Datasets:
ReadingTimeMachine
/
rtm-sgt-ocr-v1

Dataset card Data Studio Files Community
1
source
stringlengths
1
2.05k
⌀
target
stringlengths
1
11.7k
This is illustrated in the rightmost panel of 66 in part I: Bolocam-detected fragments in NGC1333 cover a wide range in mass.
This is illustrated in the rightmost panel of 6 in part I: Bolocam-detected fragments in NGC1333 cover a wide range in mass.
In Ophiuchus, when examining larger spatial scales, fragments containing L1688 continue to be the most massive ones.
In Ophiuchus, when examining larger spatial scales, fragments containing L1688 continue to be the most massive ones.
This is not exactly true for NGC1333 in Perseus, though.
This is not exactly true for NGC1333 in Perseus, though.
In this cloud, when considering extinction maps, other fragments (drawn in red) are—by a small margin—the most massive ones at given radius.
In this cloud, when considering extinction maps, other fragments (drawn in red) are—by a small margin—the most massive ones at given radius.
However, closer inspection presented here in detail) reveals that these fragments all (notcontain the cluster IC348.
However, closer inspection (not presented here in detail) reveals that these fragments all contain the cluster IC348.
In summary, cluster-forming fragments do thus indeed constitute the most massive cloud features at given radius.
In summary, cluster-forming fragments do thus indeed constitute the most massive cloud features at given radius.
Some cluster-bearing fragment, which constitutes the most massive cloud fragment at some radius, might however at a different radius be less massive than some other cluster-forming fragment.
Some cluster-bearing fragment, which constitutes the most massive cloud fragment at some radius, might however at a different radius be less massive than some other cluster-forming fragment.
In this context, note that IC348 is probably much older than NGC1333 (e.g., ?)).
In this context, note that IC348 is probably much older than NGC1333 (e.g., \citealt{gutermuth2009:cluster-survey}) ).
Dense star-forming cores, which manifest as small objects of large mass, are thus actually not expected to remain in the IC348 region.
Dense star-forming cores, which manifest as small objects of large mass, are thus actually not expected to remain in the IC348 region.
Figure 3 compares cluster-forming cloud fragments to clouds devoid of significant clusters.
Figure \ref{fig:mostmassive} compares cluster-forming cloud fragments to clouds devoid of significant clusters.
For Taurus and the Pipe Nebula, the mass-size data is presented as done before (e.g., reffig:cloud-sample)).
For Taurus and the Pipe Nebula, the mass-size data is presented as done before (e.g., \\ref{fig:cloud-sample}) ).
We stress again that the Taurus MAMBO data for small spatial scales does not cover the entire cloud.
We stress again that the Taurus MAMBO data for small spatial scales does not cover the entire cloud.
For Perseus and Ophiuchus, we choose a different plotting scheme.
For Perseus and Ophiuchus, we choose a different plotting scheme.
Here, we only plot the data for fragments containing the most massive fragment found at small radii (NGC1333 in Perseus, L1688 in Ophiuchus).
Here, we only plot the data for fragments containing the most massive fragment found at small radii (NGC1333 in Perseus, L1688 in Ophiuchus).
We find that, at given radius, cluster-forming Ophiuchus cloud fragments (i.e., towards L1688) are significantly more massive than those in Taurus and Pipe.
We find that, at given radius, cluster-forming Ophiuchus cloud fragments (i.e., towards L1688) are significantly more massive than those in Taurus and Pipe.
The naive expectation (i.e., that cluster-forming fragments are more massive) is thus confirmed.
The naive expectation (i.e., that cluster-forming fragments are more massive) is thus confirmed.
For Perseus towards however, the result is more nuanced.
For Perseus (i.e., towards NGC1333), however, the result is more nuanced.
(i.e., At r<0.3pc, NGC1333),cluster-forming fragments are significantly more massive than any structure in Taurus or the Pipe Nebula.
At $r \lesssim 0.3 ~ \rm pc$, cluster-forming fragments are significantly more massive than any structure in Taurus or the Pipe Nebula.
For 0.4<r/pcS2, however, the extinction-derived mass towards NGC1333 is similar to the maximum extinction-based masses for Taurus and the Pipe Nebula.
For $0.4 \lesssim r / {\rm pc} \lesssim 2$, however, the extinction-derived mass towards NGC1333 is similar to the maximum extinction-based masses for Taurus and the Pipe Nebula.
