A Search for Neutrino Point-Source Populations in 7 Years of IceCube Data with Neutrino-count Statistics

preprint2019arXivOpen access
IceCube CollaborationM. G. AartsenM. AckermannJ. AdamsJ. A. AguilarM. AhlersM. AhrensC. AlispachK. AndeenT. AndersonI. AnsseauG. AntonC. ArgüellesJ. AuffenbergS. AxaniP. BackesH. BagherpourX. BaiA. Balagopal V.A. BarbanoS. W. BarwickB. BastianV. BaumS. BaurR. BayJ. J. BeattyK. -H. BeckerJ. Becker TjusS. BenZviD. BerleyE. BernardiniD. Z. BessonG. BinderD. BindigE. BlaufussS. BlotC. BohmM. BörnerS. BöserO. BotnerJ. BöttcherE. BourbeauJ. BourbeauF. BradascioJ. BraunS. BronJ. Brostean-KaiserA. BurgmanJ. BuscherR. S. BusseT. CarverC. ChenE. CheungD. ChirkinS. ChoiK. ClarkL. ClassenA. ColemanG. H. CollinJ. M. ConradP. CoppinP. CorreaD. F. CowenR. CrossP. DaveC. De ClercqJ. J. DeLaunayH. DembinskiK. DeoskarS. De RidderP. DesiatiK. D. de VriesG. De WasseigeM. de WithT. DeYoungA. DiazJ. C. Díaz-VélezH. DujmovicM. DunkmanE. DvorakB. EberhardtT. EhrhardtP. EllerR. EngelP. A. EvensonS. FaheyA. R. FazelyJ. FeldeK. FilimonovC. FinleyA. FranckowiakE. FriedmanA. FritzT. K. GaisserJ. GallagherE. GansterS. GarrappaL. GerhardtK. GhorbaniT. GlauchT. GlüsenkampA. GoldschmidtJ. G. GonzalezD. GrantZ. GriffithS. GriswoldM. GünderM. GündüzC. HaackA. HallgrenL. HalveF. HalzenK. HansonA. HaungsD. HebeckerD. HeeremanP. HeixK. HelbingR. HellauerF. HenningsenS. HickfordJ. HignightG. C. HillK. D. HoffmanR. HoffmannT. HoinkaB. Hokanson-FasigK. HoshinaF. HuangM. HuberT. HuberK. HultqvistM. HünnefeldR. HussainS. InN. IovineA. IshiharaG. S. JaparidzeM. JeongK. JeroB. J. P. JonesF. JonskeR. JoppeD. KangW. KangA. KappesD. KappesserT. KargM. KarlA. KarleU. KatzM. KauerJ. L. KelleyA. KheirandishJ. KimT. KintscherJ. KirylukT. KittlerS. R. KleinR. KoiralaH. KolanoskiL. KöpkeC. KopperS. KopperD. J. KoskinenM. KowalskiK. KringsG. KrücklN. KulaczN. KurahashiA. KyriacouM. LabareJ. L. LanfranchiM. J. LarsonF. LauberJ. P. LazarK. LeonardA. LeszczyńskaM. LeuermannQ. R. LiuE. LohfinkC. J. Lozano MariscalL. LuF. LucarelliJ. LünemannW. LuszczakY. LyuW. Y. MaJ. MadsenG. MaggiK. B. M. MahnY. MakinoP. MallikK. MallotS. MancinaI. C. MarişR. MaruyamaK. MaseR. MaunuF. McNallyK. MeagherM. MediciA. MedinaM. MeierS. Meighen-BergerT. MenneG. MerinoT. MeuresJ. MicallefD. MocklerG. MomentéT. MontaruliR. W. MooreR. MorseM. MoulaiP. MuthR. NagaiU. NaumannG. NeerH. NiederhausenS. C. NowickiD. R. NygrenA. Obertacke PollmannM. OehlerA. OlivasA. O'MurchadhaE. O'SullivanT. PalczewskiH. PandyaD. V. PankovaN. ParkP. PeifferC. Pérez de los HerosS. PhilippenD. PielothE. PinatA. PizzutoM. PlumA. PorcelliP. B. PriceG. T. PrzybylskiC. RaabA. RaissiM. RameezL. RauchK. RawlinsI. C. ReaR. ReimannB. RelethfordM. RenschlerG. RenziE. ResconiW. RhodeM. RichmanS. RobertsonN. L. RoddM. RongenC. RottT. RuheD. RyckboschD. RysewykI. SafaB. R. SafdiS. E. Sanchez HerreraA. SandrockJ. SandroosM. SantanderS. SarkarK. SataleckaM. SchaufelH. SchielerP. SchlunderT. SchmidtA. SchneiderJ. SchneiderF. G. SchröderL. SchumacherS. SclafaniD. SeckelS. SeunarineS. ShefaliM. SilvaR. SnihurJ. SoedingreksoD. SoldinM. SongG. M. SpiczakC. SpieringJ. StachurskaM. StamatikosT. StanevR. SteinP. SteinmüllerJ. StettnerA. SteuerT. StezelbergerR. G. StokstadA. StößlN. L. StrotjohannT. StürwaldT. StuttardG. W. SullivanI. TaboadaF. TenholtS. Ter-AntonyanA. TerliukS. TilavK. TollefsonL. TomankovaC. TönnisS. ToscanoD. TosiA. TrettinM. TselengidouC. F. TungA. TurcatiR. TurcotteC. F. TurleyB. TyE. UngerM. A. Unland ElorrietaM. UsnerJ. VandenbrouckeW. Van DriesscheD. van EijkN. van EijndhovenS. VanheuleJ. van SantenM. VraegheC. WalckA. WallaceM. WallraffN. WandkowskyT. B. WatsonC. WeaverA. WeindlM. J. WeissJ. WeldertC. WendtJ. WerthebachB. J. WhelanN. WhitehornK. WiebeC. H. WiebuschL. WilleD. R. WilliamsL. WillsM. WolfJ. WoodT. R. WoodK. WoschnaggG. WredeD. L. XuX. W. XuY. XuJ. P. YanezG. YodhS. YoshidaT. YuanM. Zöcklein

Abstract

The presence of a population of point sources in a dataset modifies the underlying neutrino-count statistics from the Poisson distribution. This deviation can be exactly quantified using the non-Poissonian template fitting technique, and in this work we present the first application this approach to the IceCube high-energy neutrino dataset. Using this method, we search in 7 years of IceCube data for point-source populations correlated with the disk of the Milky Way, the Fermi bubbles, the Schlegel, Finkbeiner, and Davis dust map, or with the isotropic extragalactic sky. No evidence for such a population is found in the data using this technique, and in the absence of a signal we establish constraints on population models with source count distribution functions that can be described by a power-law with a single break. The derived limits can be interpreted in the context of many possible source classes. In order to enhance the flexibility of the results, we publish the full posterior from our analysis, which can be used to establish limits on specific population models that would contribute to the observed IceCube neutrino flux.

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