adsabs/WIESP2022-NER huggingface dataset:
Paper: 2017ApJ...843..119R (fulltext)
source, no absorption occurs. An H i absorption line is centered at a frequency, blueshifted or redshifted, according to the H i cloud velocity along the line-of-sight (LOS or radial velocity, V r ). A more realistic scenario is depicted in Figure 1 with a number of H i clouds and continuum sources, represented as discrete clouds. The H i clouds in front of the object of interest (continuum source, red object) will absorb radiation. If the background LOS is adjacent, the distribution of H i and continuum clouds would be nearly the same. Figure 1. Geometry for the case of many H i clouds (yellow) and continuum sources (green) along the line-of-sight toward the object of interest (red). The equation of radiative transfer is , where is the specific intensity, ds is distance along the LOS, is the emission coefficient, and is the linear absorption coefficient. In local thermodynamic equilibrium, one has , the Planck function. For low frequency (), simplifies to the Rayleigh–Jeans formula where T B is the brightness temperature. The solution of the equation of radiative transfer in plane parallel geometry, ignoring scattering, is , with . This is converted to brightness temperature ( T B ) units using . The discrete form of this result using an arbitrary large set of optically thin H i clouds and continuum emission regions isHere the index m sums over the emission regions and the index n sums over the emitting and absorbing H i clouds. is the continuum brightness temperature of cloud m. Here, and are the total optical depth, from each continuum or H i region to the observer, is the optical depth of a single H i region along the LOS, and for all clouds in front of m . The “on” spectrum is the spectrum toward the source and the “off” spectrum is the spectrum toward the background. Subtracting the “off” spectrum from the “on” spectrum, the H i absorption spectrum is given as is the H i optical depth, and is plotted as the absorption spectrum. 3.2. Kinematic Distances and Resolving the Kinematic Distance Ambiguity The radial velocity of the object of interest, or limits on it, can be deduced from the H i absorption spectrum. Then the kinematic distance to the object of interest can be determined, assuming objects move with circular velocity V ( R ) in the Galaxy (Kwee et al. 1954 ). The radial velocity of the object with respect to the Sun is V r : or , where l is the Galactic longitude. Since the rotation curve provides V ( R ) for any R , , R can be found from this equation. The relation between distance, d , to the object from the Sun, in the direction of Galactic longitude, l , and the Galactocentric distance R is given by , which has the solution of . There is a unique solution for d if . For there are two possible distances. This is the kinematic distance ambiguity. For the purpose of this discussion we assume Galactic longitudes of 0°–90°, so that radial velocity is positive for objects inside the solar circle and negative for those
Paper: 2017ApJ...849...50N (fulltext)
rows show the correlations and S/Ns after flipping the 185 MHz image about its vertical axis before the correlation calculation, giving an independent estimate of the noise. A significant correlation between 185 MHz and 4.5 μ m sources appears after masking infrared sources down to 1 mJy, which we show in Figure 5 cleanly removes the stars from the sample. The 185 MHz and 850 nm sources exhibit a marginal correlation after masking down to 1 mJy, though it does not appear significant in comparison to the noise correlation seen after flipping the radio image. We show in Figure 7 that this is due to increased stellar contamination at 850 nm compared to that at 4.5 μ m. The 185 MHz and 4.5 μ m catalogs exhibit a positive correlation peaking at 0.0332 ± 0.005 after masking infrared sources down to 10 −4 Jy (18.9 mag) and radio sources down to 1 Jy, and remain significant down to the completeness limits of these catalogs. There is no significant correlation detection after flipping the 185 MHz image, indicating that this detection is not an artifact of the analysis or of primary beam or vignetting effects. The 185 MHz and 850 nm catalogs exhibit a marginal correlation after masking infrared sources down to 10 −3 Jy (16.4 mag) and radio sources down to 0.3 Jy, though it does not appear significant in comparison to the level of correlation noise in the flipped image. To understand these findings, we begin by investigating which 4.5 μ m sources are responsible for this correlation. We select the subset of sources detected in the WISE 3.4 μ m, 4.5 μ m, and 12 μ m bands and plot them (Figure 5 , left panel) in the [4.6 μ m]–[12 μ m] versus [3.4 μ m]–[4.6 μ m] color–color space used by Wright et al. ( 2010 ) to illustrate the separation