adsabs/WIESP2022-NER huggingface dataset:
Paper: 2020ApJ...901...85D (fulltext)
CP problem in particle physics, as was first proposed by Peccei Quinn ( 1977 ), Wilczek ( 1978 ), and Weinberg ( 1978 ). Ultralight axions are an extrapolation of the QCD axions (with expected masses in the range eV) to much smaller masses eV (Press et al. 1990 ; Hu et al. 2000 ; Peebles 2000 ). At low redshift, they form a Bose–Einstein condensate that oscillates coherently (unlike a stochastic background) on a timescale (e.g., Sikivie Yang 2009 ; Marsh 2016 ; Hui et al. 2017 ; Grin et al. 2019 ; Niemeyer 2019 , and references therein). Constraints on their mass m a have been set using Ly α forest measurements (Armengaud et al. 2017 ; Iršič et al. 2017 ; Kobayashi et al. 2017 ) and cosmic microwave background lensing (Hlozek et al. 2018 ) on megaparsec scales, dwarf spheroidals (Marsh Pop 2015 ; González-Morales et al. 2017 ; Broadhurst et al. 2019 ; Marsh Niemeyer 2019 ; Safarzadeh Spergel 2019 ) and ultradiffuse galaxies (Wasserman et al. 2019 ) on kiloparsec scales, and galactic core observations on (sub)parsec scales (Bar et al. 2019 ; Desjacques Nusser 2019 ; Davies Mocz 2020 ). Pulsar timing offers another avenue to probe the existence of coherent oscillations induced by ultralight scalar fields (Khmelnitsky Rubakov 2014 ; Blas et al. 2017 ; de Martino et al. 2017 ). Upper limits on the amplitude of such an oscillating gravitational potential in the Milky Way halo have already been derived from pulsar timing arrays (Porayko Postnov 2014 ; Porayko et al. 2018 ). Cross-correlation of residuals from different pulsars should improve these constraints (Hellings Downs 1983 ). Axion coherent oscillations also resonate with binary pulsars (Blas et al. 2017 ). While the effect is strongest near resonance, the very small width of the latter (when the coupling is purely gravitational) implies that one shall monitor instantaneous variations (Rozner et al. 2019 ) or the secular drift of orbital elements (Blas et al. 2019 ) away from resonances. Mashhoon ( 1978 ) used Lagrange’s planetary equations to develop an approximate theory of the interaction of a weak gravitational wave with a Keplerian binary. In this paper, we use angle-action variables to investigate the instantaneous variations (that is, not averaged over one orbital time) of a Keplerian system produced by an oscillating background of axion dark matter. We refer the reader to Binney Tremaine ( 1987 ) for an overview of the angle-action formalism. The paper is organized as follows. After a brief presentation of the astrophysical/cosmological context and our numerical implementation in Section 2 , we solve for the time evolution of the perturbed binary system using angle-action variables in Section 3 . We explore the instantaneous variations of the Roemer time delay as a function of orbital parameters, etc., in Section 4 . We conclude in Section 5 . 2. Setup We will use the numerical simulations of Rozner et al. ( 2019 ) to validate our theoretical predictions. We consider a binary
Paper: 2021ApJ...909...72M (fulltext)
of toroidal field, attributable to a magnetic buoyancy instability (see for e.g., Vasil Brummell 2008 ; Guerrero Käpylä 2011 ). If this were to be the case, we might then expect multiple flux structures to be formed at the same time in a turbulent environment, and such structures would most likely possess random strengths, twists, and separations drawn from some distribution. We therefore choose each of our simulations in the MC series for a particular background field strength to start with five flux concentrations, with random initial twists (−5 ≤ q ≤ 5), and random distance, d , between them (with the constraint, d ≥ 2 R , so that they do not overlap), embedded initially at the same (canonical) height in the prescribed overlying large-scale background field. The random values are drawn from a uniform distribution over the range given. Our setup certainly has some significant degree of arbitrariness, owing to the limitations in our quantitative understanding of the amount of twist in flux tubes, the distance between them, and their strengths at their origination in the deep solar interior. For example, our choice of drawing the random values of twist from a uniform distribution might not be very realistic, and a Gaussian with a larger range might be better (or perhaps even a skewed distribution, to account for the effects in the formation of tubes not included in our model, e.g., rotation: see Chatterjee et al. 2011 ). However, as mentioned above, there are some constraints on the value of ∣ q ∣. Firstly, ∣ q ∣ ≳ 12 (the dashed line in Figure 8 , which is actually a very weakly decreasing function of B s ) is unphysical, since in this case the azimuthal field is sufficiently strong to induce a negative density in the