If true, this would suggest that some
If true, this would suggest that some
fluctuations (/? if we consider that £=D/R 1) can be decomposed into multiplicative contributions from each boundary.
fluctuations $R$ if we consider that $\xi=D/R-1$ ) can be decomposed into multiplicative contributions from each boundary.
Following this model. we should find that the relative effect of redshift-space distortions on each population. and their correlations. are simply proportional to the number of redshift-space boundaries present.
Following this model, we should find that the relative effect of redshift-space distortions on each population, and their cross-correlations, are simply proportional to the number of redshift-space boundaries present.
If we choose galaxies from a sample with »©(0.1.2) redshift-space boundaries. and another from a sample (possibly the same one) with n:€(0.1.2} redshift-space boundaries. then expected correlation function is given by where/2m|n.
If we choose galaxies from a sample with $n\in\{0,1,2\}$ redshift-space boundaries, and another from a sample (possibly the same one) with $m\in\{0,1,2\}$ redshift-space boundaries, then expected correlation function is given by where $l=m+n$.
Fig.
Fig.
HE. displays the cross-correlations between the our three HV subsamples.
\ref{fig:crossABC} displays the cross-correlations between the our three HV subsamples.
As expected. the model calculated using the appropriate £" from Eq. (369)
As expected, the model calculated using the appropriate $\xi^h$ from Eq. \ref{eq:xi_h}) )
is the closest match to the measured cross-correlation in every case.
is the closest match to the measured cross-correlation in every case.
All of the models do over-predict all three measurements at large scales. but we believe this is reflective of the error associated with our measurements (one would expect it to be covariant between each sample as they all sample the same density field).
All of the models do over-predict all three measurements at large scales, but we believe this is reflective of the error associated with our measurements (one would expect it to be covariant between each sample as they all sample the same density field).
It is possible that we are seeing effects caused by the coherence of the boundaries with each other that would be removed for wider bins. such as those we consider in Section 6..
It is possible that we are seeing effects caused by the coherence of the boundaries with each other that would be removed for wider bins, such as those we consider in Section \ref{sec:des}.
Given a hybrid selection function such as that shown in Fig. 9..
Given a hybrid selection function such as that shown in Fig. \ref{fig:sch_phi_real_red},
we must split the sample into populations where we ean assume simple boundary conditions for each.
we must split the sample into populations where we can assume simple boundary conditions for each.
In fact. we can consider solving the projection equation (e.g. Eqns.
In fact, we can consider solving the projection equation (e.g. Eqns.
2 9 in real-space and redshift-space) by Monte-Carlo integration over pairs of radial galaxy locations.
\ref{eq:xi_proj_real} \ref{eq:xi_proj_red} in real-space and redshift-space) by Monte-Carlo integration over pairs of radial galaxy locations.
For each pair of locations we can determine the relative contributions from galaxies in each of the subsamples. and therefore construct a full model for the correlation function.
For each pair of locations we can determine the relative contributions from galaxies in each of the subsamples, and therefore construct a full model for the correlation function.
A number of extremely wide angle imaging surveys are planned over the neat few years: the Dark Energy Survey (DES). the Panoramic Survey Telescope Rapid Response System (Pan-Starrs) and the Large Synoptic Survey Telescope (LSST).
A number of extremely wide angle imaging surveys are planned over the next few years: the Dark Energy Survey (DES), the Panoramic Survey Telescope Rapid Response System (Pan-Starrs) and the Large Synoptic Survey Telescope (LSST).
One goal of these surveys is to constrain the current acceleration of the Universe.
One goal of these surveys is to constrain the current acceleration of the Universe.
In general. one can hope to use such surveys to make four measurements of dark energy using complimentary techniques: cluster counting. BAO. weak lensing and supernovae.
In general, one can hope to use such surveys to make four measurements of dark energy using complimentary techniques: cluster counting, BAO, weak lensing and supernovae.
In this paper we consider BAO measurements.
In this paper we consider BAO measurements.
For these experiments. radial distances to galaxies will be estimated from photometric redshifts. so there will be little information in the radial direction on the scale of BAO.
For these experiments, radial distances to galaxies will be estimated from photometric redshifts, so there will be little information in the radial direction on the scale of BAO.
Consequently. analyses will tend to rely on making projected galaxy elustering measurements in redshift slices that are sufficiently narrow to be able to reveal cosmological acceleration.
Consequently, analyses will tend to rely on making projected galaxy clustering measurements in redshift slices that are sufficiently narrow to be able to reveal cosmological acceleration.
