A Search for Neutrino Emission from Fast Radio Bursts with Six Years of IceCube Data
Abstract
We present a search for coincidence between IceCube TeV neutrinos and fast radio bursts (FRBs). During the search period from 2010 May 31 to 2016 May 12, a total of 29 FRBs with 13 unique locations have been detected in the whole sky. An unbinned maximum likelihood method was used to search for spatial and temporal coincidence between neutrinos and FRBs in expanding time windows, in both the northern and southern hemispheres. No significant correlation was found in six years of IceCube data. Therefore, we set upper limits on neutrino fluence emitted by FRBs as a function of time window duration. We set the most stringent limit obtained to date on neutrino fluence from FRBs with an energy spectrum assumed, which is 0.0021 GeV cm-2 per burst for emission timescales up to ~102 seconds from the northern hemisphere stacking search.
1 Introduction
Fast radio bursts (FRBs) are a new class of astrophysical phenomenon characterized by bright broadband radio emission lasting only a few milliseconds. Since the first FRB discovered in 2007 in archival data from the Parkes Radio Telescope (Lorimer et al., 2007), more than 20 FRBs have been detected by a total of five observatories (Spitler et al., 2014; Masui et al., 2015; Caleb et al., 2017; Bannister et al., 2017). This rules out the hypothesis of instrumental or terrestrial origin of these phenomena. The number of FRBs detected together with the duration and solid angle searched imply an all-sky FRB occurrence rate of a few thousand per day (Thornton et al., 2013; Spitler et al., 2014), which is consistent with 10% of the supernova rate (Murase et al., 2016). The burst durations suggest that FRB progenitors are very compact, with light-transit distances on the order of hundreds of kilometers. The dispersion measures – the time delay of lower frequency signal components, which is proportional to the column density of free electrons along the line of sight – of the detected FRBs are significantly greater than the Milky Way alone could provide (Cordes et al., 2016), and the majority of sources have been detected at high Galactic latitudes, indicating extragalactic origin. The distances of the FRBs extracted from their dispersion measures, however, are only upper limits and precise measurements are yet to be determined, most likely from multi-wavelength observations.
The nature of FRBs is still under heated debate, and a multitude of models have been proposed for the FRB progenitors, the majority of which involve strong magnetic fields and leptonic acceleration. Some models predict millisecond radio bursts from cataclysmic events such as dying stars (Falcke & Rezzolla, 2014), neutron star mergers (Totani, 2013), or evaporating black holes (Rees, 1977). In 2015, 16 additional bursts were detected from the direction of FRB 121102 (Spitler et al., 2014; Scholz et al., 2016), spaced out non-periodically by timescales ranging from seconds to days. This indicates that the cataclysmic scenario is not true at least for this repeating FRB. A multi-wavelength follow-up campaign identified this FRB’s host dwarf galaxy at a distance of ~1 Gpc (Chatterjee et al., 2017). It is unclear whether FRB 121102 is representative of FRBs as a source class or if repetitions are possible for only a subclass of FRBs.
While leptonic acceleration is typically the default assumption for FRB emission in most models, hadronic acceleration is also possible in the associated regions of the progenitors, which would lead to production of high-energy cosmic rays and neutrinos (Li et al., 2014). It has been proposed that cosmological FRBs could link to exotic phenomena such as oscillations of superconducting cosmic strings (Ye et al., 2017), and some authors predict that such cosmic strings could also produce ultra-high energy cosmic rays and neutrinos, from super heavy particle decays (Berezinsky et al., 2009; Lunardini & Sabancilar, 2012). Therefore, both multi-wavelength and multi-messenger follow-ups can provide crucial information to help decipher the origin of FRBs. Here, the IceCube telescope offers the opportunity to search for neutrinos correlated with FRBs.
The IceCube Neutrino Observatory consists of 5160 digital optical modules (DOMs) instrumenting one cubic kilometer of Antarctic ice from depths of 1450 m to 2450 m at the geographic South Pole (Aartsen et al. (2017e), IceCube Collaboration). Charged products of neutrino interactions in the ice create Cherenkov photons which are observed by the DOMs and allow the reconstruction of the initial neutrino energy, direction, and interaction type. Charged-current muon neutrino interactions create muons, which travel along straight paths in the ice, resulting in events with directional resolution at energies above 1 TeV (Maunu, 2016). The detector – fully installed since 2010 – collects data from the whole sky with an up-time higher than 99% per year, enabling real-time alerts to other instruments and analysis of archival data as a follow-up to interesting signals detected by other observatories.