between different types of sources. Nikutta et al. ( 2014 ) study more quantitatively how sources separate in this space, finding that stars are isolated in the region , (1 σ ). In the right panel, we plot the faintest 90% of sources (fainter than 18.25 mag at 4.6 μ m) in the same color–color space and observe that this cut effectively cleanly excludes nearly all the stars. This explains the detection of a 185 MHz–4.5 μ m correlation only after masking the brightest 10% of sources. Figure 5. The ALLWISE sources are plotted in the color–color space of Wright et al. ( 2010 ) prior to any flux cuts (left panel), showing a cluster of stars near (0, 0). The color bar shows the number of sources in each cell. Then after cutting out the brightest 10% of sources (fainter than 18.25 AB mag), the stars are cleanly removed. We roughly split the remaining sources into AGNs () and SF galaxies (; Mingo et al. 2016 ), where [3.4 μ m]—[4.6 μ m]. To further probe which mid-infrared sources are responsible for this correlation, we make a rough cut to separate quasars and active galactic nuclei (AGNs; ) from star-forming (SF) galaxies (; Mingo et al. 2016 ). In Figure 6 we plot the power spectrum of 185 MHz sources (left panel), 4.5 μ m
Paper: 2021A&A...650A..59B (fulltext)
the outer and inner edges of the synthesized beams. When this ratio is greater than 1, the measured velocity becomes sub-Keplerian, and vice versa. Starting from the initial non-convolved image, this ratio can be perfectly known in the models and is shown in the bottom-center and bottom-right panels of Fig. 6 . As expected, the emission ratio I out ∕ I in is larger at 0.3′′ than at 0.1′′ and lead to stronger velocity biases. In real observations, the emission ratio can only be approached because the image received is already convolved by the observational beam. We show, however, in the bottom-left panel of Fig. 6 that the general tendency of the flux variations can be recovered, even if their magnitude is probably underestimated. In general, we find in our models that the flux ratio I out ∕ I in is proportional to the beam size, with ratios at 0.3′′ about three times larger than at 0.1′′, which is an expected result when the spatial variation of the line emission is relatively smooth compared to the synthesized beam size. Furthermore, biases are also sensitive to the velocity range Δ V probed by the synthesized beam, which is proportional to the beam size, and increases at smaller distances from the star dueto the steeper gradient of the Keplerian velocity. This leads to velocity artifacts generally larger at 0.3′′ compared to 0.1′′ and particularly important in the inner regions of the disk. Along the northern major axis, as shown in the top panel of Fig. 7 , the velocity deviations at 0.1′′ are about three times smaller thanat 0.3′′. Going to a higher spatial resolution will have then the double advantage of (i) reducing velocity biases and of (ii) allowing a better determination of the value and morphology of these biases, through a more precise knowledge of the disk structure. 4.3 Dust also affects the measured gas velocity The circumbinary disk around HD 142527 is not only made of gas but also contains a large concentration of dust particles at the north of the disk. The presence of this dust concentration, which is optically thick at 0.9 mm with τ Dust ~ 2–3 ( Muto et al. 2015 ; Boehler et al. 2017 ), has an important impact on the measured gas velocity at the north of the disk, as shown in the top panel of Fig. 7 . The four radial regions indicated in this figure are delimited by the radii at which the velocity shifts between a sub- and a super-Keplerian velocity at the spatial resolution of 0.3′′. Regions I and IV, which contain almost no millimeter dust emission, correspond to the two regions described previously in Sect. 4.2 . The 13 CO integrated emission uniformly increases for R ≤ 135 au and decreases for R ≥ 270 au, leading to the sub-Keplerian and to the super-Keplerian profiles. Fig. 7 Velocity deviations due to the presence of dust along the north of the major axis. Top : velocity deviations measured in the pure Keplerian model at two different spatial resolutions. Middle : 13 CO integrated emission and dust emission in
Paper: 2016A&A...585A.138D (fulltext)
