dataesr/scientific-paragraphs-categorization huggingface dataset:
The DUNE far detector data will be dominated by single muon and electron events, where the electron event selection and reconstruction efficiencies will improve in the absence of nearby cosmic ray background activity. As a result the event selection will be further optimized for the DUNE far detector analyses. Understanding of energy resolution and its potential improvements will have important implications for electrons from ν e interactions in the DUNE far detector. Analysis of low-energy neutrino interactions in DUNE will benefit from a combination of muon-based energy calibration, Michel electron true energy-based calibration, and detailed MC modeling to characterize the energy resolution with potential energy losses. characteristics of ionization and W-values for conversion electrons in liquid argon, liquid argon-gas mixtures and liquid xenon, Nucl.
ACKNOWLEDGMENTS The ProtoDUNE-SP detector was constructed and operated on the  CERN Neutrino Platform. We thank the  CERN  management for providing the infrastructure for this experiment and gratefully acknowledge the support of the  CERN Experimental physics (EP) ,  Beams (BE) ,  Technology (TE) ,  Engineering (EN) ,  Information Technology (IT) , and  Industry, Procurement and Knowledge Transfer (IPT)   Departments for NP04/ProtoDUNE-SP. This document was prepared by the DUNE Collaboration using the resources of the  Fermi National Accelerator Laboratory (Fermilab) , a  U. S. Department of Energy, Office of Science, HEP User Facility. Fermilab  is managed by  Fermi Research Alliance, LLC (FRA) , acting under Contract No. DE-AC02-07CH11359. This work was supported by  CNPq ,  FAPERJ ,  FAPEG , and  FAPESP, Brazil ;  Canada Foundation for Innovation (CFI) ,  Institute of Particle Physics (IPP) , and  NSERC ,  Canada ; CERN; MšMT, Czech Republic; ERDF,  H2020-EU , and  MSCA ,  European Union ;  CNRS/IN2P3  and  CEA, France ;  INFN, Italy ; FCT, Portugal; NRF,  South Korea ;  Comunidad de Madrid (CAM) ,  Fundación "La Caixa ," and  MICINN, Spain ;  SERI  and  SNSF, Switzerland ;  TüBİTAK, Turkey ;  The Royal Society  and  UKRI/STFC ,  United Kingdom ;  DOE  and  NSF, USA.
Armed with a toolchain ending in a likelihood, techniques can be used that have been developed in other fields to sample the parameter space with a point density proportional to this. The physics has been completely abstracted into this function so the task can be easily separated. In its simplest guise, the search strategy is often a Markov Chain Monte Carlo (MCMC) based on the Metropolis-Hastings alogrithm; or other groups use more efficient versions based e. g on MultiNest  [26]. See e. g. the MasterCode  [27, 28]  or BayesFITS  [29]  collaborations. GAMBIT  [30, 31] , especially with the advent of the GAM-BIT Universal Machine  [32] , attempts to solve all problems for the user: the communication between tools and efficient running of them is solved by creating backends for common tools and taking over the task of interfacing between them.
Likelihoods are computed by its dedicated internal routines and combined across all observables; and the user then has the option of certain included search strategies  [33] , which range from the simple, to toy MCMC, to MultiNest and the bespoke code Diver  [33]. This is an admirable and powerful tool and best for large-scale scans on a computing cluster. However, one of the key motivations for this paper is to note that making the choice of reconstructing a likelihood is not the only choice that can be made. We will not criticise this approach in general, but to highlight occasions when an alternative can be used. To start with, one problem that arises with a global likelihood function is that for many use cases in High Energy Physics (HEP) they vary sharply around a small or narrow region in high-dimensional parameter spaces.
[37]  for a recent review, or  [38] [39] [40] [41] [42] [43] [44] [45] [46]  for applications with discussions in the context of High Energy Physics). Proposed methods typically involve training ensembles of neural networks; but also Bayesian Inference; test time data augmentation (varying the dataset, including dropout); and some single network models with components representing both the model and uncertainty (see e. g. the recent proposal  [47]  which appeared after the first version of this work). A more obvious class of network to use is a random forest classifier, where the result is determined based on the accumulating the results of a set of decision trees, and then the uncertainty can be related to the differing predictions within the set in a natural way. Such classifiers have recently been applied in the HEP context  [48, 49].