IceCube has discovered a diffuse astrophysical neutrino flux in the TeV to PeV energy range (Aartsen et al. (2013b), IceCube Collaboration; Aartsen et al. (2013a), IceCube Collaboration; Aartsen et al. (2014), IceCube Collaboration; Aartsen et al. (2015a), IceCube Collaboration; Aartsen et al. (2015c), IceCube Collaboration; Aartsen et al. (2016a), IceCube Collaboration). The arrival directions of these neutrinos are consistent with an isotropic distribution, indicating a majority of them have originated from extragalactic sources. Although tau neutrinos are yet to be identified among the observed flux (Aartsen et al. (2016b), IceCube Collaboration), the flavor ratio is found to be consistent with : : = 1 : 1 : 1 from analyses which combined multiple data sets (Aartsen et al. (2015b), IceCube Collaboration) and with events starting inside the detector for all flavor channels. (Aartsen et al. (2015d), IceCube Collaboration; Aartsen et al. (2017d), IceCube Collaboration). Close-to-equal flavor ratio is another feature of astrophysical neutrinos which have traversed astronomical distances and hence have reached full mixing (Argüelles et al., 2015; Bustamante et al., 2015). While the astrophysical neutrino flux has been detected in multiple channels with high significance, neither clustering in space or time nor cross correlations to catalogs have been found (Aartsen et al. (2017a), IceCube Collaboration). The once promising sources for high-energy neutrinos such as gamma ray bursts (Abbasi et al. (2012), IceCube Collaboration; Aartsen et al. (2015e), IceCube Collaboration; Aartsen et al. (2017b), IceCube Collaboration) and blazars (Aartsen et al. (2017c), IceCube Collaboration) have been disfavored as the major contributors to the observed flux. To date, the origin of the astrophysical neutrinos remains a mystery.
In Fahey et al. (2017), an analysis of four FRBs with one year of IceCube data was reported. Here we present the results of a more sophisticated study in search of high-energy neutrinos from 29 FRBs using the IceCube Neutrino Observatory. The paper is structured as follows: Section 2 describes the event sample used. We then discuss the analysis method, search strategies and background modeling in Section 3. In Section 4, we present the sensitivities and discovery potentials based on the analysis method and search strategies established in Section 3. We then report the final results and their interpretation in Section 5. Finally, we conclude and discuss the future prospects for FRB follow-ups with IceCube in Section 6.
2 Event Sample
The data used in this analysis are assembled from muon neutrino candidate events selected in previous analyses in search of prompt neutrino coincidence with gamma ray bursts (GRBs) (Aartsen et al. (2015e), IceCube Collaboration; Aartsen et al. (2017b), IceCube Collaboration). It consists of ten data sets: five years of data from the northern hemisphere and five from the southern hemisphere (Table 1). Due to the effects of atmospheric muon contamination, which are strong in the south and negligible in the north, the data samples are constructed in two “hemispheres” separated at a declination of . The northern selection extends to rather than declination because there is still sufficient Earth overburden at for efficient absorption of atmospheric muons.
