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Phe Spitzer data for this region has not been published vet. and the region is not covered. by the preliminary database from the Wicle-fielcl Infrared. Survey Explorer (WISE).
The Spitzer data for this region has not been published yet, and the region is not covered by the preliminary database from the Wide-field Infrared Survey Explorer (WISE).
Instead. we use the near-infrared. excess ALdy) from? to identify the disks.
Instead, we use the near-infrared excess $\Delta (I-K)$ from \citet{1998AJ....116.1816H} to identify the disks.
In the sample plotted in Fig. 3..
In the sample plotted in Fig. \ref{f3}, ,
left panel. 82 objects have a measured ACLA). for 40 ofthem it is >0.3 mamas. considered to be a sale disk detection by 2..
left panel, 82 objects have a measured $\Delta (I-K)$ , for 40 of them it is $>0.3$ mag, considered to be a safe disk detection by \citet{1998AJ....116.1816H}.
For the right panel. S4 have à measurement. 44 of them 0.3 mmag.
For the right panel, 84 have a measurement, 44 of them $>0.3$ mag.
"Thus. the disk fraction in. the samples plotted in Fig.
Thus, the disk fraction in the samples plotted in Fig.
3.2 is in the range of504.
\ref{f3} is in the range of.
. For two of the four variables from Table 1. the near-infrared. excess is available in. 2..
For two of the four variables from Table \ref{var} the near-infrared excess is available in \citet{1998AJ....116.1816H}.
One of them clearly has a disk (COUP 236). the other not (COUP 64).
One of them clearly has a disk (COUP 236), the other not (COUP 64).
Lack of near-infrarecl excess does not necessarily imply. the lack of circumstellar material. Le. COUP 64 might still harbour a disk.
Lack of near-infrared excess does not necessarily imply the lack of circumstellar material, i.e. COUP 64 might still harbour a disk.
Vhree of the four objects listed in Table 1 COUP 1425. 64. 236 are also identified as variables hy 2..
Three of the four objects listed in Table \ref{var} – COUP 1425, 64, 236 – are also identified as variables by \citet{2001AJ....121.3160C}.
In summary. the total fraction of objects with strong near-infrared variability on timescales of several vears [or the ONC is in the range of for all objects and for objects with clisks.
In summary, the total fraction of objects with strong near-infrared variability on timescales of several years for the ONC is in the range of for all objects and for objects with disks.
Lor I€348. a ~ 3MMsvr old. cluster in the Perseus star forming region. L start with the list of 288 spectroscopically confirmed. cluster members published by 2..
For IC348, a $\sim 3$ Myr old cluster in the Perseus star forming region, I start with the list of 288 spectroscopically confirmed cluster members published by \citet{2003ApJ...593.1093L}.
Most. of these objects are low-niass stars with spectral types Ix to M. Phe cluster was covered by 2ALASS from late 1998 to carly 1999 and by UALDSS/GCS in late 2006. Le. the typical epoch dillerence is Svvr.
Most of these objects are low-mass stars with spectral types K to M. The cluster was covered by 2MASS from late 1998 to early 1999 and by UKIDSS/GCS in late 2006, i.e. the typical epoch difference is$\sim 8$ yr.
249 objects have photometry in the L- and Ix-band in the two survevs. 201 of them are not allected by saturation in UINIDSS (if>11 and A 10.5).
249 objects have photometry in the H- and K-band in the two surveys, 201 of them are not affected by saturation in UKIDSS $H>11$ and $K>10.5$ ).
In Fig. 4..
In Fig. \ref{f5},
left panel. we plot the WK-band vs. 44.Acolour variability.2 objects showIx-band. variations 0.5 mmag. one of them with dA=0.8 and dif=0.5 mmag.
left panel, we plot the K-band vs. $H-K$colour variability.2 objects showK-band variations $>0.5$ mag, one of them with $dK=0.8$ and $dH=0.8$ mag.
This object is included in Table 1..
This object is included in Table \ref{var}. .
The right panel inFig.
The right panel inFig.
4. shows the same plot using
\ref{f5} shows the same plot using
when a test model with a 0.4 Zo spectral synthesis cluster model from Starburst99 embedded in a nebula with 1.0 Zo (12+log(O/H)= 8.66) is run.
when a test model with a 0.4 $Z_{\sun}$ spectral synthesis cluster model from $Starburst99$ embedded in a nebula with 1.0 $Z_{\sun}$ (12+log(O/H)= 8.66) is run.
Along the paper the solar abundance adopted refers to 12+log(O/H)= 8.69 from (2001).
Along the paper the solar abundance adopted refers to 12+log(O/H)= 8.69 from \citet{allende01}.
. These models are similar to the ones of Dors&Copetti(2006) and have been successful in describing observational data ofn regions (see Dorsetal.2008;; Krabbeetal. 2008,, Krabbeetal. 2007)).
