dataesr/scientific-paragraphs-categorization huggingface dataset:
g. [44, 47]  for recent discussions). The approach of Delphes models the response of the calorimeters and propagation of particles in a magnetic field, while also making use of efficiencies. However for the analysis of interest here we require a modelling of the tracker system (in particular because the signal regions are defined in terms of the number of layers hit), which is not available; and also a concept of the physical extent of the calorimeters/muon system, which is also absent at present in both approaches. Hence to implement our recasting we created a lightweight and fast code that uses particle-level information, that can easily be adapted to other analyses, that we call HackAnalysis. Ultimately we expect that the analysis described here will become available in the existing frameworks and that HackAnalysis should be useful for prototyping new features, hence the facetious name.
2. 1; how we modelled the detector in sec. 2. 2; how we modelled pileup in sec. 2. 3; and details about event simulation and how they affect the missing energy calculation in sec. 2. 4. In section 3 we discuss the validation of our approach, with additional material in appendix A. In sec. 4 we reproduce the CMS exclusion for winos, and combine it with a recasting of heavy stable charged particle searches to place limits on winos up to infinite lifetime; 200 GeV winos are excluded for all proper decay lengths above 2cm, and 700 GeV winos for all proper decay lengths above 20cm. In sec. 5 we apply our code to a model with charged scalars that are only produced in pairs and decay to an electron and a neutral fermion (equivalent to a right-handed slepton in supersymmetric models that is almost degenerate with a neutralino, such as in a co-annihilation region).
Modelling the detector Recent experimental analyses often include techniques for compensating for the imperfections of the detectors, to the point that simulating the detector response as performed e. g. in Delphes may be less accurate than using particle-level information and data-driven efficiencies. For this analysis, the most important physics objects are the charged tracks, muons, the MET and jets. Of these, the momenta/energies of the jets are relatively unimportant, in that we require only the directions; and the efficiency of the MET reconstruction has been studied  [54]. On the other hand, there is no public code to simulate the tracker response to charged particles in terms of hits and layers, and for LLP analyses, and this one in particular, we must take into account the physical dimensions of the detector: whether a charged particle decays within before leaving the muon system, for example, so that we can properly compute the MET.
There is no efficiency information given by the experiment about the reconstruction of tracks as a function of length or direction. Moreover, the signal regions are defined in terms of the number of pixel/tracker layers traversed. This means that to accurately recast the analysis the code must have some model of the position of the layers. Moreover, by using a probability of a layer hit being registered we can model the possibility of missing inner/middle hits, as well as "fake" outer hits, and this appears to be important for accurately reproducing the signal efficiencies, which are otherwise too high; we use a fixed per-hit efficiency of 94. 5%  [57]  and clearly this has a significant impact on reducing the number of tracks observed, because we just need to miss one hit along the length of the track for it to be discounted.
Effect of pileup and track isolation In principle, pileup affects the jets, MET calculation, track isolation and calculation of E ∆R<0. 5 calo , as well as providing fake tracks. To compensate, CMS employ rather effective mitigation techniques. Moreover, simulating pileup in a simplified simulation is rather unusual, since its effects are usually absorbed into the efficiencies. However, we have no such efficiencies that apply for this analysis, except for the MET trigger. Therefore we implemented pileup events in the code HackAnalysis. This is accomplished by simulating minimum bias events in pythia, and storing the final state particles that register in the detector, and also metastable particles that leave tracks. These mainly consist of hadrons. These events are stored in a compressed text file, which can then be read into HackAnalysis (in this way we save significant processing Table  3 : Data-taking periods, integrated luminosity, background and observed events.
time). As each signal event is read in/simulated, a number, drawn from a Poisson distribution with mean equal to the observed pileup average, of these stored events is randomly selected from the minimum bias database, and added to the event, with their vertices randomly distributed along the beam axis and in time according to the same distribution as used in Delphes pileup events. In general in a simplified simulation approach there are then a number of different ways for pileup events to be combined with the signal event. In a naive detector simulation we would combine them before any jet clustering and the MET calculation was performed. However, sophisticated techniques are employed by the experimental collaborations such as Jet Vertex Tagging to remove the effects of pileup, and we would then be forced to attempt to implement some version of these without knowledge of the details.