In order to assess the effect of redshift-space distortions on such measurements. we now consider one of these surveys. DES. in more detail.
In order to assess the effect of redshift-space distortions on such measurements, we now consider one of these surveys, DES, in more detail.
The DES will use a 500 Mega-pixel camera on the Blanco 4-metre telescope in Chile to conduct a galaxy survey over a sky area of ddeg?.
The DES will use a 500 Mega-pixel camera on the Blanco 4-metre telescope in Chile to conduct a galaxy survey over a sky area of $^2$.
Multi-band observations using and zfilters will allow photometric redshifts to be obtained over a range θ«ο<LA.
Multi-band observations using and filters will allow photometric redshifts to be obtained over a range $0.2<z<1.4$.
The expected redshift distribution of the galaxies will be approximately after applying approximate survey depths to basic luminosity functions.
The expected redshift distribution of the galaxies will be approximately after applying approximate survey depths to basic luminosity functions.
This function is plotted in Fig. 12..
This function is plotted in Fig. \ref{fig:des-phi}.
This distribution of galaxies will then be sub-divided into bins in order to assess the evolution of the BAO scale across the survey.
This distribution of galaxies will then be sub-divided into bins in order to assess the evolution of the BAO scale across the survey.
As discussed above. measurements of the projected correlation function will be affected by redshift-space distortions. which will increase the
As discussed above, measurements of the projected correlation function will be affected by redshift-space distortions, which will increase the
winds to the dark matter (e.g.Binney.Gerhard.&Silk2001).. but models of this sort have had mixed success.
winds to the dark matter \citep[e.g.][]{binney01}, but models of this sort have had mixed success.
The dSph galaxies (e.g.Mateo1998) surrounding the Milkv Wavy provide a good laboratory in which to test CDM predictions on small scales: most are sufficiently nearby {ο allow us to measure the velocities of hundreds of member stars. and most have such meagre barvonic content that their internal dynamics is evervwhere dominated by the dark matter.
The dSph galaxies \citep[e.g.][]{mateo98} surrounding the Milky Way provide a good laboratory in which to test CDM predictions on small scales: most are sufficiently nearby to allow us to measure the velocities of hundreds of member stars, and most have such meagre baryonic content that their internal dynamics is everywhere dominated by the dark matter.
Although it has argued for some time that CDM greatly. overpredicts the number of cwarls. some recent work (Stoehretal.2002). suggests that CDM is actually in good agreement with (he local dSph population.
Although it has argued for some time that CDM greatly overpredicts the number of dwarfs, some recent work \citep{stoehr02} suggests that CDM is actually in good agreement with the local dSph population.
Ursa Minor is. with Draco. one of the (wo most dark matter dominated dSphs of the Local Group. with a central mass to lieht ratio V/L~10M./L. (LLargreavesetal.1994:Armandrol.Olszewski.&Pryor1995).
Ursa Minor is, with Draco, one of the two most dark matter dominated dSphs of the Local Group, with a central mass to light ratio $M/L\sim 70 M_\odot/L_\odot$ \citep{HargreavesUMi, arm95}.
. UM's stars appear to have been formed in a single burst (Carreraοἱal.2002)... ancl ils size. stellar velocity dispersion. and Iuminositv resemble (hose of Draco. which has been shown to have a mass to light ratio M/L~400AL./£. within three core radii (IXlevnaetal.2001).
UMi's stars appear to have been formed in a single burst \citep{carrera02}, and its size, stellar velocity dispersion, and luminosity resemble those of Draco, which has been shown to have a mass to light ratio $M/L\sim400 M_\odot/L_\odot$ within three core radiii \citep{kleyna01}.
. Unlike most other dSphs. however. UAL has substantial morphological distortions: UMi is highly elongated. ancl appears to possess a secondary clump or shoulder on the northeast side of the major axis (Irwin&Hatidimitriou1995:IXlexiiaetal.1998:Palma2002)...
Unlike most other dSphs, however, UMi has substantial morphological distortions: UMi is highly elongated, and appears to possess a secondary clump or shoulder on the northeast side of the major axis \citep{IrHatz95, kleyna98, PalmaUMi}.
It 15 often argued that this chunp cannot be a persistent feature because the 2xLO’ vear stellar crossing; time of the system is orders of magnitude shorter (han its ~107 vear age. so that stellar orbits within an unbound chunp will diverge over hundreds of dSph crossines.
