillian Meier,
Ryo Nagai,
Ken'ichi Kin,
Nobuhiro Shimizu,
Aya Ishihara,
Shigeru Yoshida,
Tyler Anderson,
Jim Braun,
Aaron Fienberg,
Jeff Weber
Abstract:
New optical sensors called the "D-Egg" have been developed for cost-effective instrumentation for the IceCube Upgrade. With two 8-inch high quantum efficient photomultiplier tubes (PMTs), they offer increased effective photocathode area while retaining as much of the successful IceCube Digital Optical Module design as possible. Mass production of D-Eggs has started in 2020. By the end of 2021, the…
▽ More
New optical sensors called the "D-Egg" have been developed for cost-effective instrumentation for the IceCube Upgrade. With two 8-inch high quantum efficient photomultiplier tubes (PMTs), they offer increased effective photocathode area while retaining as much of the successful IceCube Digital Optical Module design as possible. Mass production of D-Eggs has started in 2020. By the end of 2021, there will be 310 D-Eggs produced with 288 deployed in the IceCube Upgrade. The D-Egg readout system uses advanced technologies in electronics and computing power. Each of the two PMT signals is digitised using ultra-low-power 14-bit ADCs with a sampling frequency of 240 megaSPS, enabling seamless and lossless event recording from single-photon signals to signals exceeding 200 PE within 10 nanosecond, as well as flexible event triggering. In this paper, we report the single photon detection performance as well as the multiple photon recording capability of D-Eggs from the mass production line which have been evaluated with the built-in data acquisition system.
△ Less
Submitted 11 August, 2021;
originally announced August 2021.
-
LeptonInjector and LeptonWeighter: A neutrino event generator and weighter for neutrino observatories
Authors:
R. Abbasi,
M. Ackermann,
J. Adams,
J. A. Aguilar,
M. Ahlers,
M. Ahrens,
C. Alispach,
A. A. Alves Jr.,
N. M. Amin,
R. An,
K. Andeen,
T. Anderson,
I. Ansseau,
G. Anton,
C. Argüelles,
S. Axani,
X. Bai,
A. Balagopal V.,
A. Barbano,
S. W. Barwick,
B. Bastian,
V. Basu,
V. Baum,
S. Baur,
R. Bay
, et al. (341 additional authors not shown)
Abstract:
We present a high-energy neutrino event generator, called LeptonInjector, alongside an event weighter, called LeptonWeighter. Both are designed for large-volume Cherenkov neutrino telescopes such as IceCube. The neutrino event generator allows for quick and flexible simulation of neutrino events within and around the detector volume, and implements the leading Standard Model neutrino interaction p…
▽ More
We present a high-energy neutrino event generator, called LeptonInjector, alongside an event weighter, called LeptonWeighter. Both are designed for large-volume Cherenkov neutrino telescopes such as IceCube. The neutrino event generator allows for quick and flexible simulation of neutrino events within and around the detector volume, and implements the leading Standard Model neutrino interaction processes relevant for neutrino observatories: neutrino-nucleon deep-inelastic scattering and neutrino-electron annihilation. In this paper, we discuss the event generation algorithm, the weighting algorithm, and the main functions of the publicly available code, with examples.
△ Less
Submitted 4 May, 2021; v1 submitted 18 December, 2020;
originally announced December 2020.
-
Machine Learning Exchange-Correlation Potential in Time Dependent Density Functional Theory
Authors:
Yasumitsu Suzuki,
Ryo Nagai,
Jun Haruyama
Abstract:
We propose a machine learning based approach to develop the exchange-correlation potential of time dependent density functional theory (TDDFT). The neural network projection from the time-varying electron densities to the corresponding correlation potentials in the time-dependent Kohn-Sham equation is trained using a few exact datasets for a model system of electron-hydrogen scattering. We demonst…
▽ More
We propose a machine learning based approach to develop the exchange-correlation potential of time dependent density functional theory (TDDFT). The neural network projection from the time-varying electron densities to the corresponding correlation potentials in the time-dependent Kohn-Sham equation is trained using a few exact datasets for a model system of electron-hydrogen scattering. We demonstrate that this neural network potential can capture the complex structures in the time-dependent correlation potential during the scattering process and provide correct scattering probabilities, which are not obtained by the standard adiabatic functionals. We also show that it is possible to incorporate the nonadiabatic (or memory) effect in the potential with this machine learning technique, which significantly improves the accuracy of the dynamics. The method developed here offers a novel way to improve the exchange-correlation potential of TDDFT, which makes the theory a more powerful tool to study various excited state phenomena.
