Curvatons are light (compared to the Hubble scale during inflation) spectator fields during inflation that potentially contribute to adiabatic curvature perturbations postinflation. They can alter CMB observables such as the spectral index
 Award ID(s):
 2014165
 NSFPAR ID:
 10376589
 Date Published:
 Journal Name:
 Journal of Cosmology and Astroparticle Physics
 Volume:
 2022
 Issue:
 05
 ISSN:
 14757516
 Page Range / eLocation ID:
 014
 Format(s):
 Medium: X
 Sponsoring Org:
 National Science Foundation
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A<sc>bstract</sc> n _{s}, the tensortoscalar ratior , and the local nonGaussianity . We systematically explore the observable space of a curvaton with a quadratic potential. We find that when the underlying inflation model does not satisfy the$$ {f}_{\textrm{NL}}^{\left(\textrm{loc}\right)} $$ ${f}_{\mathrm{NL}}^{\left(\mathrm{loc}\right)}$n _{s}andr observational constraints but can be made viable with a significant contribution from what we call a savior curvaton, a large$$ \left{f}_{\textrm{NL}}^{\left(\textrm{loc}\right)}\right $$ $\left({f}_{\mathrm{NL}}^{\left(\mathrm{loc}\right)}\right)$> 0. 05, such that the model is distinguishable from singlefield inflation, is inevitable. On the other hand, when the underlying inflation model already satisfies then _{s}andr observational constraints, so significant curvaton contribution is forbidden, a large$$ \left{f}_{\textrm{NL}}^{\left(\textrm{loc}\right)}\right $$ $\left({f}_{\mathrm{NL}}^{\left(\mathrm{loc}\right)}\right)$> 0. 05 is possible in the exceptional case when the isocurvature fluctuation in the curvaton fluid is much greater than the global curvature fluctuation. 
MCMC chains for the GWB analyses performed in the paper "The NANOGrav 15 yr Data Set: Search for Signals from New Physics".
The data is provided in pickle format. Each file contains a NumPy array with the MCMC chain (with burnin already removed), and a dictionary with the model parameters' names as keys and their priors as values. You can load them as
with open ('path/to/file.pkl', 'rb') as pick: temp = pickle.load(pick) params = temp[0] chain = temp[1]
The naming convention for the files is the following:
 igw: inflationary Gravitational Waves (GWs)
 sigw: scalarinduced GWs
 sigw_box: assumes a boxlike feature in the primordial power spectrum.
 sigw_delta: assumes a deltalike feature in the primordial power spectrum.
 sigw_gauss: assumes a Gaussian peak feature in the primordial power spectrum.
 pt: cosmological phase transitions
 pt_bubble: assumes that the dominant contribution to the GW productions comes from bubble collisions.
 pt_sound: assumes that the dominant contribution to the GW productions comes from sound waves.
 stable: stable cosmic strings
 stablec: stable strings emitting GWs only in the form of GW bursts from cusps on closed loops.
 stablek: stable strings emitting GWs only in the form of GW bursts from kinks on closed loops.
 stablem: stable strings emitting monochromatic GW at the fundamental frequency.
 stablen: stable strings described by numerical simulations including GWs from cusps and kinks.
 meta: metastable cosmic strings
 metal: metastable strings with GW emission from loops only.
 metals metastable strings with GW emission from loops and segments.
 super: cosmic superstrings.
 dw: domain walls
 dwsm: domain walls decaying into Standard Model particles.
 dwdr: domain walls decaying into dark radiation.
For each model, we provide four files. One for the run where the newphysics signal is assumed to be the only GWB source. One for the run where the newphysics signal is superimposed to the signal from Supermassive Black Hole Binaries (SMBHB), for these files "_bhb" will be appended to the model name. Then, for both these scenarios, in the "compare" folder we provide the files for the hypermodel runs that were used to derive the Bayes' factors.
In addition to chains for the stochastic models, we also provide data for the two deterministic models considered in the paper (ULDM and DM substructures). For the ULDM model, the naming convention of the files is the following (all the ULDM signals are superimposed to the SMBHB signal, see the discussion in the paper for more details)
 uldm_e: ULDM Earth signal.
 uldm_p: ULDM pulsar signal
 uldm_p_cor: correlated limit
 uldm_p_unc: uncorrelated limit
 uldm_c: ULDM combined Earth + pulsar signal direct coupling
 uldm_c_cor: correlated limit
 uldm_c_unc: uncorrelated limit
 uldm_vecB: vector ULDM coupled to the baryon number
 uldm_vecB_cor: correlated limit
 uldm_vecB_unc: uncorrelated limit
 uldm_vecBL: vector ULDM coupled to BL
 uldm_vecBL_cor: correlated limit
 uldm_vecBL_unc: uncorrelated limit
 uldm_c_grav: ULDM combined Earth + pulsar signal for gravitationalonly coupling
 uldm_c_grav_cor: correlated limit
 uldm_c_cor_grav_low: low mass region
 uldm_c_cor_grav_mon: monopole region
 uldm_c_cor_grav_low: high mass region
 uldm_c_unc: uncorrelated limit
 uldm_c_unc_grav_low: low mass region
 uldm_c_unc_grav_mon: monopole region
 uldm_c_unc_grav_low: high mass region
 uldm_c_grav_cor: correlated limit
For the substructure (static) model, we provide the chain for the marginalized distribution (as for the ULDM signal, the substructure signal is always superimposed to the SMBHB signal)

Modern data analysis frequently involves largescale hypothesis testing, which naturally gives rise to the problem of maintaining control of a suitable type I error rate, such as the false discovery rate (FDR). In many biomedical and technological applications, an additional complexity is that hypotheses are tested in an online manner, onebyone over time. However, traditional procedures that control the FDR, such as the BenjaminiHochberg procedure, assume that all pvalues are available to be tested at a single time point. To address these challenges, a new field of methodology has developed over the past 15 years showing how to control error rates for online multiple hypothesis testing. In this framework, hypotheses arrive in a stream, and at each time point the analyst decides whether to reject the current hypothesis based both on the evidence against it, and on the previous rejection decisions. In this paper, we present a comprehensive exposition of the literature on online error rate control, with a review of key theory as well as a focus on applied examples.We also provide simulation results comparing different online testing algorithms and an uptodate overview of the many methodological extensions that have been proposed.more » « less

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