dataesr/scientific-paragraphs-categorization huggingface dataset:
JHEP08(2022)068 or computed by theory groups. Therefore it is interesting to identify classes of missing simplified models (or missing topologies) which are relevant for a given input model, but are not constrained by the database. As in previous versions, SModelS provides as output the list of the simplified models with highest cross sections which were not constrained by the database. The lack of constraints can be due to two main reasons: 1. no experimental result has considered the specific simplified model or 2. the model parameters (masses and/or widths) fall outside the range considered by the experimental result. In SModelS v2, the classification of missing topologies has changed significantly with respect to previous versions, since inclusion of displaced results in the database makes it more difficult to uniquely classify the unconstrained topologies.
The strategy adopted in SModelS v2 is to classify all experimental results into displaced or prompt results 7 and to consider the unconstrained topologies according to these types of results. They are therefore grouped according to the following coverage groups: • missing (prompt): not covered by any prompt-type results. This group corresponds to all topologies which did not match any of the simplified models constrained by prompt results in the database. • missing (displaced): not covered by any displaced-type results. This group corresponds to all topologies which did not match any of the simplified models constrained by displaced results in the database. • missing (all): not covered by any type of result. This group corresponds to all topologies which did not match any of the simplified models considered by the prompt and the displaced results in the database.
• outside the grid: this group corresponds to topologies which are matched by at least one experimental result in the database (prompt or displaced), but their parameters (masses and/or widths) fall outside the ranges considered by the results. In addition, the missing (prompt) group reweights its topology cross sections by the fraction of prompt decays, as defined in eq. (  2. 3). The missing (displaced) group, on the other hand, reweights its topologies by the fraction of displaced decays. Since the grouping defined above is somewhat arbitrary, it is possible for the user to redefine them with a few simple changes in the SModelS code, as detailed in the online manual  [24]. 7 Prompt results are all those which assumes all decays to be prompt and the last BSM particle to be stable (or decay outside the detector).
Disappearing tracks. The SModelS database now contains efficiency maps for one or two charged tracks from searches for long-lived charginos by ATLAS  [29]  (ATLAS-SUSY-2016-06, 36 fb -1 ) and CMS  [30]  (CMS-EXO-19-010, 101 fb -1 ). Since these analyses can be very sensitive to the LLP decay length, which depends on the LLP boost and consequently on its spin, the disappearing track analyses were implemented for specific spin assignments. For the ATLAS analysis, we use the efficiency maps provided by  [31, 32]  for both the fermionic (chargino) and the scalar (charged Higgs) LLP cases. For the CMS analysis, we use the efficiency maps provided by the collaboration; here only the fermion (chargino) case is available.
JHEP08(2022)068 Figure  5. SModelS constraints on long-lived charged scalars in the scotogenic model with scalar DM, with the H ± decaying either to π ± H 0 or to ± H 0. The white line denotes the exclusion limit (r max = 1) from HSCP searches, while the pink line denotes the exclusion limit from disappearing track searches; the value of r max is shown in color. The pink and white contours show the exclusion limits (r max = 1) from the disappearing track and HSCP searches, respectively. The HSCP limits are relevant for cτ H ± 2 m, corresponding to m H ± -m H 0 < m π. The sharp cut-off at the pion mass results from the rapid change in lifetime when the H ± → π ± H 0 decay becomes kinematically allowed. The strongest HSCP constraints come from the 13 TeV ATLAS analysis with 36 fb -1  [28] , the corresponding CMS analysis  [57]  in the SModelS database having lower luminosity (12.
For this analysis, however, simplified model results are available only for the chargino/neutralino hypothesis (fermionic LLPs). Since the disappearing track searches are implemented in the SModelS database for specific spin assignments, see section 3. 1, by default the CMS results are not applied to the scalar LLP scenario. This is, among other considerations, motivated by the fact that the disappearing track searches make use of hard jets originating from initial-state radiation. The picture that emerges when ignoring the spin dependence is shown in figure  6. Indeed the CMS results  [30] , when applied to the scalar case, significantly extend the excluded region for decay lengths above about 10 cm (blue contour). For cτ H ± ∼ 1 m, masses up to almost 350 GeV are excluded. Comparing the red and orange regions in JHEP08(2022)068 figure  6 -the former being the ATLAS exclusion with EMs for the fermionic case, the latter the ATLAS exclusion with EMs for the scalar case -one sees that the effect of the spin dependence is rather small, roughly of the order of 10% in the excluded mass.