Active learning with a neural network In this section we describe our AL algorithm to train a neural network. We will assume the reader is familiar with neural networks and deep learning; they are becoming ubiquitous in science, see e. g. the reference above or  [50]  for a comprehensive "living review" of HEP applications, and  [51] [52] [53] [54] [55]  for some examples in the context of the string landscape. However, while it has become widely adopted for experimental, quantum and collider-oriented physics, there have been very few applications so far in BSM theory, with notable exceptions mentioned in the introduction, and so our presentation will be somewhat pedestrian. Our approach is based on data that can be simply categorised as "good" or "bad". This decision can be made on a given point by an "oracle" which in a simple model could just be a formula, whereas in model building applications it will be based on ranges of observables computed using given tools.
In our applications, in order to build a new class of search algorithm capable of using the latest versions of the relevant codes we shall take our classification based on simple criteria and consider a point to be "bad" if it fails the test for one observable; this has been argued to be a poor choice for global scans  [57]  but is at least unambiguous and easy to implement, and should be a good approximation for the cases we are interested in. Our first step is to create an initial dataset, typically consisting of random points, and query the oracle to get the result. We then feed these points and the oracle results to a neural network and let it train on those. We describe the parameters of the networks used in the subsequent sections, but they all consist of an input layer feeding to a series of 2 to 5 hidden layers of large (order 100) size connected by ReLU activation functions, and a final ouput layer with one neuron whose output is mapped to a sigmoid function.
By using the standard deviation and r max we make sure that the relative weight of the diversity measure and the neural network uncertainty have a weight that depends only on α. Once we have our set of K points, we pass these to the oracle, and then train the discriminator on the outputs over a given number of epochs. We also have a choice as to whether to train the discriminator on the whole dataset or just on new points. The cost of training on the full dataset is time, especially once a large number of points have been accrued; however, only training on new data, especially when those points are chosen to be near the boundary, can be deleterious. Hence we train on the full dataset every fixed number of iterations of this procedure. The reader should note that it is also necessary to balance the dataset for training the discriminator: in cases where we have many bad points and few good ones (especially initially) it is advantageous for the discriminator to just classify everything as bad.
The straight line of the demicircle is not recognized with lower numbers of random points either. For the donut and the demicircle, the algorithm also needs to train twice as often on the full dataset in order to prevent it from weighting pieces of new data too much. The diversity alpha, which ensures that new points have a certain distance from one another, is also increased for the pizza and the demicircle. This helps identify the pointy corners of these shapes. For the donut, this is not necessary as has no pointy edges. It is plausible, albeit far from proven, that similar settings adjustments may be necessary for models which exhibit holes or pointed edges in their parameter space. Nevertheless, the fact that we were able to correctly identify 7 out of 10 shapes with the exact same settings illustrates the versatility of active learning scans without the need for extensive finetuning.
Performance of the AL scan We then move to the AL and the MCMC scan with 200k points each to explore their properties. The MCMC and AL scans have ranges as provided in Table  4. The settings of the AL scan can be found in Table  3. We find that making an AL scan with many points is not completely straightforward though. Whether a network can handle a load of points depends at least in part on its number of parameters, i. e. its size. Their quantitative performance, and attempts to understand how good the dataset generated by the AL process is, are discussed in in appendix B. An additional difficulty is that the relative scarcity of good points in the ranges we want to explore: In a random scan with these ranges, solely around 3/10,000 points are good. While an MCMC would be able to find at least one good region somewhere despite this scarcity, the AL would not be able to operate on such few points because it does not work with gradients of any kind (as of now).