that we assume to be fully ionised and composed of hydrogen and helium with the equilibration timescales and where and stand for the electron and ion mass, respectively; is the ion number density with the proton mass and being the ion mean molecular weight; is the ion charge number; the electron charge; and is the Coulomb logarithm. Since , we neglect the term in the energy exchange rates. For a mixture of hydrogen and helium, the timescale of the temperature coupling between hydrogen and helium would be proportional to . Thus, the equilibration between hydrogen and helium operates times faster than the equilibration timescale between hydrogen and electrons or helium and electrons. For hydrogen or helium, the ratio of also simplifies to (not the case for heavier elements). Therefore, protons and HeIII can be considered as one single population sharing the same temperature. Writing , we see that The variation in energy is symmetric between the transfer of ion energy and electron energy, but this is not the case for the variation in temperature, which is . Therefore, the difference in temperature change between ions and electrons will be a factor , where is the “mean molecular weight of electrons” . For astrophysical plasmas essentially composed of hydrogen and helium, the change in temperature between ions and electrons is close to symmetric. To solve for the system of two non-linear coupled equations, we rewrite ( 16 ) as where . We introduce the vector , and rewrite the previous two equations as The goal is now to iterate on to find the value of and as required by equations ( 20 ). The derivatives and are obtained by differentiating equations ( 20 ) with respect to and , where the superscript replaces the superscript in ( 20 ). The values of are obtained with Cramer’s rule, and is added to the value of until the variation in . 3. Numerical tests If not indicated, we use a courant factor of with adaptive time-stepping between the different levels of refinement. Fig. 1 Top : diffusion of a pressure step function as a function of position at (black), (red), (blue) for the simulation (symbols) and the analytic solution (solid lines). The extra levels of refinement are indicated as dashed lines. Bottom : relative errors of the simulation with respect to the analytic solution. 3.1. Diffusion of a step function We performed a simple 1D test of the diffusion of a step function with a constant diffusion coefficient of without hydrodynamics. The initial setup is a pressure of between and outside with . We start with a minimum level of refinement of and refine up to level wherever the relative pressure variation is larger than . The time step is chosen to be (for level 7), where is the stability time step for diffusion (only useful for explicit schemes). The time evolution of the step function has the following analytical solution for a constant diffusion coefficient where . Figure 1 shows the result of the diffusion of the step function with the
Paper: 2016ApJ...831..189R (fulltext)
sources, while the HCO + line is either undetected or detected at a very weak level ( T mb 0.1 K) in disk-dominated Class II/Stage II sources. Another notable feature is an axisymmetric profile observed in the HCO + line, which may be indicative of an infalling envelope. Typically, a blue-dominated infall asymmetry has been seen in the HCO + profile of some low-mass protostars, while a red-dominated axisymmetry is usually associated with disk-only objects or the presence of an outflow (e.g., Thi et al. 2004 ; Evans et al. 1999 ; van Kempen et al. 2009 ). However, this is not always the case, since red-dominated asymmetry has been observed in embedded sources (D. Harsono et al. 2016, in preparation), an interpretation of which could be an expanding envelope that is due to an outflow. Among the bona fide detections in our present work, J182902.12, J182855.78, and J182956.67 have previously been categorized as Stage 0/I systems by Heiderman Evans ( 2015 ), based on the criteria of HCO + (3-2) integrated line intensity of ≥0.68 K km s −1 , while J182841.87 has been categorized as a Stage II system. We note that the Heiderman Evans ( 2015 ) criteria is solely based on a HCO + detection within 14′′ of the Spitzer c2d source position. As can be seen in Figure 6 , J182902.12 show a broad HCO + profile centered at the cloud velocity. The line model fit shows a typical Gaussian, which implies that there is no infalling envelope in this case. We have shown through SED modeling that J182902.12 is a Class Flat system and may possess a tenuous envelope. Using the “Stage” classification scheme also places J182902.12 in the Stage I-T category. Therefore, the Stage 0/I classification based on the HCO + detection is inconsistent with other schemes. For the case of J182855.78, results from line modeling indicate that the observed HCO + asymmetry can be explained by an infalling envelope model (Figure 6 ). The SED modeling results and the Stage classification also indicate that this is a genuine case of a Class 0/I or Stage 0/I system. For the case of J182956.67, the HCO + profile shows a hint of a blueshifted asymmetry and self-absorption at higher velocities (Figure 6 ). The results from line modeling for J182956.67 suggest that there may be blue-dominated infall asymmetry due to a weak envelope component with velocities between 0.5 and 1 km s −1 and a mass infall rate of 10 −7 M yr −1 . However, both the SED model fit and the Stage classification indicate that this is a Class II edge-on disk system or a Stage II source, rather than embedded Stage 0/I systems (Table 1 ; column 9). On the contrary, J182841.87 can be placed in the Stage I-T category and shows signatures of a tenuous envelope (Section 3.2 ), unlike the Stage II classification based on HCO + nondetection. The envelope mass in this system is too low to produce any detectable emission in a high-density tracer such as the HCO + (3-2) line. The submillimeter emission observed toward this source likely
