Abstract Solar flares are explosions on the Sun. They happen when energy stored in magnetic fields around solar active regions (ARs) is suddenly released. Solar flares and accompanied coronal mass ejections are sources of space weather, which negatively affects a variety of technologies at or near Earth, ranging from blocking high-frequency radio waves used for radio communication to degrading power grid operations. Monitoring and providing early and accurate prediction of solar flares is therefore crucial for preparedness and disaster risk management. In this article, we present a transformer-based framework, named SolarFlareNet, for predicting whether an AR would produce a$$\gamma$$ -class flare within the next 24 to 72 h. We consider three$$\gamma$$ classes, namely the$$\ge$$ M5.0 class, the$$\ge$$ M class and the$$\ge$$ C class, and build three transformers separately, each corresponding to a$$\gamma$$ class. Each transformer is used to make predictions of its corresponding$$\gamma$$ -class flares. The crux of our approach is to model data samples in an AR as time series and to use transformers to capture the temporal dynamics of the data samples. Each data sample consists of magnetic parameters taken from Space-weather HMI Active Region Patches (SHARP) and related data products. We survey flare events that occurred from May 2010 to December 2022 using the Geostationary Operational Environmental Satellite X-ray flare catalogs provided by the National Centers for Environmental Information (NCEI), and build a database of flares with identified ARs in the NCEI flare catalogs. This flare database is used to construct labels of the data samples suitable for machine learning. We further extend the deterministic approach to a calibration-based probabilistic forecasting method. The SolarFlareNet system is fully operational and is capable of making near real-time predictions of solar flares on the Web.
more »
« less
Mining the Mind: Linear Discriminant Analysis of MEG Source Reconstruction Time Series Supports Dynamic Changes in Deep Brain Regions During Meditation Sessions
Abstract Meditation practices have been claimed to have a positive effect on the regulation of mood and emotions for quite some time by practitioners, and in recent times there has been a sustained effort to provide a more precise description of the influence of meditation on the human brain. Longitudinal studies have reported morphological changes in cortical thickness and volume in selected brain regions due to meditation practice, which is interpreted as an evidence its effectiveness beyond the subjective self reporting. Using magnetoencephalography (MEG) or electroencephalography to quantify the changes in brain activity during meditation practice represents a challenge, as no clear hypothesis about the spatial or temporal pattern of such changes is available to date. In this article we consider MEG data collected during meditation sessions of experienced Buddhist monks practicing focused attention (Samatha) and open monitoring (Vipassana) meditation, contrasted by resting state with eyes closed. The MEG data are first mapped to time series of brain activity averaged over brain regions corresponding to a standard Destrieux brain atlas. Next, by bootstrapping and spectral analysis, the data are mapped to matrices representing random samples of power spectral densities in$$\alpha$$ ,$$\beta$$ ,$$\gamma$$ , and$$\theta$$ frequency bands. We use linear discriminant analysis to demonstrate that the samples corresponding to different meditative or resting states contain enough fingerprints of the brain state to allow a separation between different states, and we identify the brain regions that appear to contribute to the separation. Our findings suggest that the cingulate cortex, insular cortex and some of the internal structures, most notably the accumbens, the caudate and the putamen nuclei, the thalamus and the amygdalae stand out as separating regions, which seems to correlate well with earlier findings based on longitudinal studies.