△ Less
Submitted 16 April, 2020; v1 submitted 16 February, 2020;
originally announced February 2020.
-
Combined sensitivity to the neutrino mass ordering with JUNO, the IceCube Upgrade, and PINGU
Authors:
IceCube-Gen2 Collaboration,
:,
M. G. Aartsen,
M. Ackermann,
J. Adams,
J. A. Aguilar,
M. Ahlers,
M. Ahrens,
C. Alispach,
K. Andeen,
T. Anderson,
I. Ansseau,
G. Anton,
C. Argüelles,
T. C. Arlen,
J. Auffenberg,
S. Axani,
P. Backes,
H. Bagherpour,
X. Bai,
A. Balagopal V.,
A. Barbano,
I. Bartos,
S. W. Barwick,
B. Bastian
, et al. (421 additional authors not shown)
Abstract:
The ordering of the neutrino mass eigenstates is one of the fundamental open questions in neutrino physics. While current-generation neutrino oscillation experiments are able to produce moderate indications on this ordering, upcoming experiments of the next generation aim to provide conclusive evidence. In this paper we study the combined performance of the two future multi-purpose neutrino oscill…
▽ More
The ordering of the neutrino mass eigenstates is one of the fundamental open questions in neutrino physics. While current-generation neutrino oscillation experiments are able to produce moderate indications on this ordering, upcoming experiments of the next generation aim to provide conclusive evidence. In this paper we study the combined performance of the two future multi-purpose neutrino oscillation experiments JUNO and the IceCube Upgrade, which employ two very distinct and complementary routes towards the neutrino mass ordering. The approach pursued by the $20\,\mathrm{kt}$ medium-baseline reactor neutrino experiment JUNO consists of a careful investigation of the energy spectrum of oscillated $\barν_e$ produced by ten nuclear reactor cores. The IceCube Upgrade, on the other hand, which consists of seven additional densely instrumented strings deployed in the center of IceCube DeepCore, will observe large numbers of atmospheric neutrinos that have undergone oscillations affected by Earth matter. In a joint fit with both approaches, tension occurs between their preferred mass-squared differences $ Δm_{31}^{2}=m_{3}^{2}-m_{1}^{2} $ within the wrong mass ordering. In the case of JUNO and the IceCube Upgrade, this allows to exclude the wrong ordering at $>5σ$ on a timescale of 3--7 years --- even under circumstances that are unfavorable to the experiments' individual sensitivities. For PINGU, a 26-string detector array designed as a potential low-energy extension to IceCube, the inverted ordering could be excluded within 1.5 years (3 years for the normal ordering) in a joint analysis.
△ Less
Submitted 15 November, 2019;
originally announced November 2019.
-
Electronics Development for the New Photo-Detectors (PDOM and D-Egg) for IceCube-Upgrade
Authors:
Ryo Nagai,
Aya Ishihara
Abstract:
The planned IceCube-Upgrade will enhance the capability of IceCube in the detection of GeV-scale neutrino physics and enable an improved measurement of the properties of the glacial ice. Three types of new optical sensors will be deployed during the Upgrade: PDOM, D-Egg, and mDOM. Since the design of the PDOM and D-Egg are very similar, the development of the front-end electronics for the two opti…
▽ More
The planned IceCube-Upgrade will enhance the capability of IceCube in the detection of GeV-scale neutrino physics and enable an improved measurement of the properties of the glacial ice. Three types of new optical sensors will be deployed during the Upgrade: PDOM, D-Egg, and mDOM. Since the design of the PDOM and D-Egg are very similar, the development of the front-end electronics for the two optical sensors has been merged. The photo-electron signals detected by the PMTs are digitized with high-speed ultra-low power ADCs and processed in an FPGA, before being sent to the data acquisition system located on the surface of the South-Pole glacier. The almost final revision of the front-end electronics is equipped with the common microcontroller unit and the communication daughter board for simplifying the communication scheme for the three different modules. This contribution focuses on the design of the front-end electronics and presents first results from the performance tests.