Northern () Data | Start date | End date | Rate (mHz) | Events | Livetime (days) | |
IC86-1 | 2011-05-13 | 2012-05-15 | 3.65 | 107,612 | 341.9 | 2.13∘ |
IC86-2 | 2012-05-15 | 2013-05-02 | 5.50 | 157,754 | 332.2 | 2.68∘ |
IC86-3 | 2013-05-02 | 2014-05-06 | 6.20 | 193,320 | 362.2 | 2.79∘ |
IC86-4 | 2014-05-06 | 2015-05-15 | 6.17 | 197,311 | 369.8 | 2.79∘ |
IC86-5 | 2015-05-15 | 2016-05-12 | 6.07 | 186,600 | 356.8 | 2.83∘ |
Southern () Data | Start date | End date | Rate (mHz) | Events | Livetime (days) | |
IC79 | 2010-05-31 | 2011-05-13 | 2.46 | 67,474 | 314.6 | 1.02∘ |
IC86-1 | 2011-05-13 | 2012-05-15 | 1.90 | 58,982 | 359.6 | 1.10∘ |
IC86-2 | 2012-05-15 | 2013-05-02 | 3.18 | 91,485 | 328.6 | 1.05∘ |
IC86-3 | 2013-05-02 | 2014-05-06 | 3.23 | 100,820 | 358.6 | 1.04∘ |
IC86-4 | 2014-05-06 | 2015-05-18 | 1.90 | 60,500 | 350.7 | 1.04∘ |
2.1 Northern data set
The northern data samples () cover five years of IceCube operation from 2011 May 13 to 2016 May 12, during which 20 northern FRBs were detected (Table 2): three each from a unique source and 17 bursts from FRB 121102. In the northern hemisphere, the Earth filters out cosmic ray-induced atmospheric muons, so the data samples consist primarily of atmospheric muon neutrinos with a median energy on the order of 1 TeV. The event rate in the northern hemisphere increases from 3.5 mHz in the first year (Aartsen et al. (2015e), IceCube Collaboration) to 6 mHz in later years (Aartsen et al. (2017b), IceCube Collaboration), as shown in Figure 1. This year-to-year variation is due largely to two combined effects: first, the initial event selections treat each year of the IceCube data sample independently due to filter and data processing scheme updates in the early years of IceCube operation; second, each data sample was separately optimized for sensitivity to its corresponding set of GRBs111In the northern data set, the IC86-1 sample was optimized for sensitivity to a stacking search for GRBs. In later years, sensitivity to a max-burst search was instead optimized, accounting for the large year-to-year rate fluctuation between samples IC86-1 and IC86-2 (see Figure 1, Table 1)..
Within each year, a seasonal variation of the background rate can also be seen (Aartsen et al. (2013c), IceCube Collaboration). In the Austral summer, the warming atmosphere expands and increases the average height and mean free path of products from cosmic-ray interactions, allowing pions to more frequently decay into 222IceCube cannot differentiate between neutrinos and anti-neutrinos, so here denotes both neutrinos and anti-neutrinos and increasing the overall rate of atmospheric muons and neutrinos in IceCube. The phase of the seasonal variation in the northern sample is the same as that in the southern sample because the northern sample is dominated by events between and in declination (Figure 2), which corresponds to production in the atmosphere at latitudes between and .
2.2 Southern data set
The southern data samples () consist of five years of data from 2010 May 31 to 2015 May 18, during which nine southern FRBs were detected. The year-to-year event rate, 2-3.5 mHz, is lower than that of the northern samples due mainly to a higher energy threshold imposed to reduce background from atmospheric muons and the asymmetric separation of hemispheres which makes the northern hemisphere ~20% larger in solid angle than the southern (Aartsen et al. (2017b), IceCube Collaboration). The southern samples are dominated by down-going atmospheric muons with median energy on the order of 10 TeV. The effective area of IceCube to neutrino events which pass the event selection can be seen in Figure 3, where the effective area has been determined for the declination of each FRB in this analysis.




Northern () FRBs | Time (UTC) | Duration (ms) | RA | Dec | IceCube Data Sample |