These models are similar to the ones of \citet{dors06} and have been successful in describing observational data of regions (see \citealt{dors08}; \citealt{krabbe08}, \citealt{krabbe07}) ).
In general, grids of photoionization models are built assuming a fixed N/O-O/H relation.
In general, grids of photoionization models are built assuming a fixed N/O-O/H relation.
However, this constancy can yield large uncertainties in O/H estimates via strong-line methods (Pérez-Montero&Contini 2009).
However, this constancy can yield large uncertainties in O/H estimates via strong-line methods \citep{perez09}.
. This problem can be circumvented by the use of detailed photoionization models.
This problem can be circumvented by the use of detailed photoionization models.
To analyze the source of these uncertainties, we built detailed models in order to reproduce the observational emission line intensities of 11Hu regions (see Table 2)) located along the disk of the galaxy M1101, observed by Kennicuttetal.(2003),, and we compared our estimates with O/H and U values from other methods.
To analyze the source of these uncertainties, we built detailed models in order to reproduce the observational emission line intensities of 11 regions (see Table \ref{tab2}) ) located along the disk of the galaxy 101, observed by \citet{kennicutt03}, and we compared our estimates with O/H and $U$ values from other methods.
These objects were selected because they cover the wide range in metallicity and ionization parameter considered in this paper.
These objects were selected because they cover the wide range in metallicity and ionization parameter considered in this paper.
We computed individual models for each object adopting the following methodology.
We computed individual models for each object adopting the following methodology.
Firstly, a model for each region was built by initially guessing the Z and values derived from a comparison between the grid of photoionizationU models shown in the diagnostic diagram n] vs. n] (see Figure 2)) and the observational data.
Firstly, a model for each region was built by initially guessing the $Z$ and $U$ values derived from a comparison between the grid of photoionization models shown in the diagnostic diagram ] vs. ] (see Figure \ref{f1}) ) and the observational data.
The electron density of each model wasconsidered to be that computed utilizing the task temden of the package IRAF, where we consider the sulfur ratio n]46716/[S 1]A6731 and electron temperature for the O* ion measured by Kennicuttetal.(2003).
The electron density of each model wasconsidered to be that computed utilizing the task temden of the package IRAF, where we consider the sulfur ratio $\lambda$ $\lambda$ 6731 and electron temperature for the $\rm O^{+}$ ion measured by \citet{kennicutt03}.
. The stellar cluster was assumed to have an age of 2.5 Myr, M, = 100 Μο and metallicity was matched with the closest one nebular assumed in the models.
The stellar cluster was assumed to have an age of 2.5 Myr, $M_{\rm up}$ = 100 $M_{\odot}$ and metallicity was matched with the closest one nebular assumed in the models.
Then, we ran new models ranging the O/H and U values by 0.3 and 0.5 dex, respectively, with a step of 0.1 dex.
Then, we ran new models ranging the O/H and $U$ values by 0.3 and 0.5 dex, respectively, with a step of 0.1 dex.
From this series of models we selected a model which produced the smallest y?=Xjonyug+Xjomue Where Xi=dh.-Du[Tin D, and T are the observational and predicted intensities of the3 line ratios, ea.respectively.
From this series of models we selected a model which produced the smallest $\sum \chi_{i}^2=\chi_{[\rm O\, II]/\rm H\beta}^2 +\chi_{[\rm O\,III]/\rm H\beta}^2$, where $\chi_{i}=(I_{\rm obs.}^{i}-I_{\rm pred.}^{i})^{2}/I_{\rm obs.}^{i}$; $I_{\rm obs.}^{i}$ and $I_{\rm pred.}^{i}$ are the observational and predicted intensities of the line ratios, respectively.
Another series of models was computed considering the O/H and U values found by the criterion above but ranging the N/H and S/H abundances by 0.3 dex in order to reproduce the intensities of the 1]46584 and 1]A6720 emission lines.
Another series of models was computed considering the O/H and $U$ values found by the criterion above but ranging the N/H and S/H abundances by 0.3 dex in order to reproduce the intensities of the $\lambda$ 6584 and $\lambda$ 6720 emission lines.
The satisfactory solution is found when reproduces Joys, within the observational uncertainties and the prea,model has the smallest 47=Xjomyug+XiomngXinuyug+ Xisujug: I
The satisfactory solution is found when $I_{\rm pred.}$ reproduces $I_{\rm obs.}$ within the observational uncertainties and the model has the smallest $\sum \chi_{i}^2=\chi_{[\rm O\,II]/\rm H\beta}^2 +\chi_{[\rm O \,III]/\rm H\beta}^2 +\chi_{[\rm N \,II]/\rm H\beta}^2 +\chi_{[\rm S\, II]/\rm H\beta}^2$ .
some cases no satisfactory solution was reached considering the age of the ionizing cluster of 2.5 Myr.