Therefore a random forest classifier was first trained on a random dataset and used to filter proposed points in a larger MCMC scan over the dark matter parameter space. Here we shall examine whether AL can do a better job at this task, by selecting points to train a discriminator that decides whether a given point has a Higgs mass in the range 122-128 GeV. One approach would be to attempt to learn the Higgs mass itself, using the continuous data available to us. However, the SARAH code for the MDGSSM produces a lot of null points, where the result is not a bad Higgs mass but rather that there is no valid spectrum (typically because of the presence of tachyons). This is a classic issue with complicated models that is the manifestation of the little hierarchy problem: the inputs need to be fine-tuned to some extent to be compatible with the assumptions that are forced on it (electroweak expectation value below the scale of the new particles).
AL vs. other networks In the same fashion as with the SMSQQ, we now benchmark our AL scan against various neural networks and RFCs. The results are shown in Table  7. Regularly testing on a dataset of 20k random points, we find that the error rate drops below 5% after around 24k not-null points, i. e. after adding about 7 sets of K new points to the initial dataset of 20k points. This is a substantially better error rate than the one we get with the SMSQQ. We should not overinterpret this, however, because the distributions of good and bad points are vastly different from those in the SMSQQ. Note that the AL scan that was retrained on another 24k points scored slightly more than 5% , which is due to statistics. This tells us that there is some uncertainty to all values shown in this table.
Nevertheless, they give us an idea of where we are going with this. As expected, the AL trained on 58k points outperforms the one that trained on 24k points only. This means that new interesting points add value to the overall performance of the network. The neural networks trained on the AL-generated points do similarly well as the AL networks. In the case of the 58k training points, the NN slightly outperforms the corresponding AL network. This highlights the importance of training the AL on its full dataset from time to time; this was not done with this model due to the fact that it requires further finetuning of the network to ensure stability in subsequent training rounds. Regularly retraining the AL network on its full dataset therefore is, as of now, only recommended for simpler models -we did this for the toy models in Sect.
In comparison to RFCs, AL scans vastly outperform in terms of accuracy. Vanilla neural networks can sometimes slightly outperform AL networks when trained on the same points (see appendix B and Sect. 6. 2); however, such NNs cannot generate interesting points on their own. To combine the advantages of the two, one could retrain the AL network on its full dataset every so often. At this point in time, this is only feasible for small or simple models at this point, though, because retraining on larger models requires more finetuning of the network parameters. We also find that regular NNs sometimes fail to train with a large amount of training points (see appendix B). This might be due to suboptimal settings to train with such large datasets, or it could be that the network is fundamentally not able to cope with such many points at once.
If it is a matter of network parameters, an autotuning AL scan might fix this problem in the future. One last drawback of AL scans is that we need a sufficiently large amount of good points in order to start training(see Sect. 5. 2. This could be reduced in the future by introducing gradients to the selection of K from L training points. This, too, is left for future work. Finally, the code for this work will be released as part of a general framework for running simple scans along with a future publication. At that point it would also be interesting to consider more sophisticated deep learning and AL approaches such as those in the swyft library  [87] , and whether they can be used to improve the performance of tasks that we are interested in here. Similarly, other ML techniques could be employed to select parameter spaces, such as autotuning of the parmaters, or using a GAN  [88, 89] , where two neural networks try to fool one another with increasingly interesting points.
As is often the case in this area of research, it is difficult to prove the one or the other, though. The AL network does fine, however, by getting the points in a piecemeal fashion, which gives it the chance to adjust its weights gradually. As expected, the NN trained on 50k random points does consistently worse than that trained on 50k AL-generated points because the quality of AL-generated points is higher Table  8  Benchmark comparison of RFC, AL, and various neural networks (NN). Each classifier was trained on one set of either 50k or 200k points which are either AL-generated or random. The 50k points that serve the AL as an initial dataset already lead to a very good result; therefore the active learning and the simple neural network are identical in this case. The neural network fails to train on 200k points; it stays at a 50% error throughout the process.
Acknowledgements MDG acknowledges support from the  grants "HiggsAutomator" and "DMwithLLPatLHC" of the Agence Nationale de la Recherche (ANR)  ( ANR-15-CE31-0002 ), ( ANR-21-CE31-0013 ). We thank  Humberto Reyes  and  Sabine Kraml  for collaboration on related topics and helpful discussions.