FRB 110523 | 2011-05-23 15:06:19.738 | 1.73 | 21h 45′ | -00∘ 12′ | IC86-1 |
FRB 110703 | 2011-07-03 18:59:40.591 | 23h 30′ | -02∘ 52′ | IC86-1 | |
FRB 121102 b0 | 2012-11-02 06:47:17.117 | 3.3 | 05h 32′ | 33∘ 05′ | IC86-2 |
FRB 130628 | 2013-06-28 03:58:00.02 | 09h 03′ | 03∘ 26′ | IC86-3 | |
FRB 121102 b1 | 2015-05-17 17:42:08.712 | 3.8 | 05h 32′ | 33∘ 05′ | IC86-4 |
FRB 121102 b2 | 2015-05-17 17:51:40.921 | 3.3 | 05h 32′ | 33∘ 05′ | IC86-4 |
FRB 121102 b3 | 2015-06-02 16:38:07.575 | 4.6 | 05h 32′ | 33∘ 05′ | IC86-5 |
FRB 121102 b4 | 2015-06-02 16:47:36.484 | 8.7 | 05h 32′ | 33∘ 05′ | IC86-5 |
FRB 121102 b5 | 2015-06-02 17:49:18.627 | 2.8 | 05h 32′ | 33∘ 05′ | IC86-5 |
FRB 121102 b6 | 2015-06-02 17:49:41.319 | 6.1 | 05h 32′ | 33∘ 05′ | IC86-5 |
FRB 121102 b7 | 2015-06-02 17:50:39.298 | 6.6 | 05h 32′ | 33∘ 05′ | IC86-5 |
FRB 121102 b8 | 2015-06-02 17:53:45.528 | 6.0 | 05h 32′ | 33∘ 05′ | IC86-5 |
FRB 121102 b9 | 2015-06-02 17:56:34.787 | 8.0 | 05h 32′ | 33∘ 05′ | IC86-5 |
FRB 121102 b10 | 2015-06-02 17:57:32.020 | 3.1 | 05h 32′ | 33∘ 05′ | IC86-5 |
FRB 121102 b11 | 2015-11-13 08:32:42.375 | 6.73 | 05h 32′ | 33∘ 05′ | IC86-5 |
FRB 121102 b12 | 2015-11-19 10:44:40.524 | 6.10 | 05h 32′ | 33∘ 05′ | IC86-5 |
FRB 121102 b13 | 2015-11-19 10:51:34.957 | 6.14 | 05h 32′ | 33∘ 05′ | IC86-5 |
FRB 121102 b14 | 2015-11-19 10:58:56.234 | 4.30 | 05h 32′ | 33∘ 05′ | IC86-5 |
FRB 121102 b15 | 2015-11-19 11:05:52.492 | 5.97 | 05h 32′ | 33∘ 05′ | IC86-5 |
FRB 121102 b16 | 2015-12-08 04:54:40.262 | 2.50 | 05h 32′ | 33∘ 05′ | IC86-5 |
Southern () FRBs | Time (UTC) | Duration (ms) | RA | Dec | IceCube Data Sample |
FRB 110220 | 2011-02-20 01:55:48.957 | 5.6 | 22h 34′ | -12∘ 24′ | IC79 |
FRB 110627 | 2011-06-27 21:33:17.474 | 21h 03′ | -44∘ 44′ | IC86-1 | |
FRB 120127 | 2012-01-27 08:11:21.723 | 23h 15′ | -18∘ 25′ | IC86-1 | |
FRB 121002 | 2012-10-02 13:09:18.402 | 2.1; 3.7 | 18h 14′ | -85∘ 11′ | IC86-2 |
FRB 130626 | 2013-06-26 14:56:00.06 | 16h 27′ | -07∘ 27′ | IC86-3 | |
FRB 130729 | 2013-07-29 09:01:52.64 | 13h 41′ | -05∘ 59′ | IC86-3 | |
FRB 131104 | 2013-11-04 18:04:01.2 | 06h 44′ | -51∘ 17′ | IC86-3 | |
FRB 140514 | 2014-05-14 17:14:11.06 | 2.8 | 22h 34′ | -12∘ 18′ | IC86-4 |
FRB 150418 | 2015-04-18 04:29:05.370 | 0.8 | 07h 16′ | -19∘ 00′ | IC86-4 |


3 Analysis Methods
3.1 Unbinned likelihood method
An unbinned maximum likelihood method is used to search for spatial and temporal coincidence of neutrino events with detected FRBs (Aartsen et al. (2015f), IceCube Collaboration). In a given coincidence window T centered on the time of detection of each FRB, the likelihood of observing events for an expected events is
(1) |
where and are the expected number of observed signal and background events, is the reconstructed direction and estimated angular uncertainty for each event , is the signal PDF – taken to be a radially symmetric 2D Gaussian with standard deviation – evaluated for the angular separation between event and the FRB with which it is temporally coincident, and is the background PDF for the data sample to which event belongs evaluated at the declination of event . The uncertainties of the FRB locations are taken into account in , but they are significantly smaller than the median angular uncertainty of the data. In any time window T, the events are those which IceCube detected within T of any FRB detection. Before background event rates and PDFs were calculated, on-time data – data collected within days of any FRB detection – were removed from the samples until all analysis procedures were determined. The remaining data (>1700 days of data per hemisphere) are considered off-time data, which we used to determine background characteristics to prevent artificial bias from affecting the results of our search. Figure 2 shows examples of off-time data distributions for both northern and southern hemispheres.