△ Less
Submitted 30 August, 2019;
originally announced August 2019.
-
Completing density functional theory by machine-learning hidden messages from molecules
Authors:
Ryo Nagai,
Ryosuke Akashi,
Osamu Sugino
Abstract:
Kohn-Sham density functional theory is the base of modern computational approaches to electronic structures. Their accuracy vitally relies on the exchange-correlation energy functional, which encapsulates electron-electron interaction beyond the classical one. The functional provides a way to obtain the density and energy without solving the many-body equation and can, in principle, be determined…
▽ More
Kohn-Sham density functional theory is the base of modern computational approaches to electronic structures. Their accuracy vitally relies on the exchange-correlation energy functional, which encapsulates electron-electron interaction beyond the classical one. The functional provides a way to obtain the density and energy without solving the many-body equation and can, in principle, be determined to reproduce the exact ones universally. However, the past approaches are dependent on the theoretical development, which limits the possibility of the functional to human's intuition. Here, we demonstrate a systematic way to machine-learn a functional from a database, without complicated assumptions. The density and energy are related with a flexible feed-forward neural network, which is trained to reproduce accurate dataset, and the KS-DFT is solved by taking the functional derivatives with the back-propagation technique. Surprisingly, a trial functional, trained for just a few molecules, has been shown to be applicable to hundreds of molecular systems with comparable accuracy to the standard functionals. Also, by adding the nodes connected to the hidden layers, a non-local term is straightforwardly included to improve accuracy, which has been hitherto impractically difficult. Utilizing the strategy of rapidly advancing machine learning techniques, this novel approach is expected to enrich the DFT framework by constructing a functional just from a database for materials conventionally difficult to calculate accurately.
△ Less
Submitted 31 October, 2019; v1 submitted 1 March, 2019;
originally announced March 2019.
-
Neural-network Kohn-Sham exchange-correlation potential and its out-of-training transferability
Authors:
Ryo Nagai,
Ryosuke Akashi,
Shu Sasaki,
Shinji Tsuneyuki
Abstract:
We incorporate in the Kohn-Sham self consistent equation a trained neural-network projection from the charge density distribution to the Hartree-exchange-correlation potential $n \rightarrow V_{\rm Hxc}$ for possible numerical approach to the exact Kohn-Sham scheme. The potential trained through a newly developed scheme enables us to evaluate the total energy without explicitly treating the formul…
▽ More
We incorporate in the Kohn-Sham self consistent equation a trained neural-network projection from the charge density distribution to the Hartree-exchange-correlation potential $n \rightarrow V_{\rm Hxc}$ for possible numerical approach to the exact Kohn-Sham scheme. The potential trained through a newly developed scheme enables us to evaluate the total energy without explicitly treating the formula of the exchange-correlation energy. With a case study of a simple model we show that the well-trained neural-network $V_{\rm Hxc}$ achieves accuracy for the charge density and total energy out of the model parameter range used for the training, indicating that the property of the elusive ideal functional form of $V_{\rm Hxc}$ can approximately be encapsulated by the machine-learning construction. We also exemplify a factor that crucially limits the transferability--the boundary in the model parameter space where the number of the one-particle bound states changes--and see that this is cured by setting the training parameter range across that boundary. The training scheme and insights from the model study apply to more general systems, opening a novel path to numerically efficient Kohn-Sham potential.
△ Less
Submitted 8 February, 2018;
originally announced February 2018.