Acknowledgments We thank  Stéphane Deny  for sharing the data and  Tobias Kühn  for useful discussions. This work was supported by the  Agence Nationale de la Recherche  ( ANR-21-CE37-0024   NatNetNoise ,  ANR-20-CE37-0018-04 ShootingStar ,  ANR-18-CE37-0011   DECORE  and  ANR-22-CE37-0016-02 PerBaCo ), by  LabEx LIFE-SENSES  ( ANR-10-LABX-65 ), by  IHU FOReSIGHT  ( ANR-18-IAHU-01 ), by  Sorbonne Université  with the.
82 ± 0. 10) atoms/(t•yr), a factor of 8. 4 lower than the initially estimated value. This reduces the previously-dominant intrinsic background contribution from  137  Xe to a level similar to the 8 B neutrino background via ν-e -scattering. This increased importance of the formerly subdominant 8 B background calls for a revision of its initially simplified calculation. The neutrino flux spectrum is now convolved with the energy-dependent electron neutrino survival probability P ee (E ν ), according to the MSW-LMA solution [2]. Accordingly, Table  3  and Figures 6, 7 and 8 of the initial manuscript are corrected. The DARWIN sensitivity to the 0νββ decay of  136  Xe is recalculated with the updated background rates. The figureof-merit estimator (section 6. 1 of the original manuscript) projects a half-life sensitivity at 90% confidence level (C.
ACKNOLEDGMENTS We thank  Andrea Puglisi  and  Andrea Gnoli  for their invaluable help in setting up this project and for their comments on the manuscript. We also thank  Marianne Impéror-Clerc ,  Laura Filion ,  Anuradha Jagannathan  and  Franscesco Sciortino  for carefully reading and commenting on our paper and  Stéphane Cabaret  for the design of the quasi-2D cell. This work has been done with the support of  Investissements d'Avenir of LabEx PALM  (Grant No. ANR-10-LABX-0039-PALM ) and of the  Agence Nationale de la Recherche (ANR) , grant  ANR-18-CE09-0025.
2008; Alston et al. 2015). The much longer periods involved in AGNs make it difficult to unequivocally measure QPOs in their light curves; thus, an uninterrupted sampling over several periods is needed in order to obtain a statistically significant measurement. Over the last decade, the Fermi-LAT instrument  (Atwood et al. 2009 ) has revolutionized our view of the γ-ray sky in the 100 MeV-100 GeV band thanks to its unprecedented sensitivity and all-sky monitoring observing strategy. Fourteen years after its launch, we have now access to continuous light curve data on hundreds of AGNs. In the GeV band, the extra-galactic sky is dominated by a peculiar AGN class: blazars, which are AGNs whose relativistic jet is closely aligned with our line of sight. The relativistic Doppler effect boosts the emission and makes these type of AGNs among the brightest sources in the Universe.
Fermi-LAT data analysis The Large Area Telescope (LAT) on board the Fermi Gammaray Space Telescope is a pair conversion telescope sensitive to γ-rays in the energy band from 20 MeV to 500 GeV. Since its launch on June 11, 2008, Fermi-LAT scans the sky every ∼3 h, regularly monitoring the γ-ray emission from different sources. More details on the Fermi-LAT instrument are described in  Atwood et al. (2009)  and references therein. In the current study, γ-ray data from the observation of the considered sources from August 8, 2008 to April 4, 2021 (from MJD 54686 to MJD 59308) were downloaded and analyzed using the standard analysis procedure provided by the Fermi-LAT Collaboration. The data for each of the considered sources are analyzed in an identical manner. The Pass 8 data in the energy range from 100 MeV to 500 GeV are analyzed using Fermi ScienceTools (1.
Continuous wavelet transform In this work we make use of the continuous wavelet transform (CWT) technique, which is the convolution of a time series with a dilated and translated wavelet function, to analyze the time frequency properties of the light curves. Acting as a band-pass filter, the CWT maps the power of any particular periodic behavior at different times in the time-frequency space. This is notably useful, since the CWT technique not only gives access to the frequencies of potential QPOs, but also when the periodicities appear and end and how they evolve in time. We used the Python implementation PyCWT provided by  Torrence & Compo (1998)  and as mother wavelet the Morlet wavelet. This wavelet consists of a plane wave modulated by a Gaussian and is shown in Appendix A. The wavelet power spectrum is defined as the square of the amplitude of the wavelet coefficient.