The same applies to H ± H ∓ (+X soft ) production. This is called mass compression in SModelS; the behaviour is controlled by the minmassgap parameter, with minmassgap = 5 GeV being the default value. Since the cross sections are added up, the constraints typically become stronger once mass compression comes into play. There are, however, some differences between the disappearing tracks and HSCP results, which are worth explaining. For the disappearing tracks search, results only exist for the direct production of one or two LLPs. Hence, charged scalars from the decay of the neutral inert states are not taken into account except for very small mass splittings where SModelS applies the mass compression. This explains why the exclusion reach increases for m A 0 -m H ± < 5 GeV. For the HSCP search  [28] , on the other hand, the database contains EM results for all topologies occurring in the IDM/scotogenic model.
JHEP08(2022)068 However, the EMs were produced using SUSY processes and it turns out that for two of the relevant 1-step topologies (concretely THSCPM8 and THSCPM11, see figure  15  and table 3 in appendix B), the choices for the production modes and spins of the involved particles are different to the ones in the model considered here. These EMs tend to underestimate the efficiencies of the scalar case. 12 For the direct HSCP production (topologies THSCPM1b and THSCPM2b), Drell-Yan production of a scalar was used, which matches the model considered here. As a consequence, when mass compression is applied for small mass differences, one gets a slightly stronger constraint from the EM results than without mass compression. Furthermore, the database also contains the UL results provided by the ATLAS collaboration for direct HSCP production.
The higgsino LSP scenario (µ M 2 , M 1 ) can also lead to non-prompt decays, cf. the light blue and yellowish points in figure  9 , but these remain in the sub-mm regime and are thus not probed by the current LLP results. Figure  10  shows the points excluded by the LHC searches in the SModelS v2 database in the m χ± 1 versus m χ0 1 plane. The color of each excluded point denotes the most constraining analysis, that is the analysis giving the highest r-value, r max. As we can see, points in the mass-degenerate region are excluded by the ATLAS  [29]  and CMS  [30]  disappearing track searches (light and dark pink points). These points correspond to the wino LSP scenario. The compressed region, where m χ± 1 -m χ0 1 m W , is mostly tested by the CMS 16 We assume that stop parameters can always be adjusted such that m h ≈ 125 GeV without influencing the EW-ino sector.
In total, results from 62 ATLAS and CMS searches at 13 TeV are implemented in the current database, 7 of which are for LLPs. We demonstrated the physics capabilities of SModelS v2 analysing the constraints on long-lived charged scalars in the scotogenic model with either scalar or fermionic DM. In the former case, LLPs arise for sub-GeV mass splitting with the scalar DM candidate. Considering the charged scalar as the next-to-lightest Z 2 -odd state, we found that HSCP searches constrain its mass up to around 500-600 GeV for mass splittings below the pion threshold. This covers the range favoured by DM considerations for such small mass splittings. For mass splittings slightly above m π ± , resulting in decay lengths of the order of 0. 1-1 m, we found that disappearing track searches from ATLAS with 36 fb -1 (CMS with 101 fb -1 ) can exclude charged scalar masses up to around 220 (350) GeV, depending on the lifetime.
Only few points with a mixed LSP, leading to a small relic, escape this conclusion and evade also the DM direct detection bounds. The disappearing track searches, on the other hand, are sensitive to the wino LSP case with Ωh 2 10 -2. In this case, the suppressed relic abundance also leads a small effective direct detection cross section, evading the Xenon1T limit by order(s) of magnitude, to the effect that this scenario is best tested at colliders. SModelS v2 is publicly available on GitHub  [24]  and can serve the whole community for fast testing of LHC constraints for BSM models that feature a Z 2 -like symmetry. As Z 2 -like symmetries are prevalent in DM models, SModelS is also interfaced from micrOMEGAs. A lot more work is foreseen to further extend and improve the usage of simplified model results and both, the SModelS code and the database, will continue to evolve.