-
Generating carrier-envelope-phase stabilized few-cycle pulses from a free-electron laser oscillator
Authors:
Ryoichi Hajima,
Ryoji Nagai
Abstract:
We propose a scheme to generate carrier-envelope-phase (CEP) stabilized few-cycle optical pulses from a free-electron laser oscillator. The CEP stabilization is realized by continuous injection of CEP-stabilized seed pulses from an external laser to the FEL oscillator whose cavity length is perfectly synchronized to the electron bunch repetition. Such CEP-stabilized few-cycle FEL pulses will be an…
▽ More
We propose a scheme to generate carrier-envelope-phase (CEP) stabilized few-cycle optical pulses from a free-electron laser oscillator. The CEP stabilization is realized by continuous injection of CEP-stabilized seed pulses from an external laser to the FEL oscillator whose cavity length is perfectly synchronized to the electron bunch repetition. Such CEP-stabilized few-cycle FEL pulses will be an efficient driver for exploring high-harmonic generation at energies 1-10~keV for attosecond and zeptosecond science, broad-band x-ray optical frequency combs and coherent control of chemical reaction.
△ Less
Submitted 11 June, 2017; v1 submitted 7 June, 2017;
originally announced June 2017.
-
Ultra-low-loss tapered optical fibers with minimal lengths
Authors:
Ryutaro Nagai,
Takao Aoki
Abstract:
We design and fabricate ultra-low-loss tapered optical fibers (TOFs) with minimal lengths. We first optimize variations of the torch scan length using the flame-brush method for fabricating TOFs with taper angles that satisfy the adiabaticity criteria. We accordingly fabricate TOFs with optimal shapes and compare their transmission to TOFs with a constant taper angle and TOFs with an exponential s…
▽ More
We design and fabricate ultra-low-loss tapered optical fibers (TOFs) with minimal lengths. We first optimize variations of the torch scan length using the flame-brush method for fabricating TOFs with taper angles that satisfy the adiabaticity criteria. We accordingly fabricate TOFs with optimal shapes and compare their transmission to TOFs with a constant taper angle and TOFs with an exponential shape. The highest transmission measured for TOFs with an optimal shape is in excess of 99.7 % with a total TOF length of only 23 mm, whereas TOFs with a constant taper angle of 2 mrad reach 99.6 % transmission for a 63 mm TOF length.
△ Less
Submitted 9 November, 2014;
originally announced November 2014.
-
Planar Pixel Sensors for the ATLAS Upgrade: Beam Tests results
Authors:
J. Weingarten,
S. Altenheiner,
M. Beimforde,
M. Benoit,
M. Bomben,
G. Calderini,
C. Gallrapp,
M. George,
S. Gibson,
S. Grinstein,
Z. Janoska,
J. Jentzsch,
O. Jinnouchi,
T. Kishida,
A. La Rosa,
V. Libov,
A. Macchiolo,
G. Marchiori,
D. Münstermann,
R. Nagai,
G. Piacquadio,
B. Ristic,
I. Rubinskiy,
A. Rummler,
Y. Takubo
, et al. (6 additional authors not shown)
Abstract:
Results of beam tests with planar silicon pixel sensors aimed towards the ATLAS Insertable B-Layer and High Luminosity LHC (HL-LHC) upgrades are presented. Measurements include spatial resolution, charge collection performance and charge sharing between neighbouring cells as a function of track incidence angle for different bulk materials. Measurements of n-in-n pixel sensors are presented as a fu…
▽ More
Results of beam tests with planar silicon pixel sensors aimed towards the ATLAS Insertable B-Layer and High Luminosity LHC (HL-LHC) upgrades are presented. Measurements include spatial resolution, charge collection performance and charge sharing between neighbouring cells as a function of track incidence angle for different bulk materials. Measurements of n-in-n pixel sensors are presented as a function of fluence for different irradiations. Furthermore p-type silicon sensors from several vendors with slightly differing layouts were tested. All tested sensors were connected by bump-bonding to the ATLAS Pixel read-out chip. We show that both n-type and p-type tested planar sensors are able to collect significant charge even after integrated fluences expected at HL-LHC.
△ Less
Submitted 9 November, 2012; v1 submitted 5 April, 2012;
originally announced April 2012.