It is often argued that this clump cannot be a persistent feature because the $\sim 2\times10^7$ year stellar crossing time of the system is orders of magnitude shorter than its $\sim 10^{10}$ year age, so that stellar orbits within an unbound clump will diverge over hundreds of dSph crossings.
UMis relatively circular orbit (Schweitzerοἱal.2003) weighs against the common explanation that the chunp is a temporary artefact generated through ongoing tidal disruption by the Galaxy.
UMi's relatively circular orbit \citep{schweitzer03} weighs against the common explanation that the clump is a temporary artefact generated through ongoing tidal disruption by the Galaxy.
Generally. models that attempt to explain the dSphlis. large velocity dispersions bv tidal disruption still require dark matter. or else must postulate an unseen supply of dSphs to replace those that awe disrupted (Oh.Lin.&Aarseth1995)... or require that dSphs are disintegrated remnants viewed along a very Iortuitous line of sight (Ixroupa1997).
Generally, models that attempt to explain the dSphs' large velocity dispersions by tidal disruption still require dark matter, or else must postulate an unseen supply of dSphs to replace those that are disrupted \citep{oh95}, or require that dSphs are disintegrated remnants viewed along a very fortuitous line of sight \citep{kroupa97}.
. In this paper. we use a data set consisting of new and extant UAL stellar velocities to argue (hal (he second peak in UMis stellar distribution has a cold kinematical signature. ancl we demonstrate that a plausible explanation is (hat the clump is a disrupted stellar cluster sloshing back and forth within UMi's halo.
In this paper, we use a data set consisting of new and extant UMi stellar velocities to argue that the second peak in UMi's stellar distribution has a cold kinematical signature, and we demonstrate that a plausible explanation is that the clump is a disrupted stellar cluster sloshing back and forth within UMi's halo.
We use numerical simulations ancl analytical argumentis to show that persistent substructure is consistent wilh a cored halo. but not with a cusped CDM halo.
We use numerical simulations and analytical arguments to show that persistent substructure is consistent with a cored halo, but not with a cusped CDM halo.
In Alay 2002. we obtained spectra of 623 stars in UAL using the WYFFOS multifibre
In May 2002, we obtained spectra of 63 stars in UMi using the WYFFOS multifibre
Primarily in. rotation analyses. sunspots have long been used as tracers of solar-surface streams.
Primarily in rotation analyses, sunspots have long been used as tracers of solar-surface streams.
The covariance of latitudinal and longitudinal motions were first investigated by Ward (1965). using sunspot data from the Greenwich Photoheliographic Results (GPR) for the years 1935-1944.
The covariance of latitudinal and longitudinal motions were first investigated by Ward (1965), using sunspot data from the Greenwich Photoheliographic Results (GPR) for the years 1935-1944.
He calculated the (vjv4)? covariances and reported positive and negative values for the northern and southern. solar hemispheres. respectively.
He calculated the $\langle v_{\theta} v_{\phi} \rangle$ covariances and reported positive and negative values for the northern and southern solar hemispheres, respectively.
This implies equatorward shifts that are produced by positive longitudinal velocities (1). because 9 is defined to be the polar angle of the considered feature as measured from the north pole.
This implies equatorward shifts that are produced by positive longitudinal velocities $(v_{\phi})$ , because $\theta$ is defined to be the polar angle of the considered feature as measured from the north pole.
Based on Sac Peak data. this result was confirmed by Coffey and Gilman (1969)). who also published a plot demonstrating the latitudinal growth of covariances with no distinction between the northern and southern hemispheres.
Based on Sac Peak data, this result was confirmed by Coffey and Gilman \cite{coffey}) ), who also published a plot demonstrating the latitudinal growth of covariances with no distinction between the northern and southern hemispheres.
Gilman and Howard (1984)) analysed Mount Wilson sunspot data for a 62 year period and reported that covariances derived for individual sunspots are smaller (by about 60%) than those derived for sunspot groups.
Gilman and Howard \cite{gilman}) ) analysed Mount Wilson sunspot data for a 62 year period and reported that covariances derived for individual sunspots are smaller (by about $\%$ ) than those derived for sunspot groups.
Howard (1991)) and Pulkkinen and Tuominen (1998)) reported almost linear latitudinal variation of covariance for <40° latitudes.
Howard \cite{howard}) ) and Pulkkinen and Tuominen \cite{pulkkinen}) ) reported almost linear latitudinal variation of covariance for $< 40^{o}$ latitudes.
