Bryan Zaldivar

We propose a novel statistical method to extend Fermi-LAT catalogues of high-latitude γ-ray sources below their nominal threshold. To do so, we rely on the determination of the differential source-count distribution of sub-threshold sources which only provides the statistical flux distribution of faint sources. By simulating ensembles of synthetic skies, we assess quantitatively the likelihood for pixels in the sky with relatively low-test statistics to be due to sources, therefore complementing the source-count distribution with spatial information. Besides being useful to orient efforts towards multi-messenger and multi-wavelength identification of new γ-ray sources, we expect the results to be especially advantageous for statistical applications such as cross-correlation analyses.

The first loading of gadolinium (Gd) into Super-Kamiokande in 2020 was successful, and the neutron capture efficiency on Gd reached 50\%. To further increase the Gd neutron capture efficiency to 75\%, 26.1 tons of was additionally loaded into Super-Kamiokande (SK) from May 31 to July 4, 2022. As the amount of loaded was doubled compared to the first loading, the capacity of the powder dissolving system was doubled. We also developed new batches of gadolinium sulfate with even further reduced radioactive impurities. In addition, a more efficient screening method was devised and implemented to evaluate these new batches of . Following the second loading, the Gd concentration in SK was measured to be ppm via an Atomic Absorption Spectrometer (AAS). From the mean neutron capture time constant of neutrons from an Am/Be calibration source, the Gd concentration was independently measured to be 332.7 6.8(sys.) 1.1(stat.) ppm, consistent with the AAS result. Furthermore, during the loading the Gd concentration was monitored continually using the capture time constant of each spallation neutron produced by cosmic-ray muons,and the final neutron capture efficiency was shown to become 1.5 times higher than that of the first loaded phase, as expected.

We present a measurement of neutrino oscillation parameters with the Super-Kamiokande detector using atmospheric neutrinos from the complete pure-water SK I–V (April 1996–July 2020) dataset, including events from an expanded fiducial volume. The dataset corresponds to 6511.3 live days and an exposure of 484.2 kiloton-years. Measurements of the neutrino oscillation parameters Δ m 3 2 2, sin 2 θ 2 3, sin 2 θ 1 3, δ CP, and the preference for the neutrino mass ordering are presented with atmospheric neutrino data alone, and with constraints on sin 2 θ 1 3 from reactor neutrino experiments. Our analysis including constraints on sin 2 θ 1 3 favors the normal mass ordering at the 92.3% level.

Among multi-messenger observations of the next galactic core-collapse supernova, Super-Kamiokande (SK) plays a critical role in detecting the emitted supernova neutrinos, determining the direction to the supernova (SN), and notifying the astronomical community of these observations in advance of the optical signal. On 2022, SK has increased the gadolinium dissolved in its water target (SK-Gd) and has achieved a Gd concentration of 0.033%, resulting in enhanced neutron detection capability, which in turn enables more accurate determination of the supernova direction. Accordingly, SK-Gd's real-time supernova monitoring system (Abe te al. 2016b) has been upgraded. SK_SN Notice, a warning system that works together with this monitoring system, was released on December 13, 2021, and is available through GCN Notices (Barthelmy et al. 2000). When the monitoring system detects an SN-like burst of events, SK_SN Notice will automatically distribute an alarm with the reconstructed direction to the supernova candidate within a few minutes. In this paper, we present a systematic study of SK-Gd's response to a simulated galactic SN. Assuming a supernova situated at 10 kpc, neutrino fluxes from six supernova models are used to characterize SK-Gd's pointing accuracy using the same tools as the online monitoring system. The pointing accuracy is found to vary from 3-7 depending on the models. However, if the supernova is closer than 10 kpc, SK_SN Notice can issue an alarm with three-degree accuracy, which will benefit follow-up observations by optical telescopes with large fields of view.