The probabilities for an HSCP candidate to pass the muon trigger and satisfy the loose and tight selection criteria are given as a function of its velocity, β, and pseudorapidity, η, at generator level in the auxiliary information of  [28]. We denote them by P trig , P loose and P tight , respectively. (Note that P tight = P loose P tight-promotion. ) Furthermore, for each of the two signal regions, the analysis considers four different choices for the cut on the reconstructed mass, m reco. This gives a total of eight kinematic regions for which EMs are needed for the SModelS database. We compute the probability for the reconstructed mass to lie above the respective cut by assuming m reco to be Gaussian distributed. The respective mean value and variance has been provided as a function of the true mass in the auxiliary information of  [28].
JHEP08(2022)068 If #define CLEAN is commented out in micrOMEGAs, the input and output files for SModelS (i. e. the smodels. slha file containing the mass spectrum, decay tables and cross sections, and the smodels. slha. smodelsslha file containing the SModelS results) will be kept. With standard settings, smodels. slha. smodelsslha reports all the excluding experimental results for excluded points but only the most constraining result for non-excluded points; to always have all applicable results listed, set [slha-printer] expandedOutput = True in the smodels_parameters. ini file. Other options, like testCoverage, combineSRs, etc. , can also be turned on/off via the smodels_parameters. ini file. Other output formats can be chosen via the [printer] outputType =. option. Available formats are slha, summary, python, xml and stdout; note however that slha output is always necessary for micrOMEGAs.
Acknowledgments We thank the  ATLAS  and  CMS  collaborations for the provision of a plethora of simplified model results in readily usable format on HEPData. We are particularly grateful for the large number of efficiency map results supplementing the upper limit maps, and for the full likelihoods in JSON format. Furthermore, we wish to thank  Laura Jeanty  for helpful discussions on the ATLAS HSCP analysis, and  A. Belyaev  et al. for providing dedicated efficiency maps from [31] for our purpose on Zenodo. The work of G. A. and S. K. was supported in part by the  IN2P3  master project " Théorie -BSMGA ". J. H. acknowledges support from the  DFG  via the Collaborative Research Center TRR 257 and the  F. R. S. -FNRS (Chargé de recherches). Su. K. is supported by the  Austrian Science Fund through Elise-Richter  grant project number  V592-N27.
The research of H. R. G. is funded by the  Italian PRIN  grant  20172LNEEZ. A. L. acknowledges support from  FAPESP  (project grant  2018/25225-9 ). W. W. acknowledges financial support from the  Université Grenoble Alpes  and the hospitality of the  LPSC Grenoble  for a research visit to Grenoble at the final stage of this work. H. R. G. also acknowledges the hospitality and financial support of the LPSC Grenoble trough the  BSMGA  project.
of Run 2 results in the SModelS 2. 1. 0 database Tables1 and 2present a list of all Run 2 results included in the SModelS database. The last column in each table displays which type of information is available (if any) for combining distinct signal regions within a given analysis. CMS-PAS-EXO-16-036 [57] HSCP 12. 9 CMS-PAS-SUS-16-052 [97] ISR jet + soft 35. 9 Cov. CMS-SUS-16-009 [98] 0 + jets, top tag 2. 3 CMS-SUS-16-032 [99] 2 b-or 2 c-jets 35. 9 CMS-SUS-16-033 [100] 0 + jets 35. 9 CMS-SUS-16-034 [101] CMS-SUS-16-035 [102] CMS-SUS-16-036 [103] CMS-SUS-16-037 [104] CMS-SUS-16-039 [105] CMS-SUS-16-041 [106] CMS-SUS-16-042 [107] CMS-SUS-16-043 [108] CMS-SUS-16-045 [109] CMS-SUS-16-046 [110] CMS-SUS-16-047 [111] CMS-SUS-16-049 [112] CMS-SUS-16-050 [113] CMS-SUS-16-051 [114] CMS-SUS-17-001 [115] CMS-SUS-17-003 [116] CMS-SUS-17-004 [74] 2 OSSF leptons 2 SS leptons 0 + jets 1 + jets with MJ multi-, EWino multi-+ jets 1 + jets W H(bb), EWino 2 b + 2 H(γγ) high-p T γ γ + jets, high H T 0 stop 0 + top tag 1 stop 2 stop 2 taus EWino combination 35.
