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1. Interpretation of the spectrum of eastward‐moving tropical convective anomalies

   https://doi.org/10.1002/qj.3709  

   Roundy, Paul E. ( December 2019 , Quarterly Journal of the Royal Meteorological Society)

   Wave‐number‐frequency power spectrum analysis has been used as a primary tool to detect the ranges of wave numbers and frequencies about which observed convectively coupled equatorial waves are active. Previous works have suggested that activity in these waves clusters between roughly 12 and 60 m equivalent depths because spectral peaks normalized by dividing by a smoothed spectral background follow those ranges. Through a combination of wave‐number‐frequency power spectrum analysis, filtering and linear regression, this work shows that the traditional approach generates confusion because it conflates different, sometimes conflicting, signals from around the world that contribute to the same parts of the spectrum. Results also suggest that the traditional method leads us to ignore substantial power associated with variability structurally consistent with observed Kelvin waves but that occurs at lower frequencies. Wave signals at these frequencies are stronger than but similar to Kelvin wave signals coincident with the Kelvin peak in the normalized spectrum. Results suggest that the wave signal itself has red properties, possibly because more strongly convectively coupled waves propagate more slowly. The slower, more intense wave signals outside of the standard band would impact tangible weather signals and should not be ignored in operations. Instead, results support the view that disturbances labelled as Kelvin waves form a continuum with the Madden–Julian Oscillation (MJO) and suggest that the whole region of the spectrum from the broadly recognized Kelvin band to the MJO should be considered together.

    

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2. On-Chip Digital Fourier-Transform Spectrometer Using a Thermo-Optical Michelson Grating Interferometer

   Richard A. Soref, Francesco De ( November 2018 , Journal of lightwave technology)

   This theoretical modeling and simulation paper presents designs and projected performance of an on-chip digital Fourier transform spectrometer using a thermo-optical (TO) Michelson grating interferometer operating at∼1550 and 2000 nm for silicon-on-insulator and for germanium-on-silicon technological platforms, respectively. The Michelson interferometer arms consist of two unbalanced tunable optical delay lines operating in the reflection mode. They are comprised of a cascade connection of waveguide Bragg grating resonators (WBGRs) separated by a piece of straight waveguide with lengths designed according to the spectrometer resolution requirements. The length of eachWBGRis chosen according to the Butterworth filter technique to provide one resonant spectral profile with a bandwidth twice that of the spectrometer bandwidth. A selectable optical path difference (OPD) between the arms is obtained by shifting the notch in the reflectivity spectrum along the wavelength axis by means of a low-power TO heater stripe atop the WBGR, inducing an OPD that depends on the line position of the WBGR affected by TO switching.We examined the device performances in terms of signal recostruction in the radio-frequency (RF) spectrum analysis application at 1 GHz and at 1.5 GHz of spectrometer resolution. The investigation demonstrated that high-quality spectrum reconstruction is obtained for both Lorentzian and arbitrary input signals with a bandwidth up to 40 GHz. We also show that spectrum reconstruction of 100–200 GHz RF band input signals is feasible in the Ge-on-Si chips. 

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3. The NANOGrav Search for Signals from New Physics: MCMC chains

   https://doi.org/10.5281/zenodo.8083620  

   NANOGrav, The Collaboration ( January 2023 , Zenodo)

   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 burn-in 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: scalar-induced GWs
     • sigw_box: assumes a box-like feature in the primordial power spectrum.
     • sigw_delta: assumes a delta-like 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
     • stable-c: stable strings emitting GWs only in the form of GW bursts from cusps on closed loops.
     • stable-k: stable strings emitting GWs only in the form of GW bursts from kinks on closed loops.
     • stable-m: stable strings emitting monochromatic GW at the fundamental frequency.
     • stable-n: stable strings described by numerical simulations including GWs from cusps and kinks.
   • meta: metastable cosmic strings
     • meta-l: metastable strings with GW emission from loops only.
     • meta-ls metastable strings with GW emission from loops and segments.
   • super: cosmic superstrings.
   • dw: domain walls
     • dw-sm: domain walls decaying into Standard Model particles.
     • dw-dr: domain walls decaying into dark radiation.

   For each model, we provide four files. One for the run where the new-physics signal is assumed to be the only GWB source. One for the run where the new-physics 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 B-L
     • uldm_vecBL_cor: correlated limit
     • uldm_vecBL_unc: uncorrelated limit
   • uldm_c_grav: ULDM combined Earth + pulsar signal for gravitational-only 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

   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)

    

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4. All-sky modelling requirements for Bayesian 21 cm power spectrum estimation with bayeseor

   https://doi.org/10.1093/mnras/stad401  

   Burba, Jacob ; Sims, Peter H. ; Pober, Jonathan C. ( February 2023 , Monthly Notices of the Royal Astronomical Society)

   We present a comprehensive simulation-based study of the bayeseor code for 21 cm power spectrum recovery when analytically marginalizing over foreground parameters. To account for covariance between the 21 cm signal and contaminating foreground emission, bayeseor jointly constructs models for both signals within a Bayesian framework. Due to computational constraints, the forward model is constructed using a restricted field of view (FoV) in the image domain. When the only Epoch of Reionization contaminants are noise and foregrounds, we demonstrate that bayeseor can accurately recover the 21 cm power spectrum when the component of sky emission outside this forward-modelled region is downweighted by the beam at the level of the dynamic range between the foreground and 21 cm signals. However, when all-sky foreground emission is included along with a realistic instrument primary beam with sidelobes above this threshold extending to the horizon, the recovered power spectrum is contaminated by unmodelled sky emission outside the restricted FoV model. Expanding the combined cosmological and foreground model to cover the whole sky is computationally prohibitive. To address this, we present a modified version of bayeseor that allows for an all-sky foreground model, while the modelled 21 cm signal remains only within the primary FoV of the telescope. With this modification, it will be feasible to run an all-sky bayeseor analysis on a sizeable compute cluster. We also discuss several future directions for further reducing the need to model all-sky foregrounds, including wide-field foreground subtraction, an image-domain likelihood utilizing a tapering function, and instrument primary beam design.

    

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5. Wideband Temporal Spectrum Sensing Using Cepstral Features

   https://doi.org/10.1109/WoWMoM.2019.8793002  

   Cheng, Hanke ; Mark, Brian L. ; Ephraim, Yariv ( June 2019 , 2019 IEEE 20th International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM))

   Spectrum sensing enables secondary users in a cognitive radio network to opportunistically access portions of the spectrum left idle by primary users. Tracking spectrum holes jointly in time and frequency over a wide spectrum band is a challenging task. In one approach to wideband temporal sensing, the spectrum band is partitioned into narrowband subchannels of fixed bandwidth, which are then characterized via hidden Markov modeling using average power or energy measurements as observation data. Adjacent, correlated subchannels are recursively aggregated into channels of variable bandwidths, corresponding to the primary user signals. Thus, wideband temporal sensing is transformed into a multiband sensing scenario by identifying the primary user channels in the spectrum band. However, future changes in the configuration of the primary user channels in the multiband setup cannot generally be detected using an energy detector front end for spectrum sensing. We propose the use of a cepstral feature vector to detect changes in the spectrum envelope of a primary user channel. Our numerical results show that the cepstrum-based spectrum envelope detector performs well under moderate to high signal-to-noise ratio conditions. 

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