Many applications in Lattice field theory require to determine the Taylor series of observables with respect to action parameters. A primary example is the determination of electromagnetic corrections to hadronic processes. We show two possible solutions to this general problem, one based on reweigting, that can be considered a generalization of the RM123 method. The other based on the ideas of Numerical Stochastic Perturbation Theory (NSPT) in the Hamiltonian formulation. We show that 1) the NSPT-based approach shows a much reduced variance in the determination of the Taylor coefficients, and 2) That both approaches are related by a change of variables. Numerical results are shown for the case of in 4 dimensions, but we expect these observations to be general. We conclude by commenting on the possible use of Machine Learning techniques to find similar change of variables that can potentially reduce the variance in Taylor coefficients.

Preceding a core-collapse supernova, various processes produce an increasing amount of neutrinos of all flavors characterized by mounting energies from the interior of massive stars. Among them, the electron antineutrinos are potentially detectable by terrestrial neutrino experiments such as KamLAND and Super-Kamiokande via inverse beta decay interactions. Once these pre-supernova neutrinos are observed, an early warning of the upcoming core-collapse supernova can be provided. In light of this, KamLAND and Super-Kamiokande have been monitoring pre-supernova neutrinos since 2015 and 2021, respectively. Recently, we performed a joint study between KamLAND and Super-Kamiokande on pre-supernova neutrino detection. A pre-supernova alert system combining the KamLAND detector and the Super-Kamiokande detector is developed and put into operation, which can provide a supernova alert to the astrophysics community. Fully leveraging the complementary properties of these two detectors, the combined alert is expected to resolve a pre-supernova neutrino signal from a 15 M star within 510 pc of the Earth, at a significance level corresponding to a false alarm rate of no more than 1 per century. For a Betelgeuse-like model with optimistic parameters, it can provide early warnings up to 12 hours in advance.

We give an up-to-date description of the micrOMEGAs functions. Only the routines which are available for the users are described. Examples on how to use these functions can be found in the sample main programs distributed with the code.

Neutrinos from very nearby supernovae, such as Betelgeuse, are expected to generate more than ten million events over 10\,s in Super-Kamokande (SK). At such large event rates, the buffers of the SK analog-to-digital conversion board (QBEE) will overflow, causing random loss of data that is critical for understanding the dynamics of the supernova explosion mechanism. In order to solve this problem, two new DAQ modules were developed to aid in the observation of very nearby supernovae. The first of these, the SN module, is designed to save only the number of hit PMTs during a supernova burst and the second, the Veto module, prescales the high rate neutrino events to prevent the QBEE from overflowing based on information from the SN module. In the event of a very nearby supernova, these modules allow SK to reconstruct the time evolution of the neutrino event rate from beginning to end using both QBEE and SN module data. This paper presents the development and testing of these modules together with an analysis of supernova-like data generated with a flashing laser diode. We demonstrate that the Veto module successfully prevents DAQ overflows for Betelgeuse-like supernovae as well as the long-term stability of the new modules. During normal running the Veto module is found to issue DAQ vetos a few times per month resulting in a total dead time less than 1\,ms, and does not influence ordinary operations. Additionally, using simulation data we find that supernovae closer than 800~pc will trigger Veto module resulting in a prescaling of the observed neutrino data.

We report the first measurement of the atmospheric neutrino-oxygen neutral-current quasielastic (NCQE) cross section in the gadolinium-loaded Super-Kamiokande (SK) water Cherenkov detector. In June 2020, SK began a new experimental phase, named SK-Gd, by loading 0.011% by mass of gadolinium into the ultrapure water of the SK detector. The introduction of gadolinium to ultrapure water has the effect of improving the neutron-tagging efficiency. Using a 552.2 day dataset from August 2020 to June 2022, we measure the NCQE cross section to be 0.74±0.22 (stat)− 0.15+ 0.85 (syst)× 10− 38 cm 2/oxygen in the energy range from 160 MeV to 10 GeV, which is consistent with the atmospheric neutrino-flux-averaged theoretical NCQE cross section and the measurement in the SK pure-water phase within the uncertainties. Furthermore, we compare the models of the nucleon-nucleus interactions in water and …