structure. Secondly, structures where ∣ q ∣ ≳ 8 are unstable to kink instabilities (according to Linton et al. ( 1996 ), which does not fit our model exactly, but is probably a reasonable guide). Therefore, our chosen distribution (uniform, ∣ q ∣ ≤ 5) seems reasonable, in that it makes it likely that the initial simulation setup has a mixture of weak and reasonably strong concentrations of both helicities, and, if anything, overemphasizes the stronger twists (i.e., those more likely to violate the SHHR). An MC series of this type (fifty simulations, five random tubes each) is reported for four different background field strengths, ranging from relatively weak to relatively influential (0.01 ≤ B s ≤ 0.10). We identify the weak background field with the beginning and end of the cycle, and the stronger background with the peak of the cycle, although we have no direct observational evidence from the deep solar interior of the relative strengths of these fields from which to set their values. Figure 13 shows the results of the MC simulations for each of the four different background field strengths. Each circle in the plot represents an accumulation over a specified time interval [ t 0 , t 1 ] in the simulation of the absolute value
Paper: 2019ApJ...871..133J (fulltext)
; Jess et al. 2012 ; Krishna Prasad et al. 2015 , to name but a few). Wang et al. ( 2013 ) employed simulations to indicate that PDs, which are ubiquitously observed in coronal SDO /AIA observations, may be produced by small-scale impulsive heating events (e.g., nanoflares) at the loop footpoints. However, the models employed by Wang et al. ( 2013 ) utilize a more straightforward cosine-based impulsive functional form, rather than the traditional impulsive rise and gradual decay found in many small-scale flare studies. Furthermore, since Keys et al. ( 2011 ) also documented how the velocity evolution of small-amplitude flares closely matches the intensity fluctuations, the Monte Carlo–based nanoflare time series presented here could equally be employed to model intensity or velocity impulses at the base of coronal loops. These more traditional impulsive rises and exponential decays could then be fed into magnetohydrodynamic modeling codes, such as that used by Wang et al. ( 2013 ), to investigate in more detail whether nanoflares can contribute to the creation of PDs in coronal loops, fans, and plumes. Additionally, Hudson Warmuth ( 2004 ) revealed evidence to support the earlier work of Uchida ( 1968 ), whereby wave motion in coronal loops can be associated with weakly nonlinear effects originating in metric Type II bursts. Therefore, the inherent “sawtooth” shape of the nanoflare light curves may be able to help probe some of the weakest oscillating structures, especially since chromospheric and coronal fluctuations are becoming increasingly ubiquitous with continual improvements in instrument spatial and temporal resolutions (Jess et al. 2015 ; Grant et al. 2018 ; Houston et al. 2018 ). Finally, the nanoflare characterization approaches described here can also be directly applied to high time resolution observations of stellar sources (e.g., modernizing the work of Audard et al. 1999 ; Kashyap et al. 2002 ). For example, the photometric precision of high-cadence Kepler observations (Koch et al. 2010 ), or those taken through an assortment of optical filters by ULTRACAM (Dhillon et al. 2007 ) at very high cadences, would be ideal to test the nanoflare signatures on variable dwarf stars. Indeed, developing from the work of Hawley et al. ( 2014 ), Pitkin et al. ( 2014 ), Balona ( 2015 ), and Lurie et al. ( 2015 ), the initial Monte Carlo simulations could be extended to larger-amplitude values to expand the “nanoflare” coverage into more macroscopic sources that demonstrate intensity fluctuations exceeding 3 σ N . With larger-scale impulsive events being much more evident in the resulting light curves, the user would have a much better grasp of the e -folding timescales associated with the flaring activity. As a result, the occurrence rates of (quasi-)periodic activity could be much more reliably constrained, thus better revealing the energy output of such flaring events on distant stellar sources. In addition, high temporal resolution (∼0.1 s)
Paper: 2020ApJ...894...19R (fulltext)
