dataesr/scientific-paragraphs-categorization huggingface dataset:
2018a ) and time-dependent neutrino clustering (IceCube  Collaboration et al. 2018b)  analyses suggesting this BL Lactype object as the first known source of high-energy neutrinos and the first identified extragalactic cosmic ray accelerator. Further blazar source identifications can certainly be anticipated; however, the absence of point source excesses in the ANTARES  (Albert et al. 2017b)  and IceCube  (Aartsen et al. 2017a; Albert et al. 2018a ) time-integrated datasets set strict limits on the fraction of the cosmic high-energy neutrinos that can originate in these observed sources. Possible alternative source populations include starforming galaxies, starburst galaxies, galaxy groups and clusters, supernovae, and standard and low-luminosity gamma-ray bursts (see Murase 2015 for a review).
Of these source possibilities, the transient and highlyvariable source populations will likely require timesensitive searches for identification. Hadronic models foresee that neutrinos and γ-rays are co-generated through the production and subsequent decay of mesons, mainly pions. γ-rays then result from the decay of neutral pions, while the decay of charged pions produces neutrinos. Additional processes in dense astrophysical regions can then degrade the energy of individual γ-rays to lower energies while leaving the neutrino energy spectrum almost unaffected, resulting in correlated emission of higher-energy neutrinos and lower-energy γ-rays. The present paper is organized as follows: Details of the datasets are provided in Sec. 2. Our statistical approach and signal injection studies are discussed in Sec.
DATASETS The Fermi LAT dataset is highly complementary for cross-reference with high-energy neutrino datasets. The LAT offers a 1. 4 steradian field of view, provides all sky coverage every three hours on average, and exhibits good sensitivity over the 100 MeV ∼ < ε γ ∼ < 300 GeV energy band. This analysis was performed using publicly available Fermi LAT data. The relevant Fermi data were the Pass 8 photon reconstructions available from the LAT FTP server 2. These photon events were filtered using the Fermi Science Tools, keeping only photons with a zenith angle smaller than 90 • , energies between 100 MeV and 300 GeV, detected during good time intervals (GTI) as provided in the LAT satellite files 3. The point spread function (PSF) of the LAT is given by a so-called double King function  (King 1962 ) with the parameters depending on the photon energy, conversion type, and incident angle with respect to the LAT boresight  (Ackermann et al.
2013). At energies in the hundreds of MeV, the angular uncertainty can be several degrees, especially for off-axis photons. At ε γ > 1 GeV the average uncertainty drops below 1 • , and at ε γ ∼ > 100 GeV angular uncertainties are better than 0. 1 •. The ANTARES data used spans from February 2007 to December 2015. Data from this 8. 9 year interval are divided into track and cascade events, all of which are upgoing. According to the selection criteria defined in  (Albert et al. 2017b) , during this period 7622 track and 180 cascade neutrino candidates were identified. The Fermi mission has public data available starting from 4 August 2008. The ANTARES data is coincident with weeks 9 through 396 of the Fermi data, with 6774 tracklike events and 162 cascade-like events falling within that 7. 3 year window.
For the ANTARES data, the average PSFs for tracks and cascades are derrived from Monte-Carlo simulation, and then interpolated. For track and cascacde events, the 90% containment radii for the PSFs are 1. • 5 and 10 • respectively. A healpix  (Górski et al. 2005 ) map of resolution 8 (NSide=256, mean spacing of 0. • 23) was constructed using the entire Fermi data set (weeks 9 to 495 at the time of creation) with aforementioned photon selection criteria. Using the HEASoft software 4  , events were binned into three logarithmically uniform energy bins. Each energy bin was then further binned into a healpix map, with the live time calculated via a Monte Carlo simulation. Dividing the counts map by the live time map produced the Fermi exposure map. Zero-valued (low-exposure) pixels were replaced by the average of the nearest neighbor pixels.
The Π γ B γ,i ( x) term is the product of LAT γ-ray backgrounds for each photon at the coincidence location, taken from the background maps shown in Fig 1. Together with the factorial terms, this acts like a Poisson probability of observing n γ photons from background. The p c factor, similar to the IceCube signalness  (Aartsen et al. 2017b) , is an energy proxy calculated by the ANTARES collaboration. The p c for a neutrino event is computed on an event-by-event basis using the normalised anti-cumulative distribution of the number of hits from the full ANTARES 2012-2017 neutrino dataset. This probability represents the fraction of ANTARES events with a number of hits larger than that observed for the event: the larger the number of hits, the smaller the p c value. Overall, larger values of the λ statistic suggest a greater likelihood of a physically associated multiplet from a cosmic source, rather than a coincidence of uncorrelated events.