However, since the MET cut and even more so the trigger appear on a rapidly falling part of the distribution, the small differences are amplified and the proportion of events passing the cut differ by between 10% to 30%, which leads to substantial uncertainty on the predictions. Not related to the impact of merging/matching, but to illustrate the effect of charginos escaping from the detector on the MET calculation, in figure  2  the distribution of missing energy can be seen for 700 GeV winos with proper decay lengths of 10cm and 10000cm; in the right-hand plot most charginos continue to the muon system, so the single-chargino events have large missing energy, and double-chargino events even more so (i. e. most of the double-chargino events are in the overflow bin). This underlines the sensible choice of CMS to use both p miss T and p miss,/ µ T in the cuts, and that our code is able to reproduce this effect.
The effect of merging/matching on the distribution of p T of the charginos is shown in figure  3 , for 100 GeV winos and 1100 GeV winos. As in the MET case, both distributions are smooth and there is no obvious superior choice just from examining the plots, but substantial differences between the two can be seen in particular for lighter winos, which again contribute to the uncertainty. As a result, in the validation of the code we provide more than one set of results so that the effect of matching/merging can be seen, which yields differences up to about 30% overall. for double-chargino and single-chargino events, for both CKKW-L merging and MLM matching. Left: 700 GeV winos with 10cm proper decay length; right: 700 GeV winos with 10000cm proper decay length. On the right-hand figure, nearly 80% of χ± χ∓ events lie in the first bin, since the charginos are both escaping and being classed as muons, giving almost no missing energy.
There is therefore in principle a large amount of data for validation, in fact too much to present in it entirety here. The classic standard is to compare cutflows, and we present a selection of these in appendix A, with many more available online at the address given in sec. 6. We provide a comparison of the three different matching/merging approaches in table  7 , where it can be seen that the MadGraph MLM and CKKW-L approaches give the best agreement for the chosen data point. For each point we simulated 500k events which, when split across 15 cores on a 40-core 3500 GHz cluster computer, took about an hour per point to simulate both MLM and CKKW-L events combined. In table 6 we demonstrate the (marginal) effect of the pileup on the cutflow, as discussed previously. In tables 8, 9 and 10 we compare all of the the benchmark points for one signal region, namely 2018A, for the MadGraph MLM matching (which uses reweighting rather than vetos) which we denote "HEPMC," since that is the input mode for the code HackAnalysis.
These are from 0. 2cm to 10000cm in logarithmic steps. For example, let us look at the quoted acceptances for single chargino events (which are the most important) and take the best case at short lifetimes: a 100 GeV wino. The acceptances for signal region 2018A are: Decay length (cm) Acceptance 0. 2 6. 4 × 10 -13 ± 6 × 10 The data up to decay lengths of about 10cm is therefore up to 100% uncertainty and there is little point attempting to match to better than a factor of 2 (or even at all below 1cm). Similarly at longer decay lengths a similar story plays out for this signal region (i. e. the data is not meaningful). Instead it is only useful to compare to the "best" number of layers for any point, as that is where the statistical power is greatest. Therefore to give a reasonable measure of the uncertainty from our code we compute δ ≡ i ( CM S i -i )L i i CM S i L i i ∈ {2015, 2016A, 2016B, 2017, 2018A, 2018B} (3.
Long dead winos Having validated our code by comparing with cutflows and acceptances, here we reproduce the exclusion limits for winos, which is a classic SU (2) triplet fermion having zero hypercharge and can be regarded as a Minimal Dark Matter candidate. However, since we have a recasting code, we can do more: we can also apply the constraints from other analyses. In particular, very long-lived winos can also be looked for in searches for heavy stable charged particles; there is the analysis  [67]  by ATLAS which provided extensive recasting material including efficiencies and pseudocode. Notably this formed the basis for a code on the LLPrecasting repository https://github. com/llprecasting/recastingCodes/blob/master/HSCPs/ ATLAS-SUSY-2016-32/. By treating any chargino that escapes the muon system as a stable particle, and approximating the ATLAS muon chambers as a cylinder of radius 12m and of length 46m (so |z| < 23m) we can use this analysis to constrain longer-lived charginos.