Nesme-Ribes et al. (1993))
Nesme-Ribes et al. \cite{nesme-ribes}) )
measured no significant covariance by using Meudon sunspot measurements for a period of eight years (1977-1984). which contradicts the results of all other studies.
measured no significant covariance by using Meudon sunspot measurements for a period of eight years (1977-1984), which contradicts the results of all other studies.
Several authors have used other tracers. such as chromospheric features observed in Call lines.
Several authors have used other tracers, such as chromospheric features observed in CaII lines.
Belvedere et al. (1976))
Belvedere et al. \cite{belvedere}) )
argued that faculae observed in the K line are more reliable tracers than sunspots. although their positions are somewhat more ambiguous; they analysed the 1967-70 Catania observations and detected similar behaviour to that reported by Howard (1991)) and Pulkkinen and Tuominen (1998)).
argued that faculae observed in the K line are more reliable tracers than sunspots, although their positions are somewhat more ambiguous; they analysed the 1967-70 Catania observations and detected similar behaviour to that reported by Howard \cite{howard}) ) and Pulkkinen and Tuominen \cite{pulkkinen}) ).
Call observations were used by Schrótter and Wóhhl (1976)): they traced the motion of bright mottles and found that their circulation. pattern. displayed giant cell motions that could be described by (v/v4) covariances.
CaII observations were used by Schrötter and Wöhhl \cite{schroter}) ): they traced the motion of bright mottles and found that their circulation pattern displayed giant cell motions that could be described by $\langle v_{\theta} v_{\phi} \rangle$ covariances.
In the above works. the (14) values are approximately 1027/57. but the values derived from Call data are higher than those measured from sunspot data.
In the above works, the $\langle v_{\theta} v_{\phi} \rangle$ values are approximately $10^{3} m^{2}/s^{2}$, but the values derived from CaII data are higher than those measured from sunspot data.
Vr&nnak et al. (2003))
Vršnnak et al. \cite{vrsnak}) )
detected covariances even in the corona but only for young point-like structures. indicating their anchorage in deeper layers.
detected covariances even in the corona but only for young point-like structures, indicating their anchorage in deeper layers.
Most of the papers cited above measured azimuthal. meridional covariances or correlations using more general terms called the (vj) turbulent-velocity covariances. also referred to as Reynolds stresses. where v; and v; are the orthogonal velocity components.
Most of the papers cited above measured azimuthal, meridional covariances or correlations using more general terms called the $\langle v_{i} v_{j} \rangle$ turbulent-velocity covariances, also referred to as Reynolds stresses, where $v_{i}$ and $v_{j}$ are the orthogonal velocity components.
It is assumed that the sources of Reynolds stresses are turbulent. giant convection cells that interact with the solar rotation by means of the Coriolis force.
It is assumed that the sources of Reynolds stresses are turbulent, giant convection cells that interact with the solar rotation by means of the Coriolis force.
The Reynolds stresses were held mostly responsible for equatorward momentum transport (Ward (1965)). Riiddiger et al. (1998))).
The Reynolds stresses were held mostly responsible for equatorward momentum transport (Ward \cite{ward}) ), Rüddiger et al. \cite{ruediger}) )),
re. for the maintenance of differential rotation.
i.e. for the maintenance of differential rotation.
Some authors estimated the equatorward momentum flux using covariance data (Pulkkinen and Tuominen. 1998.. Paterno et al. 1991)).
Some authors estimated the equatorward momentum flux using covariance data (Pulkkinen and Tuominen, \cite{pulkkinen}, Paternò et al., \cite{paterno}) ).
D'Silva and Howard (1995)) presented an alternative approach and argues that Reynolds stresses are not indispensable in explaining the covariance values: sunspots affected by the Coriolis force may produce similar result. without any turbulent convection pattern.
D'Silva and Howard \cite{d'silva}) ) presented an alternative approach and argues that Reynolds stresses are not indispensable in explaining the covariance values: sunspots affected by the Coriolis force may produce similar result, without any turbulent convection pattern.
Spatial- and temporal-feature properties. such as long-term latitudinal distributions. have been reported. with differences being apparent between hemispheres. and. in some cases. a size dependence on sunspot evolutionary phase being found.
Spatial- and temporal-feature properties, such as long-term latitudinal distributions, have been reported, with differences being apparent between hemispheres, and, in some cases, a size dependence on sunspot evolutionary phase being found.