Background Generation We generate a set of 10,000 Monte Carlo scrambled versions of each of our datasets in order to characterize their null distributions and define analysis thresholds, prior to performing any study of the unscrambled datasets. Our scrambling procedure begins by first converting the coordinates of each neutrino to detector coordinates. The arrival time and azimuthal angle of each original neutrino ν i are then exchanged with another randomly selected neutrino ν j. Each neutrino retains its original elevation. Finally, the coordinates are converted back to the equatorial system. This approach is similar to the method used in our previous work  (Turley et al. 2016) , with the primary difference being the use of detector coordinates for the scrambling procedure. Fermi LAT photons are not scrambled as the LAT data contains known sources and extensive (complex) structure.
Coincidence analysis is carried out for each scrambled dataset and λ values are calculated for the resulting ν+γ coincidences via Eq. 1. Thresholds from this analysis for false alarm rates of 1 per decade (λ D ) and 1 per century (λ C ) are presented in Table  1. In contrast to previous work  (Turley et al. 2018) , due to the sensitivity to multi-neutrino events and the use of both track and cascade events, we split the analysis into three separate parts. The first part is to detect all coincidences with single-neutrino track-like events. The second looks for coincidences with multi-neutrino track like events. The third and final part is a search for coincidences with all single-neutrino cascade-like events. Multi-neutrino cascades are not considered, as there are no cascade-like events within the temporal acceptance window of each other.
Signal Injection To estimate the sensitivity of our analysis to subthreshold populations of cosmic ν+γ emitting sources, we generate a population of signal-like events. These events are injected into the scrambled datasets so that the injected distributions can be compared to the null distribution. We determine the multiplicity of a generated signal event following the methods used in  Turley et al. (2018). This method assumes a population of sources emitting one neutrino, with associated photon fluence distributed according to N (S ≥ S 0 ) ∝ S -3/2 0. In this formulation, N (S ≥ S 0 ) is the number of events observed with a fluence greater than the threshold fluence S 0. Setting this minimum to 0. 001 photons, we can invert this relationship and generate the expectation value for the multiplicity of an arbitrary event in terms of a uniform random variable u as n γ = S 0 u -2/3.
The distribution of n γ is then calculated by drawing randomly from a Poisson distribution with the expectation value n γ. Excluding events with zero photons, this yields the following n γ distribution: 93. 8% singlet, 4. 5% doublet, 0. 9% triplet, and 0. 38%, 0. 19%, 0. 095%, 0. 0567%, 0. 0365%, 0. 0244%, and 0. 0174% for multiplicities four through ten. A signal event of photon multiplicity n γ is then generated by choosing a random right ascension and drawing a random declination from the list of all ANTARES events. These coordinates serve as a sky position for the coincidence. The PSFs for n γ LAT photons and n ν neutrinos are then centered on this point, and placed randomly according to their respective PSFs. All photons are chosen to have the same inclination angle, which is drawn from the full set of inclination angles within the Fermi dataset.
A conversion type for each photon is similarly drawn from the Fermi dataset. Photon energies are drawn from a power law with a photon index Γ = 2. Using the photon background maps, the number of unassociated photons expected to arrive within the temporal and spatial windows for that section of sky is calculated. From this Poisson probability, n b photons are randomly placed uniformly within the spatial window. Energy and conversion type for the background photons are chosen in the same manner as for the signal photons. All background photons are given the same inclination angle as the signal photons. Each particle is also given an arrival time randomly selected from a uniform distribution. Using this information, a λ value is calculated following the methods of Sec 3. Due to the it- erative rejection of one or more low-significance γ-rays, events can end up with some of the injected photons excluded.