Although the ATLAS analysis has only 36. 1fb -1 it is especially powerful, since a heavy particle in the muon system is a rather striking signal, and so together these two anaylyses can provide overlapping regions of exclusion. For the CMS disappearing track analysis, we generate two charged track and one charged track events separately, and use the same data as for tables 5 and 4 supplemented by additional refinement points at intermediate lifetimes. To calculate exclusion limits we use a python code (available online) to combine the results from different data-taking periods in the same signal region (and treat the 2015/2016 data as belonging to signal region 3) to produce a CL s value, and exclude points at 95% confidence level. For the production cross-sections we use the publicly-available NLO-NLL results  [68, 69]   Table  5 : Errors for chargino-chargino events.
Top: using CKKW-L merging; Bottom: using MLM matching. For the ATLAS heavy stable charged particle analysis, we used the leading-order pythia code from the LLPrecasting repository, with the same cross-sections and a bespoke code to calculate the exclusion limits. The results are shown in figure  4. As can be seen the excluded regions overlap so that 200 GeV winos are excluded for any decay lengths (cτ ) longer than about 2cm, and 700 GeV winos for lengths longer than about 20cm. One peculiarity of the ATLAS search is that it is only sensitive to masses above around 175 GeV (since the mass measurement relies on time of flight information they placed a cut on the lowest masses). Hence in principle the disappearing track search is most sensitive for very light winos even at rather long lifetimes, and a (meta-) stable wino below about 175 GeV cannot be ruled out by these searches.
Limits on light charged scalars Having demonstrated the versatility of the code and reproduced the CMS data, here we apply it to a model with charged scalars. If we add a single charged scalar S -with hypercharge 1 (and charged under lepton number) and a neutral fermion χ0 then the most general Lagrangian is L = L SM -m 2 S ± |S -| 2 - 1 4 λ 2 |S -| 4 -λ 3 |S -| 2 |H| 2 -y RS S -e c χ0 - 1 2 m χ0 χ0 χ0 + h. c. (5. 1) Clearly this is a prototype of a bino and right-handed slepton in supersymmetry, except that the couplings λ 2 , λ 3 and y RS are undetermined. This is an excellent (if rather fine-tuned) dark matter model, and the classic LHC constraints would be dominated by pair production of S ± via a Z boson; the cross-sections are small but there should be limits from conventional monojet/monophoton (mono-X) searches.
A hackable recasting code Here we describe the code that incorporates the recast of the CMS disappearing track search, and also another version of the ATLAS heavy stable particle search, so that both can be run on the same simulated events. This code is called HackAnalysis and is available at https://github. com/llprecasting/recastingCodes/ tree/master/DisappearingTracks/CMS-EXO-19-010; it is maintained at https://goodsell. pages. in2p3. fr/hackanalysis where versions with any other analyses in future will appear. As mentioned in the introduction, HackAnalysis is not meant to be a new framework, but merely to be used for prototyping so that analyses/features can be exported e. g. to MadAnalysis. It is therefore designed to be flexible and editable to give the user complete control at every stage without obfuscation.
It has three modes of operation: (1) event generation using pythia; (2) reading LesHouchesEvent (. lhe) files (presumably from MadGraph) and showering through pythia; (3) reading hepmc2 files. In the latter two cases, either compressed (via gzip) or uncompressed files are accepted. In modes (  1 ) and (  2 ) multicore operation is possible (via pragma omp); in mode (2) this means the. lhe or. lhe. gz files should be split into one file per core (which can be performed automatically with a python script provided). As a basis for the event handling, events from pythia or hepmc are converted to a common event format which is based on a modified version of the heputils (https://gitlab. com/hepcedar/heputils) package where, in compliance with the licence, the namespace is renamed (as HEP). In addition some code is taken from mcutils and in principle smearing can be applied identically to GAMBIT although this is not actually used for these analyses.