et al. 2014 ). We used the same tools to produce periodograms for our SOAR and McDonald data, whose rapid acquisition cadence allowed us to identify the correct K2 periodogram alias (Figure 1 ). After identifying the true alias, we performed a least squares fit of a sinusoid to the K2 light curve data with the software Period04 (Lenz Breger 2014 ), using the peak frequency of 3152 μ Hz as an initial guess. Our best-fit value for the period of J1252 from K2 is 317.278 ± 0.013 s (3151.807 ± 0.013 μ Hz). When fitting to the ground-based data, we used this period as an initial guess, and performed nonlinear least squares fits to determine if the period was consistent for all data sets (Table 2 ). Table 2 Results of Nonlinear Least Squares Fits of a Sinusoidal Signal to McDonald Observatory Light Curves from 2018 to 2019 Note. The equation used is , where the initial guess for P is the period determined from the K2 data (317.278 ± 0.013 s). We report the time of first photometric maximum in each filter for each set as determined from the phase term. Because white dwarf rotation rates are typically on the order of hours to days (Berger et al. 2005 ; Hermes et al. 2017b ), we were surprised to find that the period of this object was far shorter, falling at over 11 times the Kepler long-cadence Nyquist frequency (283.5 μ Hz). Murphy ( 2014 ) presents the function by which periodogram amplitude diminishes with super-Nyquist signals as , where t exp is the instrument sampling rate, and P is the object period. For J1252, the measured amplitude from the Kepler long-cadence sampling rate is diminished by a factor of 17.79 compared to the intrinsic. The inferred intrinsic amplitude from this is 4.20%, which is comparable to the 4.56% amplitude from the McDonald BG40 data (Figure 1 ). The remaining difference is attributable to the respective Kepler and BG40 filter bandpass shapes and wavelength ranges. We detected a significant difference in color amplitude, with ratio g / i = 2.26 ± 0.22 in our multicolor light curves, but did not detect a significant phase shift between the two colors (Figure 2 ). The individual fits also show evidence of period and phase modulations. The 2018 McDonald BG40 and SDSS filter sets were separated in acquisition by one day, but show a 2–3 σ difference in the object period. In addition, there is significant power asymmetry in the lobes of the primary feature of the Fourier transform for the observational data, indicative of phase modulation. More data are needed to explore these phenomena further. Figure 2. Folded light curves of J1252 in SDSS- g and - i filters from McDonald Observatory in 2018. We note a large ratio (>2) in amplitude between the two filters, and no apparent phase difference. 3.2. Atmospheric Parameters and Magnetism We present the phase-binned 400 l mm −1 SOAR spectra of J1252 in Figure 3 . To produce these, we folded our individual spectra into five equally spaced phase bins, each covering one-fifth of the
Paper: 2020ApJ...895...18B (fulltext)
1982 ; Forbes 2000 ; Vršnak 2008 ; Aulanier 2014 ; Janvier et al. 2015 , for reviews). Characterizing coronal currents before and during eruptions is therefore important to develop a comprehensive understanding of the triggering and acceleration of CMEs, especially in the context of a growing need for accurate and quantitative space weather forecasts. In addition, on a more fundamental basis, specifying the spatiotemporal evolution of eruption-related currents at the Sun’s surface would also bring an unprecedented input to the versus debate (e.g., Melrose 1991 ; Parker 1996 , 2001 ; Heikkila 1997 ; Vasyliunas 2005 ), in particular regarding whether the magnetic field or the electric current should be considered as a prime variable and boundary condition in solar eruption models in particular, and in space plasmas in general. All these considerations have recently led to a renewed interest in studying solar electric currents (see, e.g., Melrose 2017 ; Georgoulis 2018 ; Schmieder Aulanier 2018b ). The paradigm regarding the spatial distribution of currents in solar active regions has evolved over the years. For a long time, the geometrical properties of cylindrical flux ropes naturally led to the view that active region magnetic fields ought to be shielded from their surroundings through external return currents, with a magnitude equal to that of the direct currents. This so-called current neutralization of coronal flux ropes was considered to be a natural prediction of the paradigm, in the context of the MHD twisting of coronal flux-tubes (as explained e.g., in Parker 1996 ; Dalmasse et al. 2015 ). Such neutralized currents in the corona, however, are problematic for several eruption models that rely on the existence of net currents (e.g., van Tend Kuperus 1978 ; Forbes Priest 1995 ; Kliem Török 2006 ; Démoulin Aulanier 2010 ). In addition, the most recent observations of sunspots revealed that many flaring active regions carry a non-neutralized (i.e., a net) current (Ravindra et al. 2011 ; Georgoulis et al. 2012 ; Cheng Ding 2016 ; Zhao et al. 2016 ; Vemareddy 2019 ). These observations could have been considered to support the paradigm, with some circuit models associated with net coronal electric currents directly originating from the depths of the Sun’s interior (Alfvén Carlqvist 1967 ; Melrose 1991 ). However, recent numerical MHD models of magnetic fields in the corona driven by magnetic flux emergence (Török et al. 2014 ) or by line-tied motions (Dalmasse et al. 2015 ) have both unveiled that MHD processes could also generate net coronal currents in pre-eruptive active regions, due to the shear component of the magnetic field across polarity inversion lines (PILs). Two different analytical interpretations have been provided to explain this association, either by invoking Lorentz forces (Georgoulis et al. 2012 ) or a simple property of the integral form of Ampère’s law with force-free fields (Dalmasse et al. 2015 ). In both cases, it was