Because the varied physical models predicting ν+γ coincidences have different characteristic timescales, we generate two sets of signal events for each of the three null distributions. One set draws the timestamps from a uniform distribution 100 s wide, while the other draws from a uniform distribution 1000 s wide. To calculate the sensitivity of our analysis, we inject an increasing number of signal events n inj and plot the median resulting Anderson-Darling p-value  (Scholz & Stephens 1987)  against n inj /n obs for the track and cascade data, as shown in Fig. 3. For the tracks, this provides an estimate of the threshold value of n inj that is needed to yield a statistically significant deviation from the null distribution (see columns n inj,1% and n inj,0. 1% in Table  1 ). For the cascades, the size of each individual scramble is small enough that replacing 100% of the dataset with signal events yields a p-value of 2.
RESULTS Applying our analysis to the two unscrambled neutrino datasets yields the results summarized in Table  1. Fig. 4  shows the λ distributions for the unscrambled data for the track and cascade data, along with the null distributions, and distributions for signal injections (where possible) yielding p-values of 1% and 0. 1%, respectively. All distributions are normalized to the number of coincidences in the unscrambled distribution. Note that due to the small size of the cascade coinci-  dence sample, it is not possible to inject enough signal events into a random scramble to differentiate from other random scrambles at better than p=2. 8% (97. 2% confidence). Two coincidences above the λ D threshold were observed in the track data. From Poisson statistics, two or more such coincidences would be observed 16.
We then measure the background value at the location of every neutrino in the track and cascade data to compute an av- erage photon background for each neutrino map. Carrying this out on the scrambled neutrino datasets yields an average background of (2. 33±0. 06)×10 -  photons deg -2 m -2 per 200 s for the track data, and (2. 16±0. 36)×10 -  photons deg -2 m -2 per 200 s for the cascade data. The observed backgrounds (in the same units) from the unscrambled data are 2. 36 × 10 -2 (+0. 44 σ; p = 33%) for the track data, and 2. 19 × 10 -2 (+0. 09 σ; p = 46%) for the cascade data. Both results are consistent with background (Fig. 5. ) The dispersion in the cascade background from scrambled datatsets is far larger than that for the tracks because of the much-reduced sample size (180 cascade events compared to 7622 track events); however, the two average backgrounds are consistent, as the mean of the track background is 0.
CONCLUSIONS We have carried out a search for ν+γ transients using publicly available Fermi LAT γ-ray data and ANTARES neutrino data. Our analysis used archival data from both observatories over the period August 2008 to December 2015. As with previous work  (Turley et al. 2018) , our analysis was designed to be capable of identifying either individual high-significance ν+γ transients or a population of individually subthreshold events, via statistical comparison to uncorrelated (scrambled) datasets. Our Monte Carlo simulations demonstrate a sensitivity to single-neutrino events of sufficient γ-ray multiplicity, as demonstrated by signal injection against multiple bright LAT-detected γ-ray bursts. Signal injection against scrambled datasets established our sensitivity to subthreshold populations of transient ν+γ sources at the >7% level (>200 coincidences) for tracks; however, due to the small sample size, we were not able to place meaningful limits on a subthreshold ν+γ source population within the cascades data.
Our limit of >200 coincidences in the full dataset is equivalent to >27 LAT-associated cosmic neutrinos per year in the ANTARES data. Since IceCube estimates of the cosmic neutrino flux and spectrum lead us to expect 6. 8 cosmic ANTARES neutrinos per year (Sec. 1), our limit is not physically constraining in this context. Analysis of the observed (unscrambled) data reveals two ν+γ coincidences above a nominal λ D threshold (false alarm rate FAR < 0. 1 yr -1 ; Table  2 ). Due to the 7. 3 year span of the data, we anticipate observing two or more λ > λ D coincidences 16. 6% of the time (p = 16. 6%). We observe no statistically-significant deviation of the observed λ distributions from their associated null distributions, with observed p-values of p = 39% and p = 60% for the track and cascade events, respectively.
(Scholz & Stephens 1987)tsNote-nν+γ is the expected number of neutrinos observed in coincidence with one or more γ-rays, as derived from 10,000 Monte Carlo scrambled realizations of each dataset. λDand λC are the thresholds above which a coincidence is observed only once per simulated decade or century, respectively. ninj,1% and n inj,0. 1% are the number of injected signal events required in simulations to give Anderson-Darling test(Scholz & Stephens 1987)p-values of p < 1% and p < 0. 1%, respectively, by comparison to the null distributions for each dataset. nν+γ is the number of neutrinos observed in coincidence with one or more γ-rays in unscrambled data, λmax is the maximum observed λ for each dataset, and pA-D is the Anderson-Darling test p-value from comparison of the observed λ distribution to the associated null distribution.