Automatic differentiation for stochastic processes - NASA/ADS Now on home page ads icon ads Enable full ADS view NASA/ADS Automatic differentiation for stochastic processes Catumba, G. ; Ramos Martinez, A. ; Zaldivar, B. Abstract Publication: The 39th International Symposium on Lattice Field Theory Pub Date: April 2023 Bibcode: 2023slft.confE..38C full text sources Publisher | © The SAO/NASA Astrophysics Data System adshelp[at]cfa.harvard.edu The ADS is operated by the Smithsonian Astrophysical Observatory under NASA Cooperative Agreement NNX16AC86A NASA logo Smithsonian logo Resources About ADS ADS Help What's New Careers@ADS Social @adsabs ADS Blog Project Switch to full ADS Is ADS down? (or is it just me...) Smithsonian Institution Smithsonian Privacy Notice Smithsonian Terms of Use Smithsonian Astrophysical Observatory NASA …

We apply state-of-the-art, likelihood-free statistical inference (machine-learning-based) techniques for reconstructing the spectral shape of a gravitational wave background (GWB). We focus on the reconstruction of an arbitrarily shaped signal by the LISA detector, but the method can be easily extended to either template-dependent signals, or to other detectors, as long as a characterisation of the instrumental noise is available. As proof of the technique, we quantify the ability of LISA to reconstruct signals of arbitrary spectral shape (blind reconstruction), considering a diversity of frequency profiles, and including astrophysical backgrounds in some cases. As a teaser of how the method can reconstruct signals characterised by a parameter-dependent template (template reconstruction), we present a dedicated study for power-law signals. While our technique has several advantages with respect to traditional MCMC methods, we validate it with the latter for concrete cases. This work opens the door for both fast and accurate Bayesian parameter estimation of GWBs, with essentially no computational overhead during the inference step. Our set of tools will be integrated into the package GWBackFinder, which will be made publicly available in due time.

An analysis of solar neutrino data from the fourth phase of Super-Kamiokande~(SK-IV) from October 2008 to May 2018 is performed and the results are presented. The observation time of the data set of SK-IV corresponds to ~days and the total live time for all four phases is ~days. For more precise solar neutrino measurements, several improvements are applied in this analysis: lowering the data acquisition threshold in May 2015, further reduction of the spallation background using neutron clustering events, precise energy reconstruction considering the time variation of the PMT gain. The observed number of solar neutrino events in --~MeV electron kinetic energy region during SK-IV is events. Corresponding solar neutrino flux is $(2.314 \pm 0.014\, \rm{(stat.)} \pm 0.040 \, \rm{(syst.)}) \times 10^{6}~\mathrm{cm^{-2}\,s^{-1}}$, assuming a pure electron-neutrino flavor component without neutrino oscillations. The flux combined with all SK phases up to SK-IV is $(2.336 \pm 0.011\, \rm{(stat.)} \pm 0.043 \, \rm{(syst.)}) \times 10^{6}~\mathrm{cm^{-2}\,s^{-1}}$. Based on the neutrino oscillation analysis from all solar experiments, including the SK ~days data set, the best-fit neutrino oscillation parameters are and , with a deviation of about 1.5 from the parameter obtained by KamLAND. The best-fit neutrino oscillation parameters obtained from all solar experiments and KamLAND are and .

Around one-third of the point-like sources in the Fermi-LAT catalogues remain as unidentified sources (unIDs) today. Indeed, these unIDs lack a clear, univocal association with a known astrophysical source. If dark matter (DM) is composed of weakly interacting massive particles (WIMPs), there is the exciting possibility that some of these unIDs may actually be DM sources, emitting gamma-rays from WIMPs annihilation. We propose a new approach to solve the standard, machine learning (ML) binary classification problem of disentangling prospective DM sources (simulated data) from astrophysical sources (observed data) among the unIDs of the 4FGL Fermi-LAT catalogue. We artificially build two systematic features for the DM data which are originally inherent to observed data: the detection significance and the uncertainty on the spectral curvature. We do it by sampling from the observed population of unIDs …

We report a search for time variations of the solar B neutrino flux using 5,804 live days of Super-Kamiokande data collected between May 31, 1996, and May 30, 2018. Super-Kamiokande measured the precise time of each solar neutrino interaction over 22 calendar years to search for solar neutrino flux modulations with unprecedented precision. Periodic modulations are searched for in a data set comprised of five-day interval solar neutrino flux measurements with a maximum likelihood method. We also applied the Lomb-Scargle method to this data set to compare it with previous reports. The only significant modulation found is due to the elliptic orbit of the Earth around the Sun. The observed modulation is consistent with astronomical data: we measured an eccentricity of (1.530.35)\,\%, and a perihelion shift is (1.513.5)\,days.