Paper: 2019A&A...631A..52J (fulltext)
soft X-ray emission towards the south loop. As suggested by Snowden et al. (1995) , we find that significant 0.25 keV emission remains inside the loop after the foreground emission from the Local Bubble and the background intensity are subtracted. The background intensity is the intensity that is expected from the Galactic halo and is modulated by the total gas absorption. Taking the south loop absorption into account, we find no clear spectral difference between the EXE1 region north of the east shell and the EXE2 region towards the south loop. The spectral dichotomy that is visible on the maps is largely induced by the south loop absorption. A single-temperature plasma cannot match the X-ray spectra across the three bands in any of the sampled directions, however. Two hot phases are required, with respective temperatures of 3–9 MK and 0.3–1.2 MK under the assumption of a uniform pressure across both phases. The derived pressure of (3− 8) × 10 4 cm −3 K exceeds the pressure of the Local Bubble of 10 4 cm −3 K ( Puspitarini et al. 2014 ). The need for at least two plasma phases might indicate global plasma oscillations across the superbubble that are caused by its complex interior history ( Krause et al. 2014 ). In addition, the significant emission hardening seen towards the region that is enclosed between the H α arcs A and B corresponds to a higher pressure and hotter temperature that support the hypothesis of a younger age and faster expansion of this sub-region ( Heiles et al. 1999 ). The origin of the south loop ring is unclear. A wind-blown bubble seems unlikely because of the lack of massive stars in this direction ( Burrows et al. 1993 ). We find no gradient in temperature or pressure between the south loop and adjacent regions that would support a cooling flow of hot gas that leaks at the rear of the superbubble ( Heiles et al. 1999 ). The gas ring, filled with plasma that is a few million degrees hot, is consistent with an old supernova remnant. The progenitor star may have belonged to the Cassiopeia-Taurus OB association, which extends from 130 to 300 pc ( de Zeeuw et al. 1999 ; Pellizza et al. 2005 ) in these directions. The more sensitive and refined X-ray maps that will soon be obtained by the extended Roentgen survey with an imaging telescope array (e-ROSITA) will spatially and spectrally constrain the heterogeneity of the hot gas phases that fill the superbubble better. This will enable us to reconstruct the possible sequence of supernovae that have blown this structure. 6.3 Magnetic field in the superbubble We have used Planck polarised dust emission and the gas dynamics information from the H I line decomposition to study the magnetic field in the superbubble. We probed the plane-of-sky magnetic field, B sky , using the modified Davis–Chandrasekhar–Fermi method (DCF method) proposed by Cho Yoo (2016) . We have obtained magnetic strengths 17 times lower than previous estimates along the west rim ( Soler et al. 2018 ). The
Paper: 2021MNRAS.504.5270D (fulltext)
M V ∼ −13.5 and Carina M V ∼ −9.5) merit a special mention. Although both dwarf galaxies have been quenched fairly recently (∼2.5–3 Gyr ago), there are suggestions that these dwarf galaxies have been orbiting the MW for a much longer time (e.g. Pasetto et al. 2011 ; Rocha et al. 2012 ; Patel et al. 2020 ; Rusakov et al. 2020 ). However, the results are dependent upon a number of model uncertainties and are much debated in the literature (Jahn et al. 2019 ; Pardy et al. 2019 ). We choose to retain Fornax and Carina in our sample. This leaves us with a sample of 13 and 28 satellites for the MW and M31, respectively (see Table 1 ). The lookback time of shutdown in star formation of these dwarf galaxies has been drawn from the literature, and has been estimated from data of varying quality. For 6 M31 dwarfs drawn from the ISLAandS project (Skillman et al. 2017 ), the shutdown time has been estimated from deep CMDs reaching down to the oldest main-sequence turn-off (MSTO). The shutdown time for the vast majority of the M31 dwarfs have been estimated from resolved HST data reaching fainter than the horizontal branch (Weisz et al. 2019b ). A sizeable