The authors thank  David Thompson  for helpful discussions. We gratefully acknowledge support from  Penn State's Office of the Senior Vice President for Research, the Eberly College of Science , and the  Penn State Institute for Gravitation  and the Cosmos. This work was supported in part by the  National Science Foundation  under Grant Number  PHY-1708146. K. M. is supported by the  Alfred P. Sloan Foundation  and by the  National Science Foundation  under Grant Number  PHY-1620777. The authors acknowledge the fi-nancial support of the funding agencies:  Centre National de la Recherche Scientifique (CNRS) ,  Commissariat  à l'énergie atomique et aux énergies alternatives (CEA),  Commission Européenne (FEDER fund and Marie Curie Program) ,  Institut Universitaire de France (IUF) ,  IdEx program  and  UnivEarthS Labex program at Sorbonne Paris Cité  ( ANR-10-LABX-0023  and  ANR-11-IDEX-0005-02 ),  Labex OCEVU  ( ANR-11-LABX-0060 ) and the A* MIDEX  project ( ANR-11-IDEX-0001-02 ),  Région Île-de-France (DIM-ACAV) ,  Région Alsace  (contrat CPER),  Région Provence-Alpes-Côte d'Azur, Département du Var and Ville de La Seyne-sur-Mer, France ;  Bundesministerium für Bildung und Forschung (BMBF) ,  Germany ;  Istituto Nazionale di Fisica Nucleare (INFN), Italy ;  Nederlandse organisatie voor Wetenschappelijk Onderzoek (NWO) , the  Netherlands; Council of the President of the Russian Federation  for young scientists and leading scientific schools supporting grants,  Russia ;  Executive Unit for Financing Higher Education, Research, Development and Innovation (UEFISCDI) ,  Romania ;  Ministerio de Economía y Competitividad (MINECO) :  Plan Estatal de Investigación  (refs.
Cells with a '-' could not be calculated, for reasons detailed in the main text. Thresholds Observed Values Dataset nν+γ λD λC n inj,1% n inj,0. 1% nν+γ λmax pA-D Tracks, 100 s 2716 ± 36 18. 5 25. 4 205 260 2734 18. 94 39% 1000 s " " " 220 285 " " " Cascades 83. 6 ± 5. 8 8. 1 14. 6 - - 80 2. 7 60% Track Multiplets 0. 48 ± 0. 69 --9. 3 - - 0 - -.
030 2000 Cascades p = 46% 1000 0 0. 015 0. 020 Average Background 0. 025 0. 030 Figure 5. Average Fermi γ-ray background rates at the positions of track (upper panel) and cascade (lower panel) neutrinos. In each panel, the histogram shows the distribu- tion obtained from 10,000 Monte-Carlo scrambled datasets, while the red line marks the observed background rate for unscrambled data. Background rates are expressed in units of photons per square meter per square degree per 200s. Ob- served average backgrounds are consistent with background for both datasets.
This provides further confirmation of the data shown in Figure  1E , demonstrating that the nanohybrids are not trapped within endocytic vesicles. The respective signals were further identified using spectral resolution of the emission associated with the three chromophores. The spectral scan of one confocal plane (in Figure  2B ) shows different locations for the nanohybrids (yellow), endosomal marker (red), and cell nuclei (blue). Spectral unmixing was also applied to a region, where the QD fluorescence staining is close to the Cy5-transferrin associated with the endosomal marker (Figure  2C ). The two stainings corresponding to the nanohybrids and Cy-5-transferrin do not share the same compartments. Clearly, these findings combined show that the nanocomposites, when internalized, are found in sub- cellular compartments that are distinct from those stained with the Cy5-transferrin.
Acknowledgements This work was supported by the  State Excellence Initiative "Nanotechnology in Medicine " from the  Free and Hanseatic City of Hamburg  and the  U. S. National Science Foundation  (grants  NSF-CHE #1508501  and # 1058957 ). J. -P. M. , H. M. and H. W. acknowledge the support of the  Chemical Industry Fund, VCI: German Chemical Industry Association  and the  German-American Fulbright Program. T. D. was supported by  Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)  and  PEW. We also thank  Goutam Palui ,  Xin Ji ,  Megan Muroski  (at  Florida State University ),  Ning Fang  ( Iowa State University ) and  John Zentmeyer  ( Nikon Corporation ) for helpful discussions. The graphical abstract image was adapted with permission from [8], copyright 2016  American Chemical Society.