more »
« less
- Award ID(s):
- 1951446
- PAR ID:
- 10306307
- Publisher / Repository:
- Springer Science + Business Media
- Date Published:
- Journal Name:
- Brain Topography
- Volume:
- 34
- Issue:
- 6
- ISSN:
- 0896-0267
- Page Range / eLocation ID:
- p. 840-862
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract A search for leptoquark pair production decaying into$$te^- \bar{t}e^+$$ or$$t\mu ^- \bar{t}\mu ^+$$ in final states with multiple leptons is presented. The search is based on a dataset ofppcollisions at$$\sqrt{s}=13~\text {TeV} $$ recorded with the ATLAS detector during Run 2 of the Large Hadron Collider, corresponding to an integrated luminosity of 139 fb$$^{-1}$$ . Four signal regions, with the requirement of at least three light leptons (electron or muon) and at least two jets out of which at least one jet is identified as coming from ab-hadron, are considered based on the number of leptons of a given flavour. The main background processes are estimated using dedicated control regions in a simultaneous fit with the signal regions to data. No excess above the Standard Model background prediction is observed and 95% confidence level limits on the production cross section times branching ratio are derived as a function of the leptoquark mass. Under the assumption of exclusive decays into$$te^{-}$$ ($$t\mu ^{-}$$ ), the corresponding lower limit on the scalar mixed-generation leptoquark mass$$m_{\textrm{LQ}_{\textrm{mix}}^{\textrm{d}}}$$ is at 1.58 (1.59) TeV and on the vector leptoquark mass$$m_{{\tilde{U}}_1}$$ at 1.67 (1.67) TeV in the minimal coupling scenario and at 1.95 (1.95) TeV in the Yang–Mills scenario.more » « less
-
Abstract A measurement of the dijet production cross section is reported based on proton–proton collision data collected in 2016 at$$\sqrt{s}=13\,\text {Te}\hspace{-.08em}\text {V} $$ by the CMS experiment at the CERN LHC, corresponding to an integrated luminosity of up to 36.3$$\,\text {fb}^{-1}$$ . Jets are reconstructed with the anti-$$k_{\textrm{T}} $$ algorithm for distance parameters of$$R=0.4$$ and 0.8. Cross sections are measured double-differentially (2D) as a function of the largest absolute rapidity$$|y |_{\text {max}} $$ of the two jets with the highest transverse momenta$$p_{\textrm{T}}$$ and their invariant mass$$m_{1,2} $$ , and triple-differentially (3D) as a function of the rapidity separation$$y^{*} $$ , the total boost$$y_{\text {b}} $$ , and either$$m_{1,2} $$ or the average$$p_{\textrm{T}}$$ of the two jets. The cross sections are unfolded to correct for detector effects and are compared with fixed-order calculations derived at next-to-next-to-leading order in perturbative quantum chromodynamics. The impact of the measurements on the parton distribution functions and the strong coupling constant at the mass of the$${\text {Z}} $$ boson is investigated, yielding a value of$$\alpha _\textrm{S} (m_{{\text {Z}}}) =0.1179\pm 0.0019$$ .more » « less
-
A<sc>bstract</sc> Measurements of charged-particle production in pp, p–Pb, and Pb–Pb collisions in the toward, away, and transverse regions with the ALICE detector are discussed. These regions are defined event-by-event relative to the azimuthal direction of the charged trigger particle, which is the reconstructed particle with the largest transverse momentum$$ \left({p}_{\textrm{T}}^{\textrm{trig}}\right) $$ in the range 8<$$ {p}_{\textrm{T}}^{\textrm{trig}} $$ <15 GeV/c. The toward and away regions contain the primary and recoil jets, respectively; both regions are accompanied by the underlying event (UE). In contrast, the transverse region perpendicular to the direction of the trigger particle is dominated by the so-called UE dynamics, and includes also contributions from initial- and final-state radiation. The relative transverse activity classifier,$$ {R}_{\textrm{T}}={N}_{\textrm{ch}}^{\textrm{T}}/\left\langle {N}_{\textrm{ch}}^{\textrm{T}}\right\rangle $$ , is used to group events according to their UE activity, where$$ {N}_{\textrm{ch}}^{\textrm{T}} $$ is the charged-particle multiplicity per event in the transverse region and$$ \left\langle {N}_{\textrm{ch}}^{\textrm{T}}\right\rangle $$ is the mean value over the whole analysed sample. The energy dependence of theRTdistributions in pp collisions at$$ \sqrt{s} $$ = 2.76, 5.02, 7, and 13 TeV is reported, exploring the Koba-Nielsen-Olesen (KNO) scaling properties of the multiplicity distributions. The first measurements of charged-particlepTspectra as a function ofRTin the three azimuthal regions in pp, p–Pb, and Pb–Pb collisions at$$ \sqrt{s_{\textrm{NN}}} $$ = 5.02 TeV are also reported. Data are compared with predictions obtained from the event generators PYTHIA 8 and EPOS LHC. This set of measurements is expected to contribute to the understanding of the origin of collective-like effects in small collision systems (pp and p–Pb).more » « less
-
Abstract We compute the supports of the perverse cohomology sheaves of the Hitchin fibration for {\mathrm{GL}_{n}}over the locus of reduced spectral curves. In contrast to the case of meromorphic Higgs fields we find additional supports at the loci of reducible spectral curves. Their contribution to the global cohomology is governed by a finite twist of Hitchin fibrations for Levi subgroups. The corresponding summands give non-trivial contributions to the cohomology of the moduli spaces for every {n\geq{2}}. A key ingredient is a restriction result for intersection cohomology sheaves that allows us to compare the fibration to the one defined over versal deformations of spectral curves.more » « less
An official website of the United States government