Optical/FIR Data We employ deep optical and infrared observations across the COSMOS and GOODS-N fields, to serve as prior positional information for our stacking analysis. In the COSMOS field, we make use of the z ++ Y JHK sselected COSMOS2015 catalog from  Laigle et al. (2016) , which compiles data spanning UV to far-infrared wavelengths. Laigle et al. (2016)  additionally use the SEDfitting code LePhare  (Ilbert et al. 2009 ) to determine photometric redshifts, stellar masses and starformation rates for all entries in the catalog. In total, 1158 galaxies from COSMOS2015 fall within 20% of the COLDz/COSMOS primary beam sensitivity. In order to derive useful and unbiased constraints on the radio properties of star-forming galaxies via a stacking analysis, it is necessary to assess the completeness of the input sample.
This, in turn, makes it difficult to fit a more complex prescription of the radio spectrum to the available photometry. However, we may improve the sampling of our spectra by combining the available radio data across COSMOS and GOODS-N, as both fields have only three out of four frequencies in common. Given that we adopt identical mass-complete bins in both fields, we expect to trace the same underlying galaxy population, which is sup-ported by the measured radio luminosities across both fields being in good agreement (Table  3 ). In order to maximize the S/N across the combined spectrum, we re-stack in the GOODS-N field at 1. 4, 5 and 34 GHz. By not including the 10 GHz observations, we are able to use the entire 34 GHz footprint in GOODS-N for stacking, while we adopt the deeper 10 GHz constraints from COSMOS.
GOODS-N. As such, combining the two fields amalgamates radio continuum data across five frequencies within 1. 4 -34 GHz. We have verified that excluding the GOODS-N 10 GHz data does not affect our conclusions in the following sections. We first re-fit the combined radio spectra with our simple model of free-free and synchrotron emission (Equation  4 ). In the two bins with a stacked detection at 34 GHz, M -med and M -high, we determine thermal fractions of f th (34 GHz) = 0. 23 +0. 35  -0. 63 and 0. 52 +0. 18 -0. 28 , respectively. These values, while uncertain, are consistent with the thermal fractions determined for the COSMOS and GOODS-N fields individually, and are hence lower than expected from an M82-like spectrum. However, as the combined spectra span five frequencies, we can attempt to fit a more complex model to the available photometry, incorporating spectral aging.
CONCLUSIONS We have performed a multi-frequency radio stacking analysis using deep VLA observations across the wellstudied COSMOS and GOODS-N fields in order to investigate the shape of the radio spectrum of faint starforming galaxies. The deep 34 GHz observations from the COLDz survey form the foundation of this work, and are augmented by deep archival data at 1. 4, 3, 5, and 10 GHz. We construct three mass-complete bins from near-infrared selected galaxy catalogs across COSMOS and GOODS-N, and remove sources that are unlikely to be star-forming based on their optical/near-IR colors and radio emission. We stack at the known positions of the star-forming galaxies at all available frequencies, and decompose the resulting radio spectra into their free-free and synchrotron components. We detect stacked 34 GHz emission in the medium (> 10 9.
However, in all cases the stacked 34 GHz luminosities are consistent with the predicted radio luminosity from free-free emission, when assuming the star formation rates derived from optical-infrared data for the galaxies in the parent catalogs. This points towards a deficit in synchrotron emission at high frequencies (restframe 60-90 GHz), while the contribution from free-free emission is as expected. Accordingly, this implies a high thermal fraction of f th ∼ 0. 8 in this frequency range. Such a synchrotron deficit can plausibly be the result of synchrotron aging of high-energy cosmic rays. Upon combining the radio continuum data across COSMOS and GOODS-N, we fit a more complex model to the radio spectrum including a spectral break. While a precise break frequency can not be robustly ascertained, a break at rest-frame ν 15 GHz is favored.
ACKNOWLEDGEMENTS The authors would like to thank the referee for their useful suggestions that improved this paper. The authors would also like to thank  F. Owen  for sharing the 1. 4 GHz radio image of GOODS-N. The  National Radio Astronomy Observatory  is a facility of the National Science Foundation operated under cooperative agreement by  Associated Universities, Inc. H. S. B. A. and J. A. H. acknowledge support of the  VIDI research programme  with project number  639. 042. 611 , which is (partly) financed by the  Netherlands Organization for Scientific Research (NWO). D. R. acknowledges support from the  National Science Foundation  under grant Nos. AST-1614213  and  AST-1910107. D. R. also acknowledges support from the  Alexander von Humboldt Foundation  through a  Humboldt Research Fellowship for Experienced Researchers.