Monte Carlo methods represent a cornerstone of computer science. They allow to sample high dimensional distribution functions in an efficient way. In this paper we consider the extension of Automatic Differentiation (AD) techniques to Monte Carlo process, addressing the problem of obtaining derivatives (and in general, the Taylor series) of expectation values. Borrowing ideas from the lattice field theory community, we examine two approaches. One is based on reweighting while the other represents an extension of the Hamiltonian approach typically used by the Hybrid Monte Carlo (HMC) and similar algorithms. We show that the Hamiltonian approach can be understood as a change of variables of the reweighting approach, resulting in much reduced variances of the coefficients of the Taylor series. This work opens the door to find other variance reduction techniques for derivatives of expectation values.

We report a novel search for the cosmic-ray boosted dark matter using the 100 tonne· day full dataset of the PandaX-II detector located at the China Jinping Underground Laboratory. With the extra energy gained from the cosmic rays, sub-GeV dark matter particles can produce visible recoil signals in the detector. The diurnal modulations in rate and energy spectrum are utilized to further enhance the signal sensitivity. Our result excludes the dark matter–nucleon elastic scattering cross section between 10− 31 and 10− 28 cm 2 for dark matter masses from 0.1 MeV/c 2 to 0.1 GeV/c 2, with a large parameter space previously unexplored by experimental collaborations.

Severe space weather produced by disturbed conditions on the Sun results in harmful effects both for humans in space and in high‐latitude flights, and for technological systems such as spacecraft or communications. Also, geomagnetically induced currents (GICs) flowing on long ground‐based conductors, such as power networks, potentially threaten critical infrastructures on Earth. The first step in developing an alarm system against GICs is to forecast them. This is a challenging task given the highly non‐linear dependencies of the response of the magnetosphere to these perturbations. In the last few years, modern machine‐learning models have shown to be very good at predicting magnetic activity indices. However, such complex models are on the one hand difficult to tune, and on the other hand they are known to bring along potentially large prediction uncertainties which are generally difficult to estimate. In …

We report the first search result for the flux of astrophysical electron antineutrinos for energies () 10 MeV in the gadolinium-loaded Super-Kamiokande (SK) detector. In 2020 June, gadolinium was introduced to the ultrapure water of the SK detector in order to detect neutrons more efficiently. In this new experimental phase, SK-Gd, we can search for electron antineutrinos via inverse beta decay with efficient background rejection thanks to the high efficiency of the neutron tagging technique. In this paper, we report the result for the initial stage of SK-Gd, during 2020 August 26, and 2022 June 1 with a 22.5× 552 kton· day exposure at 0.01% Gd mass concentration. No significant excess over the expected background in the observed events is found for the neutrino energies below 31.3 MeV. Thus, the flux upper limits are placed at the 90% confidence level. The limits and sensitivities are already comparable with the …

Super-Kamiokande (SK) is a water Cerenkov detector located in the Mozumi mine in Gifu Prefecture, Japan (see Section 2). SK is sensitive to neutrinos with energies ranging from several MeV to TeV.

We present a novel method of inferring the dark matter (DM) content and spatial distribution within galaxies, using convolutional neural networks (CNNs) trained within state-of-the-art hydrodynamical simulations (Illustris–TNG100). Within the controlled environment of the simulation, the framework we have developed is capable of inferring the DM mass distribution within galaxies of mass ∼1011– from the gravitationally baryon-dominated internal regions to the DM-rich, baryon-depleted outskirts of the galaxies, with a mean absolute error always below ≈0.25 when using photometrical and spectroscopic information. With respect to traditional methods, the one presented here also possesses the advantages of not relying on a pre-assigned shape for the DM distribution, to be applicable to galaxies not necessarily in isolation, and to perform very well even in the absence of spectroscopic observations.