Paper: 2017ApJ...836..124D (fulltext)
the intensity weighted first moment (i.e., the centroid) of the star. We measure the total flux using a 6 pixel (≈504) aperture radius. We weight pixels that lie partially within this circular aperture by the fraction of the pixel that lies within the ideal circular aperture. We also adopt an aperture correction to correct for the stellar flux that falls outside of our aperture. The typical size of an aperture correction is 0.04 magnitudes, but can vary from night to night depending on atmospheric conditions (predominantly seeing). In Table 2 , we provide the corrected photometry for LP 661-13 across all telescopes and both seasons of data. Table 2 MEarth Photometry for LP 661-13 (This table is available in its entirety in machine-readable form.) Only a portion of this table is shown here to demonstrate its form and content. A machine-readable version of the full table is available. There is a marginal Roentgan Satellite ( ROSAT ) detection in the ROSAT faint source catalog (Voges et al. 2000 ). The potential source has a count rate of 3 ± 1 × 10 −2 counts per second. This corresponds to a flux density of approximately 1.70 ± 0.56 × 10 −13 erg cm −2 s −1 . While this may be interesting to assess the significance of the X-ray emission with stellar activity and radius inflation, further X-ray data to confirm this potential detection are needed. 3.3. Astrometry With two seasons of photometric data from the MEarth observatory, we are able to utilize the MEarth astrometric pipeline to measure the trigonometric distance to LP 661-13. The astrometric pipeline is described in detail in Dittmann et al. ( 2014 ), but we summarize it here. We eliminate nights for which the full width at half maximum for the image is greater than 5 pixels (approximately 3.5 arcseconds), or the average ellipticity of the target stars is greater than 0.5 (typically due to wind shake). We also eliminate images for which the pointing on the image is more than 15 pixels discrepant from the master MEarth image. We fit each image with a linear model in both the x and y coordinates, which allows for translation, rotation, shearing, and pixel scale variations. We also include a separate constant offset between data taken on opposite sides of the meridian, to accommodate the image flip that results from the telescopes’ German equatorial mount. We choose reference stars that lie within 600 pixels of the target star to avoid higher order effects at the edges of the CCD. We fit our astrometry iteratively, first fitting the linear plate model to each frame and then fitting for the proper motion and parallax of each star until convergence. Internal errors are estimated through a residual permutation. This provides the advantage that it preserves time-correlated noise in our error assessment but tends to be a weaker way to estimate errors for series with few data points. However, we have over 9000 data points and do not believe our astrometric error bars are underestimated. We perform
Paper: 2021ApJ...912...78R (fulltext)
takes place in TX Col (see Buckley Tuohy 1989 ; Norton et al. 1997 ; Wheatley 1999 ; Hellier 2002 ). Thus, a change in mass flow can be a reason for changing geometries between stream-fed dominance and disk-fed dominance. Further, since pure disk-fed accretion also takes place for some of the observational days, it implies that for those days, the contribution of mass accretion from the stream has been negligible in comparison to the disk. 2. They suggested that change in accretion mode is related to changes in the disk itself, which can be easily understood from our one-day analysis of TX Col. The sudden changes between the disk overflow to disk-fed or stream-fed cannot be only because of the change in the mass flow. The changes related to the disk, i.e., the change in the structure of the disk also takes place on such a timescale so that the combined effect of the two processes (i.e., the change in the mass accretion rate and the change in the structure of the disk) have shown such tremendous changes in the system. 