In Section 3 we recall some key features of the freeze-in dark matter production mechanism, discuss cosmological constraints on our models and point out that the observation of a signal in agreement with freeze-in dark matter could falsify simple electroweak baryogenesis and leptogenesis explanations for the origin of the matter-antimatter asymmetry of the Universe. In Section 4 we turn our attention to LHC LLP searches that constrain our models, namely searches for heavy stable charged particles (HSCP), R-hadrons, displaced vertices (DV), displaced lepton searches (DL) and disappearing tracks (DT). We recast all analyses using the latest 8 TeV and 13 TeV LHC data and present projections to the end-of-lifetime HL-LHC reach. While such projections are simplistic and need to be taken with a grain of salt, they elucidate the unique capabilities of the LHC to probe a large region of the viable parameter space and how different searches can test different particle physics and cosmological scenarios.
The last three parameters {y f s }) in Eq. (2. 3) determine the interaction strength of the DM particle to the visible sector, and the freeze-in mechanism forces them to be small. Note that since we are assume no specific flavor protection mechanism, our setup naturally leads to flavor violation, both in the lepton and quark sector. These effects are naturally suppressed due to the size of the freeze-in coupling, but we will comment on them in the context of each specific model in the next subsections. In order to perform our phenomenological analysis, we have implemented the three models described by the Lagrangian of Eq. (2. 2) in the FeynRules package  [63]  and exported them in UFO  [64]  and CalcHEP  [65]  file format for use with MadGraph5 aMC@NLO  [66]  and micrOMEGAs 5  [67]. The three cases (couplings to leptons, up-and down-type quarks) have been implemented separately and the corresponding model files can be found in the FeynRules Model Database [68], or directly in  [69].
Last, but not least, one could have rare decays with invisible final states, for instance K + → π + ss; once again these diagrams would contribute proportional to (y f s ) 2 and we can safely neglect them  9. Compared to the leptonic model, electroweak precision data considerations are replaced here by the running of the strong coupling, however the corresponding bounds are rather mild and in the range of a few hundred GeVs  [80]. LHC searches for multi-jets plus missing energy are subdominant, as the jet requirements force them to be mostly prompt. Finally, we note that in this setup the LHC vector-like fermion pair production does not only proceed via s-channel gluon exchange, but also via a t-channel exchange of F fermions  10. In a nutshell, the models described here are only mildly constrained from indirect effects and direct prompt searches.
However, since the distribution for the decay position is a decreasing exponential, even for such large lifetimes there is still a significant fraction of R-hadrons which decay within the tracker. At this point it is worth noting that we compared these limits with the ones obtained from prompt multi-jet + missing energy searches corresponding to the above topology. This was done by recasting the CMS 13 TeV SUSY multi-jet + missing energy search with 35. 9 fb -1 luminosity  [130]  within the PAD (public analysis database) framework of MadAnalaysis5  [131]. The recasted analysis can be found at the link  [132]. We observed that the prompt searches are significantly weaker than the displaced vertex + MET search, thus we ignore the former in the following.
Extrapolation to High Luminosity LHC While extrapolating phenomenological analyses from LHC data to larger luminosities is in principle a well understood task (taking into account some limitations in the publicly available information) and a straightforward procedure when prompt objects are involved, searches involving LLPs are quite a different case. The main reason is that the leading backgrounds involved are instrumental (interactions of particles with detector material, cosmic muons, beam halo, cavern radiation, detector noise, etc). These cannot be accurately simulated with Monte Carlo and state-of-the art public detector simulation packages and need to be taken from data. Indeed, every beam configuration would require a new estimation of these backgrounds. It is worth noting that usually an important bottleneck for these analyses is the poor trigger efficiency.
Acknowledgments We would like to thank  Monika Blanke ,  Jared Evans ,  Enrique Fernandez-Martinez ,  Carlos Pena  and  Kalliopi Petraki  for useful discussions. We also thank the  2017 "Les Houches Physics at TeV colliders " workshop where this work started. A. L. was supported by the  Sao Paulo Research Foundation (FAPESP) , projects  2015/20570-1  and  2016/50338-6. A. G.