A generic test statistic (TS) is used in this analysis, defined as the logarithmic ratio of the likelihood of the alternative hypothesis and that of the null hypothesis , which can be written as
(2) |
The TS is maximized with respect to to find the most probable number of signal-like events among temporally coincident events. is calculated by multiplying time-dependent background rate for each FRB, modeled from off-time data, by T.
Two search strategies are implemented based on this test statistic. The stacking search tests the hypothesis that the astrophysical class of FRBs emits neutrinos. In this search, and are the total number of expected signal and background events contained in the time windows of an entire list of FRBs for the hemisphere. One TS value (with its corresponding ) is returned for an ensemble of events which consist of on-time events from all the bursts. This TS represents the significance of correlation between the events analyzed and the source class as a whole. The max-burst search tests the hypothesis that one or a few bright sources emit neutrinos regardless of source classification. In this search, and are evaluated separately for each FRB. A TS- pair is calculated for each FRB considering only the events coincident with its time window. The most statistically significant of these TS (and its corresponding ) is returned as the max-burst TS value of the ensemble.
Since neutrino emission mechanisms and potential neutrino arrival times relative to the time of radio detection are unknown, we employ a model-independent search using an expanding time window, similar to a previous search for prompt neutrino emission from gamma ray bursts by IceCube which found no correlation (Abbasi et al. (2012), IceCube Collaboration). Starting with centered on each FRB, we search a series of time windows expanding by factors of two, i.e. for . We stop expanding at a time window size of 1.94 days (167772.16 s), where the background becomes significant. For the repeating burst FRB 121102 with burst separations less than the largest time window searched, time windows of consecutive bursts stop expanding when otherwise they would overlap.
In the northern max-burst search, a bright radio burst with a flux of 7.5 Jy detected by the LOFAR radio array (Stewart et al., 2016) was included. This LOFAR burst was detected on 2011 December 24 at 04:33 UTC, near the North Celestial Pole (RA = 22h53m47.1s, DEC = +86∘2146.4) and lasted ~11 minutes. The burst was not consistent with an FRB, so it was not included in the stacking search, during which some degree of uniformity among the stacked source class was required.
3.2 Background ensembles
For each search method and hemisphere, we simulate background-only Monte Carlo pseudo-experiments for every T. This is done by first finding the seasonal variation-adjusted background rate (from Figure 1) for each FRB in the hemisphere. The product of these rates and T gives a set of mean values for the Poisson distributions from which background events will be drawn. In a single trial, the number of events in the time window of each FRB is randomly drawn, and each event is assigned spatial coordinates which are uniform in detector azimuth and have declination values drawn from the PDFs shown in Figure 2. An angular uncertainty for each event is also randomly assigned from the angular distribution of the off-time data (Maunu, 2016). The TS value for the trial is maximized with respect to and the process is repeated for trials, forming a TS distribution for the background-only hypothesis.
For example, Figure 4 shows the background-only TS distribution for the southern stacking search at . Negative TS values are rounded to zero for the purposes of calculating the significance of analysis results. Building a TS distribution in this manner implicitly factors in a trials factor for the number of bursts searched, since increasing the number of sources inflates the TS values of both the analysis result and the background-only distribution. However, there is an additional trials factor when searching in overlapping time windows, so the cross-time-window trials factor must be accounted for when calculating significance values.


For each search, the analysis procedure returns the most optimal time window and the corresponding TS- pair, as determined by the p-value of the observed TS in the background-only distribution. Post-trial p-values are obtained by investigating more ensembles of background-only trials. For each trial, a set of events is injected for the largest T following the background-only procedure described above. Then, for each T, a TS value is calculated relative to its corresponding subset of events which are randomly selected from the total event set. The most significant of these TS values has a p-value which becomes one background-only pre-trial p-value. These trials are repeated times, forming a pre-trial p-value distribution. The position of the pre-trial p-value from the search on on-time data in this distribution determines its post-trial p-value.
4 Sensitivity
The sensitivity and discovery potential are calculated by injecting signal events following an assumed unbroken power law energy spectrum (, , and ) on top of injected background events. The injected signal fluence (time integrated flux, denoted as ) is found which yields a certain probability of obtaining a certain significance in the background-only TS distribution (Neyman, 1937; Aartsen et al. (2017b), IceCube Collaboration). Specifically, sensitivity and discovery potential are defined as the minimum signal fluences required to surpass, respectively, the median in 90 of the trials and the point in 90 of the trials. Figure 5 shows the sensitivities and discovery potentials for both hemispheres and search strategies. The searches in the northern hemisphere are roughly an order of magnitude more sensitive than those in the south, because of the differences in effective area as described in Section 2.