In some cases no satisfactory solution was reached considering the age of the ionizing cluster of 2.5 Myr.
For these, it was necessary to assume an age of 1 Myr because their observed emission lines could only be reproduced by means of a harder spectral energy distribution.
For these, it was necessary to assume an age of 1 Myr because their observed emission lines could only be reproduced by means of a harder spectral energy distribution.
In Table 2,, we present the final parameter obtained for the models.
In Table \ref{tab2}, , we present the final parameter obtained for the models.
We employ six diagnostic diagrams containing predicted and observed emission line ratios sensitive to Z and U.
We employ six diagnostic diagrams containing predicted and observed emission line ratios sensitive to $Z$ and $U$ .
The diagrams considered are described below.
The diagrams considered are described below.
below the critical angular velocity ο). it becomes uustable aud explodes as à SN Ia 2005)).
below the critical angular velocity $\Omega_c$, it becomes unstable and explodes as a SN Ia \citealt{Yoon2005}) ).
We now check the possibility that most of the SN Ia are formed by the CD scenario. and use observations to coustrain the properties of these SN Ia progenitors.
We now check the possibility that most of the SN Ia are formed by the CD scenario, and use observations to constrain the properties of these SN Ia progenitors.
Equation (10)) for the spin-down time depends on L pliysical variables.
Equation \ref{eq:taub}) ) for the spin-down time depends on 4 physical variables.
The radius 2 aud the critical angular velocity £2 will not ciffer much from oue WD to another. as we are mainly interesteca in WDs of masses LOAD. that are rapidly rotating.
The radius $R$ and the critical angular velocity $ {\tilde{\Omega}_c}$ will not differ much from one WD to another, as we are mainly interested in WDs of masses $\sim 1.5 M_\odot$ that are rapidly rotating.
The magnetic field aud the inclination augT can vary by more than two orders of maguitucde between different WDs.
The magnetic field and the inclination angle can vary by more than two orders of magnitude between different WDs.
For that. we write the spin-down time from equation (10)) iu the form where we defined what we term the maguetic-cipole parameter We now try to use observations to estimate the distribution of WDs that are the remuauts of core-degenerate mergers with respect toa. uncer the assumption that most (all) SNe Ia are forme through this channel.
For that, we write the spin-down time from equation \ref{eq:taub}) ) in the form where we defined what we term the magnetic-dipole parameter We now try to use observations to estimate the distribution of WDs that are the remnants of core-degenerate mergers with respect to $\eta$, under the assumption that most (all) SNe Ia are formed through this channel.
Maoz et al. (
Maoz et al. (
2011: see also Grauretal. 2011.. Maozetal.2010.. 2010)) suminarize the SN Ia rate versus time — the delay time distribution (DTD) — as foun [rom observations with e2—1. aud where / is the time since the star formation event that formed the binary system that later became the SN progenitor.
2011; see also \citealt{Graur2011}, , \citealt{Maozetal2010}, \citealt{Ruiter2010}) ) summarize the SN Ia rate versus time $-$ the delay time distribution (DTD) $-$ as found from observations with $\epsilon \simeq -1$, and where $t$ is the time since the star formation event that formed the binary system that later became the SN progenitor.
The last equation cau be written as equation (11)) with 7—/. we have that when substituted iuto equation (11)) gives We recall that this result holds only for the massive WDs that were formed from the merger of two lower mass WDs duriug or shortly after a common envelope pliase under the assumption tliat most SNe Iaare formect via the CD channel.
The last equation can be written as equation \ref{eq:taub2}) ) with $\tau_{\rm B} \rightarrow t$, we have that when substituted into equation \ref{eq:dndt2}) ) gives We recall that this result holds only for the massive WDs that were formed from the merger of two lower mass WDs during or shortly after a common envelope phase under the assumption that most SNe Iaare formed via the CD channel.
Equation (16)) cau be also written explicitly as
Equation \ref{eq:dNdeta1}) ) can be also written explicitly as
and 500 shown in Figure 5, the CFRg's are 2.2x 107”, 7.8x 107%, 1.5x 1073, 2.3x107*, 6.8x1075, 2.7x107°, 1.2x10~°, respectively.
and 500 shown in Figure 5, the $_{\rm ff}$ 's are $2.2\times10^{-2}$ , $7.8\times10^{-3}$ , $1.5\times10^{-3}$ , $2.3\times10^{-4}$, $6.8\times10^{-5}$, $2.7\times10^{-5}$, $1.2\times10^{-5}$, respectively.
In order to measure the CFRg's from our numerical simulation, we make least-square fitting of each curve from t=0 to t=a3tg in Figure 5 with a straight line.