The code HackAnalysis is very lightweight, making use as far as possible of existing libraries; this means that editing and (re)building is very fast. It therefore requires pythia  [60]  (from version 8. 303) for showering/event generation; fastjet  [56]  for jet clustering (in principle this could be slimmed down to fjcore, but it may be desirable to use the more advanced features for e. g. pileup subtraction); YODA (https://yoda. hepforge. org/) to read YAML files and handle histogramming (and the reading of HEPData efficiency tables if required, although so far no analyses rely on this feature) and hepmc2 if reading/writing that format is desired. Compilation is straightforward on a unix-based system; the user must only provide the paths for the necessary packages in the Makefile: hepmcpath=<HepMC2 i n s t a l l a t i o n d i r e c t o r y > p y t h i a 8 p a t h=<p y t h i a d i r e c t o r y > f a s t j e t p a t h=<f a s t j e t i n s t a l l a t i o n d i r e c t o r y > YODApath=<YODA i n s t a l l a t i o n d i r e c t o r y > YAMLpath=<YODA code d i r e c t o r y >/ s r c / yamlcpp The only subtlety here is the YAMLpath; the code uses the YAML reader included in YODA, but a peculiarity of that package is that the header files are not installed in the installation directory, so this path must point to the directory where the YODA code is stored.
Note that it would be straightforward to include a separate YAML reader at the expense of installing more packages. Once the code is built, a library and three executables are created, named analysePYTHIA. exe, analysePYTHIA LHE. exe and analyseHEPMC. exe, corresponding to modes (1), (  2 ) and (3) respectively. All three accept a YAML file to specify settings such as which analyses to run, the names of pythia configuration files, names of output files, whether to include pileup, etc. An example YAML file to run the program would be: --a n a l y s e s : -DT CMS -HSCP ATLAS s e t t i n g s : n e v e n t s : 50000 c o r e s : 4 C o n f i g f i l e : p y t h i a c o n f i g. c f g LHE f i l e : MGdir/ u n w e i g h t e d e v e n t s HEPMC f i l e : u n w e i g h t e d e v e n t s. hepmc. gz E f f i c i e n c y Filename : o u t p u t e f f i c i e n c i e s.
e f f Cutflow Filename : o u t p u t c u t f l o w. t x t Histogram Filename : h i s t o s. yoda I n c l u d e P i l e u p : t r u e P i l e u p Filename : m i n b i a s. t a r. gz Average P i l e u p : 2 9. 0 The output is a set of text files: an efficiency file (which contains the efficiency and uncertainty of each signal region) which resembles an SLHA format; a cutflow file (which prints the cutflows in a verbose text form); and a file of YODA histograms. Example input files, configuration files for pythia for modes (1) and (  2 ) (for MLM/CKKW-L matching/merging) and python files for reading the output and calculating exclusion limits for the included analyses are provided. Moreover, code for generating and storing a pileup event file are provided.
Conclusions We have presented an approach and a code to recast the CMS disappearing track searches using the full run 2 data, and shown that in combination with heavy stable charged particle searches a large part of the parameter space can be excluded for a wide variety of models. We have presented extensive validation material and discussion of the technical challenges, as well as some exclusions for a new model. It would now be interesting to apply the code to produce tables of efficiencies for each signal region for different classes of models (scalars with one or two tracks, vectors with one or two tracks) so that searches can be recast in a simplified models approach. Clearly, with three signal regions and six data-taking periods it is impractical to publish such tables in a paper, but they should ultimately be available online.
Acknowledgments We thank  Brian Francis  for very helpful discussions about the CMS analysis. We thank  Sabine Kraml ,  Humberto Reyes Gonzalez  and  Sophie Williamson  for collaboration on related topics;  Andre Lessa  for helpful discussions; and  Jack Araz ,  Benjamin Fuks ,  Manuel Utsch  for collaboration on LLP recasting in MadAnalysis, and  Benjamin Fuks  for comments on the draft. We thank the organisers of the LLP workshops https://longlivedparticles. web. cern. ch/,especially MDG acknowledges support from the grant "HiggsAutomator" of the  Agence Nationale de la Recherche (ANR)  ( ANR-15-CE31-0002 ).