At , we expect fewer than 0.001 background events all-hemisphere per trial in each search. As a result, the median background-only TS value is zero for all T until it becomes more probable than not that a background event is injected near an FRB location, resulting in a non-zero TS value. In general, the sensitivity remains constant in a T range that is relatively background-free and transitions to a monotonically increasing function in background-dominated T. We still search all of these low-background T because the discovery potential increases even in the small background regime (Figure 5).
As a result of our methodology, there is a point in the background transition region where the sensitivity fluence appears to improve. Where the median of the background TS distribution is zero, the 90% sensitivity threshold for signal injection remains constant. But when T is growing, there are more background events in each trial which can give rise to non-zero TS values, so the injected fluence necessary to meet the criteria for sensitivity is less. Once the median background TS value becomes non-zero, the sensitivity increases as expected.
5 Results
After correcting for trials factors induced by 25 overlapping time windows searched, no significant correlation between neutrino events and FRBs is found (nor with the LOFAR burst). The most significant pre-trial p-value () is found in the northern max-burst search at , with best-fit TS and of 3.90 and 0.99 respectively. The post-trial p-value for this search is . In the same T, the northern stacking search returned a best-fit TS and of 1.41 and 1.01 respectively, corresponding to a pre-trial p-value and post-trial p-value . The most signal-like event for both searches occurred 200.806 s after FRB 121102 b3, with an angular separation of 2.31∘ and estimated angular uncertainty of 1.31∘.
In the southern hemisphere, the max-burst search returns the most significant pre-trial p-value () at with TS and of 0.64 and 0.78, for a post-trial p-value of . In the southern stacking search, no TS value greater than zero was ever obtained for all T. Even for the largest T, where the southern max-burst search returned a positive TS value at one FRB, the order-of-magnitude increase in background for 9 FRBs stacked sufficiently diminished the significance of the events. Analysis results are summarized in Table 3, and sky maps of the events which most contributed to the results of each hemisphere are shown in Figure 6.
To set upper limits on the neutrino emission from FRBs, we use the same method which determines sensitivity, using the observed TS rather than the background-only median as a significance threshold. For most T, both the background median and analysis result TS values are zero, resulting in an upper limit equal to the sensitivity (Figure 7). The northern stacking search returned the most constraining 90% confidence level upper limit for neutrino emission from FRBs among all four searches in this analysis, per burst.
This process has been repeated for each source separately to calculate per-burst upper limits (see Table 4). fluence upper limits were determined by running background and signal-injection trials for a source list containing only one FRB, repeated for each unique source and for each year in which FRB 121102 was detected.
Northern () | best fit TS | best fit | most significant event (, ) | pre-trial (post-trial ) | optimal T | coincident FRB |
---|---|---|---|---|---|---|
max-burst test | 3.90 | 0.99 | (+200.806 s, 2.31∘) | 0.034 (0.25) | 655.36 s | FRB121102 repeater 2015/06/02 16:38:07.575 UTC |
stacking test | 1.41 | 1.01 | (+200.806 s, 2.31∘) | 0.074 (0.375) | 655.36 s | FRB121102 repeater 2015/06/02 16:38:07.575 UTC |
Southern () | best fit TS | best fit | most significant event (, ) | pre-trial (post-trial ) | optimal T | coincident FRB |
max-burst test | 0.64 | 0.78 | (-16.9 hrs, 0.20∘) | 0.412 (0.84) | 167772.16 s | FRB 140514 2014/05/14 17:14:11.06 UTC |
stacking test | 0 | 0 | – | 1.0 (1.0) | – | – |






FRB | Dec | E-2 fluence upper limit (GeV cm-2) |
FRB 121002 | -85∘ 11′ | 1.16 |
FRB 131104 | -51∘ 17′ | 1.03 |
FRB 110627 | -44∘ 44′ | 0.963 |
FRB 150418 | -19∘ 00′ | 0.331 |
FRB 120127 | -18∘ 25′ | 0.318 |
FRB 110220 | -12∘ 24′ | 0.184 |
FRB 140514 | -12∘ 18′ | 0.192 |
FRB 130626 | -07∘ 27′ | 0.153 |
FRB 130729 | -05∘ 59′ | 0.136 |
FRB 110703 | -02∘ 52′ | 0.0575 |
FRB 110523 | -00∘ 12′ | 0.0578 |
FRB 130628 | 03∘ 26′ | 0.0643 |
FRB 121102 b0 | 33∘ 05′ | 0.0932 |
FRB 121102 b1-b2 | 33∘ 05′ | 0.0925 |
FRB 121102 b3-b16 | 33∘ 05′ | 0.0919 |
LOFAR transient | 86∘ 22′ | 0.164 |




6 Conclusion and Outlook
In a search for muon neutrinos from 29 FRBs detected from 2010 May 31 to 2016 May 12, no significant correlation has been found. In both hemispheres, several events were found to be spatially coincident with some FRBs but also consistent with background.