A86, page 6 of 31 Ren, H. X. , et al. :  A&A 672 ,  A86  (2023) Acknowledgements. The authors wish to thank  Jonathan Biteau  and  Pablo Peñil  for fruitful inputs and discussions, and  Josep Maria Paredes  and  Bruno Khélifi  for comments on the early draft, as well as the anonymous referee for their constructive comments that significantly improved the manuscript. This work has been possible thanks to the open source code for the simulation of artificial light curves provided by Emmanoulopoulos et al. (2013), its python version provided by Connolly (2015) and the open source Python library PyCWT elaborated by  Torrence & Compo  (1998). M. C. has received financial support through the  Postdoctoral Junior Leader Fellowship Programme  from  la Caixa Banking Foundation , grant No. LCF/BQ/LI18/11630012.
All the mean values o i and standard deviations σ i used to implement the Gaussian filters are detailed in Table II. We discuss now how these quantities were determined. For the pion and kaon masses, these values are not necessarily straightforward to choose since the neutral pion (kaon) and the charged pion (kaon) do not have the same mass. According to the Particle Data Group (PDG), the experimental masses are  [51] m π 0 = 134. 9768(5) MeV, m π ± = 139. 57039(18) MeV, ( 19 ) m K 0 = 497. 611(13) MeV, m K ± = 493. 677(16) MeV. ( 20 ) Since the experimental uncertainties are very small, we can neglect them compared to systematic uncertainties arising from the various approximations made within the model. However, since we assume m u,0 = m d,0 and hence all our results are isospin independent, there is a problem of identification of the NJL pion/kaon with the real life hadrons.
The difficulty lies in the estimation of the systematic uncertainty of our formalism for the construction of baryons, that relies on several nested approximations (NJL model, mean field approach, quark-diquark reduction of the three-body equation, and static approximation). To be on the conservative side we choose to fix the nucleon mass uncertainty with σ m N = 50 × (m nm p ) ≈ 60 MeV, and scale this uncertainty to the other octet baryons with the same method, as we did for η mesons in Eq. (24). In the case of the baryon, the PDG database reports a Breit-Wigner mass m = 1232 ± 2 MeV and a full width = 117 ± 3 MeV. The masses of the individual ++ , + , 0 , and -resonances are not known very precisely. Since the decay of the baryon is not predicted within our model, we suggest that the width of the resonance is a good indicator of the scale of precision that cannot be accounted for by the NJL model, such that we choose σ m =.
ACKNOWLEDGMENTS This study is part of a project that has received funding from the European Union's Horizon 2020 research and innovation program under Grant Agreement 2020 No. 824093. The work of J. M. T. -R. is supported by Project No. PID2020-118758GB-I00, financed by the Spanish MCIN/AEI/10. 13039/501100011033/, and by Project No. 315477589-TRR 211 (strong-interaction matter under extreme conditions), financed by the Deutsche Forschungsgemeinschaft.
Supporting information: Additional sample characterization using Brillouin Light Scattering and description of the methodology used to extract sample parameters. I. ACKNOWLEDGEMENTS We acknowledge the support of the Agence Nationale de la Recherche (project ANR-17-CE24-0025 (TOPSKY) and of the DARPA TEE program through Grant No. MIPR HR0011831554. The authors acknowledge funding from the European Union's Horizon 2020 research and innovation program under Marie Sklodowska-Curie Grant Agreement No. 754303 and No. 860060 "Magnetism and the effect of Electric Field" (MagnEFi). J. P. G. also thanks the Laboratoire d'Excellence LANEF in Grenoble (ANR-10-LABX-0051) for its support. B. Fernandez, T. Crozes, Ph. David, E. Mossang and E. Wagner are acknowledged for their technical help. We thank Dominique Mailly who.