Because surrogate gradients can be made twice differentiable, well-established, and effective second-order gradient meta-learning methods such as Model Agnostic Meta Learning (MAML) can be used. We show that SNNs meta-trained using MAML match or exceed the performance of conventional Artificial Neural Networks (ANNs) meta-trained with MAML on event-based meta-datasets. Furthermore, we demonstrate the specific advantages that accrue from meta-learning: fast learning without the requirement of high precision weights or gradients. Our results emphasize how meta-learning techniques can become instrumental for deploying neuromorphic learning technologies on real-world problems.
, 1995). Secondly, many networks use batch sizes larger than one. While networks with a batch size equal to one eventually converge  (LeCun and Bottou, 2004) , using a smaller batch size means that learning rates must be smaller as well. Smaller learning rates result in smaller weight updates which require memories or buffers that store the weights or weight updates with higher precision, and hence hardware area. Thirdly, surrogate gradient-based SNN training inherits other fundamental issues of deep learning, namely that very large datasets and a large number of iterations are necessary for convergence. The combination of the three problems stated above, i. e. correlated data samples, data inefficiency, and memory requirements hamper the successful deployment of neuromorphic hardware to solve real-world learning problems.
In this article, we demonstrate that gradient-based meta-learning on SNNs can solve these problems in practical cases with technological interest, and are particularly well suited to the constraints of neuromorphic hardware and online learning (Fig. 1 ). To do so we combine MAML, a second-order gradient-based method that optimizes the network hyperparameters, and the surrogate gradient method  (Neftci et al. , 2019). Two ingredients were key to the results of our work. First, surrogate functions used to estimate SNN gradients can be made twice differentiable, hence are suitable for second-order learning as in MAML. Second, the definition of suitable event-based datasets to demonstrate meta-learning on SNNs. While MAML had been previously applied to SNNs, prior work focused on metatraining Hebbian Spike Time Dependent Plasticity (STDP) dynamics on non-event-based datasets which do not take any advantage of the event-based nature of SNNs.
One important obstacle to meta-learning research in neuromorphic engineering is the lack of suitable datasets. Neuromorphic hardware implementing SNNs is most suitable to processing event-based datasets and loses most of its salient features when applied to static data  (Davies, 2019). The Omniglot  (Lake et al. , 2017)  and MiniImagenet  (Vinyals et al. , 2016)  datasets have been pivotal in pushing the field of meta-learning ahead. However, there exists no event-based dataset that is comparable to Omniglot or MiniImagenet where modeling dynamics are crucial to solving the problem. Taking inspiration from existing metalearning benchmarks that fuse multiple datasets  (Sun, 2019) , we define new benchmarks that consist of combinations of event-based datasets recorded using neuromorphic vision sensors.
Specific Contributions This work provides 1) a method of parameter initialization that enables neuromorphic hardware to few-shot learn new tasks; 2) a method to construct meta-datasets using data taken from the DVS neuromorphic sensor, with two examples made publicly available: Double NMNIST and Double ASL-DVS; and 3) the effectiveness of second-order metatraining of SNNs. We present this work as a stepping stone towards implementing MAML with SNNs in neuromorphic hardware for fast adaptation to streamed event-based sensor data. Meta-Learning for SNNs using Surrogate Gradients. In the first phase, an SNN or a functional simulator of a neuromorphic hardware's SNNs is meta-trained using surrogate gradient methods on a class of tasks T i stored on a computer. The goal of meta-training is to learn an initial parameter set θ * 0 such that out-of-sample tasks (e.
MAML achieves this using two nested optimization loops, one "inner" loop and one "outer" loop. The inner loop consists of a standard Stochastic Gradient Descent (SGD) update, where the gradient operations are traced for auto-differentiation  (Griewank and Walther, 2008). In the outer loop, an update is made using gradient descent on the meta-parameters. To make use of MAML, it is essential to set up the experimental framework accordingly. Define three sets of tasks: meta training T trn , meta validation T val , and meta testing T tst. Each task consists of a training dataset D trn and a validation dataset D val of the form D = {x i , t i } M i=1. Here x i denotes the input data, t i the target (label) and M is the number of target samples. In general, the datasets corresponding to different tasks can have different sizes, but we omit this in the notation to avoid clutter.