4.2. Presence of QPOs We have also found periods of ∼5850–5950 s for days 46, 52, and 71, and these periods could be the QPOs. The flow of matter in IPs can take the form of diamagnetic “blobs” that orbit around the white dwarf (see King 1993 ; Wynn King 1995 for details). The accretion flow in TX Col can be explained as a combination of a stream and orbiting blobs (Hellier 2002 ). Keeping these ideas in mind, Mhlahlo et al. ( 2007 ) claimed that the QPO period results due to the beating of the Keplerian period of the orbiting “blobs” with the spin period. The theoretical calculation of QPO period demands the calculation of Keplerian period ( P kep = 2 π r d / v kep ), which we have calculated in the following way. The mass of the WD in the TX Col system was determined in the range of 0.48–0.82 M ⊙ (see Table 3 of Yuasa et al. 2010 ) in the different previous studies. Therefore, for our calculation, we have used the weighted mean of these mass values as 0.66 ± 0.03 M ⊙ , where the error on mass is the weighted standard deviation of different measurements. The WD radius, R WD , was calculated to be 0.012 ± 0.001 R ⊙ using the mass–radius relation of Nauenberg ( 1972 ). We have estimated the mass of the secondary as 0.61 ± 0.04 M ⊙ using the mass–period relation given by Smith Dhillon ( 1998 ). As a result, the mass ratio (q) was estimated to be 0.92 ± 0.07. The disk radius ( r d ) lies in the range of 2–3 times r r , where r r is the minimum outer radius of disk (Sulkanen et al. 1980 ). The estimation of r r involves binary separation (a), which was calculated using the relations as given in Warner ( 1995 ). The values of r r and “a” were found to be 1.08 × 10 10 cm and 12.17 × 10 10 cm, respectively. In this way, r d was estimated to be in the range of (2.2–3.2) × 10 10 cm. Using these parameters of TX Col, we obtained a range for Keplerian velocity () of 520–640 km s −1 . Further, we found that for the Keplerian velocity of ∼550
Paper: 2021ApJ...908..216V (fulltext)
the sources. Prior information of such properties can be summarized in a joint density distribution P (). If the corresponding sub-hypothesis does not include a multi-messenger detection, then there may not be a requirement for a common source and the source parameters . In that case, if the detectors are independent from each other, the detection outcomes’ probabilities can be expanded as a product.where subscript i runs over different detectors and are the detection outcomes from the i th detector. Similarly, when there is a common source we can expand the detection outcomes’ probabilities for a fixed source as a product for different detectors.There we used the notation for representing a possible set of sources. There can be an additional level of complication related to combinatorics if the sub-hypothesis can be satisfied with different groupings of the detections. To illustrate this, consider the sub-hypothesis of having five detected particles, three of them from a source and all the rest being noise originated. In this case the sub-hypothesis can be satisfied with combinations. In such cases we expand the probabilities aswhere the sum is over all the combinations of detection outcomes satisfying the sub-hypothesis , the sets and are the detection outcomes of individual detections from different sources and is equal to the reciprocal of the total possible combinations for arising from detector i . For example, for a sub-hypothesis with a single source and w particles emitted from that source, and total of W detections; . are found by physics and empirical data. For memoryless detectors, the detections from different sources are independent soThe likelihoods are found via the detector characteristics and emission models. Now we look at our second term in Equation ( 3 ), . We transform it by using the Bayes’ rule.The denominator of Equation ( 8 ) cancel with the same term in Equation ( 2 ). P () is the joint density of source parameters being integrated over. The sub-hypothesis probabilities are found via the expected counts from the sources or the noise origin by assuming a source density and using the empirically known noise trigger rate. 4. Use Cases—Example: A Joint Gravitational Wave—High-energy Neutrino—Short Gamma-ray Burst Event Search The method for multi-messenger searches introduced above can be used in all scenarios. Specifically, in high-energy astrophysics, one can search for sources which emit more than one messenger. Those messengers can be in any wavelength in the electromagnetic spectrum, and can be neutrinos, cosmic rays, gravitational waves, or any other messenger. Joint emissions of gravitational waves, high-energy neutrinos and short gamma-rays from a binary neutron star or a neutron star black hole merger (Kimura et al. 2017 ; Berger 2014 ), or a binary black hole merger in a dense medium, such as an AGN disk, or surrounded by an accretion disk can be such examples (Ford et al. 2019a , 2019b ). In this section, we