fraction of the satellites of the MW have their time of their shutdown in star formation estimated from HST data reaching down to the MSTO (Weisz et al. 2014 ). For a few of the nearer MW dwarfs, the star formation has been estimated using ground-based data (e.g. Torrealba et al. 2016 ; Bettinelli et al. 2018 ). In Table 1 , we have included both the systematic and statistical uncertainties of τ 90 . The constraints on τ 90 as well as the ancient star formation history of M31 dwarfs will dramatically improve with observations from the upcoming Cycle 27 HST Treasury survey as part of HST -GO-15902 (PI D. Weisz). Table 1. Star formation shutdown time. Note . Distance of M31 satellites is taken from Conn et al. ( 2012 ) and Weisz et al. ( 2019a ). τ 90 is taken Weisz et al. ( 2014 ), Weisz et al. ( 2019b ), Brown et al. ( 2014 ), Bettinelli et al. ( 2018 ), and Torrealba et al. ( 2016 ). M V taken from McConnachie ( 2012 ). 5.1 Temporal clustering of star formation shutdown The lookback time of shutdown in star formation in the satellites of the MW and M31 is strongly clustred. We demonstrate this by employing a kernel density estimator (KDE) to estimate the mean lookback quenching time of these clusters. In the bottom panel of Fig. 21 , we estimate the probability density function (pdf) of the lookback quenching time. To do this, we select the ‘bandwidth’ (smoothing parameter) for each KDE, such as to to optimize the bias-variance tradeoff (1.29 and 0.98 for the MW and M31, respectively). The pdf of the MW is double humped with prominent peaks at ∼2.5 and ∼11 Gyr ago. The pdf of M31 has a single prominent peak ∼6 Gyr ago. These prominent peaks continue to be significant and stable, even after taking into account the uncertainties in the lookback quenching time of the satellites. The inclusion of the
Paper: 2018ApJ...867L..15T (fulltext)
subtraction, flat-fielding, cross-talk correction, fringing, etc.). From the CP calibrated frames, we use the Astromatic 5 5 http://www.astromatic.net/ software suite (Source Extractor (SE); SCAMP; SWARP; PSFEx; Bertin Arnouts 1996 ; Bertin et al. 2002 ; Bertin 2006 ) to register frames to a common coordinate system and account for pixel scale distortions across the DECam field of view using the 2MASS astrometric reference star catalog and calibrate our photometry to the Sloan Digital Sky Survey (SDSS) system using frames of the LSE_44 SDSS southern standard star field taken at varying airmass during the observing nights. 3. Analysis 3.1. Dwarf Detection and Photometric Analysis We detect dwarf candidates using a full-color RGB image constructed from our u ′ g ′ z ′ imaging with custom Python image processing scripts. This filter combination samples the full optical spectral energy distribution (SED) and is sensitive to old, metal-poor stellar populations expected of primordial dwarf galaxies, while the inclusion of the u ′-band also serves to sample flux from younger stellar components arising from more recent star formation. Figure 1 shows the RGB image with orange dashed curves indicating NGC 5128-centric iso-radial contours. We visually searched this image for faint, extended sources displaying smooth morphologies with shallow surface-brightness ( μ ) profiles typical of low-luminosity dwarfs. Using this method, we unambiguously find 16 dwarf galaxy candidates, of which a single previously known dwarf is recovered (KK98a 189; Karachentseva Karachentsev 1998 ) and the remaining 15 are reported here for the first time, noting that one dwarf (dw1317-4255) has recently been confirmed to be a true member of the Centaurus A group Crnojević et al. 2018 ). The set of zoom-in images in Figure 1 show the dwarf candidates and measure 2′ on a side, corresponding to physical ∼2.2 × 2.2 kpc 2 regions at the distance of NGC 5128. We point out that by employing a “by eye” detection technique, we are unable to make a robust estimate of the completeness limit to our imaging in a straightforward manner and thus cannot make broad statements on the true total population of dwarfs in this region. We instead defer such an analysis to a future work detailing the overall properties of dwarfs detected throughout our survey region, noting that a lack of completeness estimate does not affect our conclusions. Figure 1. Locations of the new dwarf galaxy candidates on a u ′ g ′ z ′-based RGB color image. White-bordered images show 15 × 15 cutouts centered on the dwarfs, corresponding to ∼1.7 × 1.7 kpc 2 at the distance of NGC 5128. The scale of the background frame is indicated in the lower-left corner. Dashed orange curves show distances corresponding to 100, 150, and 200 kpc from NGC 5128. North is up, east is to the left. We use galfit (v3.0.5; Peng et al. 2010 ) to measure the structural and photometric properties of the dwarfs. We first create 365 × 365 (4 × 4 kpc