I. R. S. acknowledges financial support from  STFC  ( ST/T000244/1 ). M. A. acknowledges support from  FONDECYT  grant  1211951 , " CONICYT + PCI + INSTITUTO MAX PLANCK DE ASTRONOMIA MPG190030 " and "CONICYT+PCI+REDES 190194". B. M. acknowledges support from the  Collaborative Research Centre 956 , sub-project  A1 , funded by the  Deutsche Forschungsgemeinschaft (DFG)  -project ID 184018867. M. T. S. acknowledges support from a Scien-.
An important problem in molecular evolution is that of phylogenetic reconstruction, that is, given a set of sequences descending from a common ancestor, the reconstruction of the binary tree describing their evolution from the latter. State-of-the-art methods for the task, namely Maximum likelihood and Bayesian inference, have a high computational cost, which limits their usability on large datasets. Recently researchers have begun investigating deep learning approaches to the problem but so far these attempts have been limited to the reconstruction of quartet tree topologies, addressing phylogenetic reconstruction as a classification problem. We present here a radically different approach with a transformer-based network architecture that, given a multiple sequence alignment, predicts all the pairwise evolutionary distances between the sequences, which in turn allow us to accurately reconstruct the tree topology with standard distance-based algorithms.
If correctly inferred, these distances are enough to recover the tree topology with fast clustering methods such as NJ or BioNJ  (Gascuel, 1997). We deal with the two other issues by relying on self-attention, a concept recently popularized in natural language processing  (Vaswani et al. , 2017) , adapting a transformer-based architecture  (Rao et al. , 2021)  for the task. In a nutshell, self-attention builds functions over unordered sets of elements, which would be pairs of aligned sequences in the MSA in our case. The learnable pieces (Figure  2 , top right panel) define how each pair representation shares information with the others. This makes the resulting function adaptable to any number of pairs, and equivariant to their permutation-in the sense that permuting the sequences in the input MSA leads to a consistent permutation of the predicted evolutionary distances.
Protein structure prediction Finally, our work is also methodologically connected to the recent corpus of methods predicting contact between pairs of residues from MSAs, a crucial step in protein structure prediction. Indeed usually the goal of these methods is to predict distances between sites (columns in the alignment) which can be seen as a transposed version of the problem in our framework, that is predicting a distance between sequences (rows in the alignment). This corpus includes the Evoformer module of the most recent version of AlphaFold  (Jumper et al. , 2021) , which also works with a pair (of sites) representation, along with one of the full MSA, employing an attention mechanism, and the MSA transformer  (Rao et al. , 2021). Our method uses axial attention blocks  (Ho et al. , 2019)  similar to those employed in the latter.
From pair representations to phylogenies The r axial attention blocks of Phyloformer output for every pair of the MSA a tensor φ (r) (x, x ′ ) ∈ R d×L informed by all pairs in the same MSA. In the end we convert this representation into a single estimate of the evolutionary distance between x and x ′ with a position-wise fully connected layer (section 4. 5) with one output feature followed by an average over the sites. Phyloformer is trained end-to-end: the evolutionary distance estimates obtained on simulated MSAs are compared against the true evolutionary distances obtained from the corresponding phylogeny (Figure  1 , Data encoder box) using a mean squared error loss function, and the errors are backpropagated through the network to compute a gradient of this loss with respect to all trainable parameters.
Because each of the two sums can be pre-computed and re-used for every query, this simple factorization reduces both the number of operations and memory usage from O(M 2 • L • d) to O(M • L • d • t). We follow  Katharopoulos et al. (2020)  and use an ELU-based mapping  (Clevert et al. , 2016) φ(x) = x + 1, if x > 0 exp (x) if x ≤ 0, where the operation is applied entrywise, yielding φ(x) ∈ R d vectors for x ∈ R d. In our experiments, we typically use d = 64, making the linear self-attention approach more efficient for pairs (resp. sites) for MSAs with more than N = 12 sequences (resp. L = 64 sites). Consistently with the results reported in  Katharopoulos et al. (2020) , the linear self-attention strategy provided comparable prediction performances as the regular softmax approach, but sped up the training of Phyloformer by an order of magnitude.