We study the cycle dependence of longitudinal and latitudinal motions. where we intend to model temporal behaviour. 1.e. its variation during the activity cycle.
We study the cycle dependence of longitudinal and latitudinal motions, where we intend to model temporal behaviour, i.e. its variation during the activity cycle.
We adopt observational data from the Debrecen Photoheliographic Data. the DPD. Gyérri et al. (2007)).
We adopt observational data from the Debrecen Photoheliographic Data, the DPD, Győrri et al. \cite{dpd}) ),
which covers 13 years from 1986 until. 1998. and practically the entire solar cycle 22.
which covers 13 years from 1986 until 1998, and practically the entire solar cycle 22.
This catalogue contains position and area data of all observable sunspots on a daily basis and data on sunspot groups. that is the total area of spots in the group and the position of their centre of weight.
This catalogue contains position and area data of all observable sunspots on a daily basis and data on sunspot groups, that is the total area of spots in the group and the position of their centre of weight.
It is the most suitable source for sunspot studies in cycle 22.
It is the most suitable source for sunspot studies in cycle 22.
The present analysis uses sunspot-group data that may cause a reliability problem. according to Gilman and Howard (1984)).
The present analysis uses sunspot-group data that may cause a reliability problem, according to Gilman and Howard \cite{gilman})).
These authors argue (following the unpublished criticism of Leighton) that the longitudinal and latitudinal motions of a sunspot group may be produced by morphological
These authors argue (following the unpublished criticism of Leighton) that the longitudinal and latitudinal motions of a sunspot group may be produced by morphological
the source. except for sources at the edge of the field. where the nearest kernel was used. without interpolation.
the source, except for sources at the edge of the field, where the nearest kernel was used, without interpolation.
Subsequently. each kernel was convolved with the PSF extracted from the guide star. 77. and added to the artificial image at given positions and with a given flux.
Subsequently, each kernel was convolved with the PSF extracted from the guide star, 7, and added to the artificial image at given positions and with a given flux.
In this way. an image with a smoothly varying PSF was created.
In this way, an image with a smoothly varying PSF was created.
The diffuse emission was set to a constant value.
The diffuse emission was set to a constant value.
The fluxes and positions of the point sources were taken from the image of dither position 3.
The fluxes and positions of the point sources were taken from the image of dither position 3.
Gaussian readout noise and Poisson noise were added and the number of averaged exposures was chosen to coincide with the corresponding values of the data (see refTab:Obs)).
Gaussian readout noise and Poisson noise were added and the number of averaged exposures was chosen to coincide with the corresponding values of the data (see \\ref{Tab:Obs}) ).
PSF fitting photometry was performed in four different ways: (a) by extracting the PSF from the guide star. 77. and fitting the entire image with this single PSF: (b) marking ~200 reference stars over the entire FOV. local PSF fitting by creating local PSFs via extraction of PSF cores in overlapping subframes of size ~6.97x6.97. followed by merging with PSF wings from guide star. as described in refsec:spatial:: (c) Wiener deconvolution with the PSF extracted from 77 followed by local extraction and fitting of PSFs: (d) as (c). but using LR deconvolution.
PSF fitting photometry was performed in four different ways: (a) by extracting the PSF from the guide star, 7, and fitting the entire image with this single PSF; (b) marking $\sim200$ reference stars over the entire FOV, local PSF fitting by creating local PSFs via extraction of PSF cores in overlapping subframes of size $\sim6.9"\times6.9"$, followed by merging with PSF wings from guide star, as described in \\ref{sec:spatial}; (c) Wiener deconvolution with the PSF extracted from 7 followed by local extraction and fitting of PSFs; (d) as (c), but using LR deconvolution.
The astrometry and photometry of the recovered point sources was finally compared with the input values.
The astrometry and photometry of the recovered point sources was finally compared with the input values.
The extracted smooth diffuse background was compared to the input background (chosen to be a constant).
The extracted smooth diffuse background was compared to the input background (chosen to be a constant).
The results are illustrated in refFig:astrox.. 9.. 10.. and ]H..
The results are illustrated in \\ref{Fig:astrox}, \ref{Fig:photo}, \ref{Fig:dmag}, and \ref{Fig:backsim}.
The differences between input and recovered positions for the different methods are shown in refFig:astrox (only the x-axis values are shown. the y-axes values showing very similar behavior).
The differences between input and recovered positions for the different methods are shown in \\ref{Fig:astrox} (only the x-axis values are shown, the y-axes values showing very similar behavior).