Therefore, we set upper limits on neutrino emission from FRBs as a function of time window searched. For a energy spectrum, the most stringent limit on neutrino fluence per burst is , obtained from the shortest time window (10 ms) in the northern stacking search. This limit is much improved in comparison to a previous search with only one year of IceCube data and using a binned likelihood method (Fahey et al., 2017). The limits set in this paper are also the most constraining ones on neutrinos from FRBs for neutrino energies above 1 TeV.
At the moment, we can set even more constraining limits on high-energy neutrino emission from FRBs using IceCube’s astrophysical flux measurement (Aartsen et al. (2016a), IceCube Collaboration), assuming the current catalog of detected FRBs is representative of a homogeneous source class. Using an estimated all-sky FRB occurrence rate of 3,000 sky-1 day-1 (Macquart & Ekers, 2017), the fluence per FRB at 100 TeV cannot exceed GeV cm-2 for an emission spectrum of ; otherwise, FRBs would contribute more than the entire measured astrophysical flux. The astrophysical flux used here is extrapolated from a fit at energies of 194 TeV – 7.8 PeV, so it is only a rough estimate of the maximum neutrino emission from FRBs in the energy range this analysis concerns.


With newly operating radio observatories like CHIME (CHIME Scientific Collaboration, 2017), we expect on the order of 1,000 FRBs to be discovered quasi-isotropically each year, which will improve the sensitivity of IceCube to a follow-up stacking search by orders of magnitude (Figure 8). Future analyses using IceCube data may also benefit from a more inclusive dataset, allowing a higher overall rate of muon-like and cascade-like events in exchange for increased sensitivity at T s. Cascade-like events do not contain muons, and as a result provide an angular resolution on the order of . However, a coincident event may still provide potential for high significance in very short time windows, where background is low. Furthermore, if some sub-class of FRBs is associated with nearby supernovae, MeV-scale neutrinos can be searched in the IceCube supernova stream which looks for a sudden increase in the overall noise rate of the detector modules (Abbasi et al. (2011), IceCube Collaboration).
The ANTARES neutrino observatory is most sensitive in the southern hemisphere, where the majority of FRB sources have been detected to date. Higher FRB detection rate (due to more observation time) from the southern hemisphere also provides ANTARES the opportunities for rapid follow-up observations when FRBs are caught in real time (Petroff et al., 2017). However, we emphasize that IceCube also has excellent sensitivity in much of the southern hemisphere. In Figure 9, we provide a quantitative comparison of the effective areas of the two observatories, which can serve as a useful reference when future FRBs are detected at arbitrary declinations. At energies above 50 TeV, the effective area of IceCube to neutrinos is the highest of any neutrino observatory across the entire () sky (Figure 9). For TeV, particularly where , ANTARES complements IceCube in searches for isotropic transient sources, achieving greater effective area in of the sky. Since ANTARES is not located at a pole, the zenith angle of any astrophysical source changes throughout the day, thus detector overburden and sensitivity are time-dependent. Therefore, the effective areas provided by ANTARES for a given declination band are the day-averaged values (Adrian-Martinez et al., 2014). A joint stacking analysis between IceCube and ANTARES (Adrian-Martinez et al., 2016a, b) could maximize the sensitivity of neutrino searches from FRBs across the full sky. Furthermore, with the implementation of the expanding time window techniques, IceCube can now follow up on generic fast transients rapidly, enabling monitoring of the transient sky in the neutrino sector (Aartsen et al. (2017f), IceCube Collaboration).
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