In order to measure the $_{\rm ff}$ 's from our numerical simulation, we make a least-square fitting of each curve from $t=0$ to $t=3t_{\rm ff}$ in Figure 5 with a straight line.
In fact, the slopes of those lines give us CFRa's. They are 0.048, 0.045, 0.040, 0.036, 0.033, 0.031, 0.029 for normalised critical density values, 30, 50, 100, 200, 300, 400, and 500, respectively.
In fact, the slopes of those lines give us $_{\rm ff}$ 's. They are 0.048, 0.045, 0.040, 0.036, 0.033, 0.031, 0.029 for normalised critical density values, 30, 50, 100, 200, 300, 400, and 500, respectively.
Comparison of theoretical and numerical estimates shows that the CFRg's from the numerical experiment are 2.2, 5.8, 27, 160, 490, 1100, and 2400 times larger than those from the lognormal distribution for the critical density values 30, 50, 100, 200, 300, 400, and 500, respectively.
Comparison of theoretical and numerical estimates shows that the $_{\rm ff}$ 's from the numerical experiment are 2.2, 5.8, 27, 160, 490, 1100, and 2400 times larger than those from the lognormal distribution for the critical density values 30, 50, 100, 200, 300, 400, and 500, respectively.
The difference becomes larger as the critical density increases.
The difference becomes larger as the critical density increases.
If we pick 100 up as our fiducial, normalised critical density, the CFRg based on the theory is likely to be underestimated by about a factor 30.
If we pick 100 up as our fiducial, normalised critical density, the $_{\rm ff}$ based on the theory is likely to be underestimated by about a factor 30.
Recently Kainulainenetal.(2009) catalogued column density PDFs of 23 molecular cloud complexes from the 2MASS archive.
Recently \citet{kai09} catalogued column density PDFs of 23 molecular cloud complexes from the 2MASS archive.
They classified them into two groups based on star formation activity and compared their column density PDFs with each other.
They classified them into two groups based on star formation activity and compared their column density PDFs with each other.
The column density PDFs of star-forming clouds always have extended tails, whereas the PDFs of clouds without active star formation follow lognormal distributions or a bit excess at high column densities.
The column density PDFs of star-forming clouds always have extended tails, whereas the PDFs of clouds without active star formation follow lognormal distributions or a bit excess at high column densities.
Furthermore, the cumulative fractions of column density PDFs with star-forming clouds are significantly larger than those without active star formation.
Furthermore, the cumulative fractions of column density PDFs with star-forming clouds are significantly larger than those without active star formation.
These observational results clearly show that self-gravity plays a role in forming the extended tails of the PDFs, which is consistent with our work.
These observational results clearly show that self-gravity plays a role in forming the extended tails of the PDFs, which is consistent with our work.
We remind that the extended tails of density PDFs at high densities from isothermal simulations with self-gravity have been shown in a few previous literatures.
We remind that the extended tails of density PDFs at high densities from isothermal simulations with self-gravity have been shown in a few previous literatures.
The new finding in this Letter is that the extended tails can enhance CFRa quite significantly.
The new finding in this Letter is that the extended tails can enhance $_{\rm ff}$ quite significantly.
Cores in a turbulent cloud cannot form without self-gravity.
Cores in a turbulent cloud cannot form without self-gravity.
Core (star) formation rates or core (stellar) mass functions should be measured in the context of a self-gravitating cloud.
Core (star) formation rates or core (stellar) mass functions should be measured in the context of a self-gravitating cloud.
Therefore, it is likely that core formation rates previously measured based on the lognormal density PDF (Krumholz&McKee2005;Elmegreen2008) are underestimated.
Therefore, it is likely that core formation rates previously measured based on the lognormal density PDF \citep{kru05, elm08} are underestimated.
Likewise, the core or stellar initial mass functions based on the lognormal distribution (Padoan&Nordlund2002;Hennebelle&Chabrier2008) need to be modified.
Likewise, the core or stellar initial mass functions based on the lognormal distribution \citep{pad02, hen08} need to be modified.
'There are uncertainties in our results due to numerical resolution, measuring CFRg based on only a density threshold, violation of the numerical Jeans condition, and stellar feedback.
There are uncertainties in our results due to numerical resolution, measuring $_{\rm ff}$ based on only a density threshold, violation of the numerical Jeans condition, and stellar feedback.
Because of the limited space, we briefly discuss on the last three.
Because of the limited space, we briefly discuss on the last three.
Firstly, the density threshold alone may not fully capture collapsing gas.
Firstly, the density threshold alone may not fully capture collapsing gas.
More elaborated collapse indicators are needed (for example, Federrathetal.(2010b))).
More elaborated collapse indicators are needed (for example, \citet{fed10b}) ).
Secondly, Figure 2, for example, shows that density values at a high density tail especially at later stages of our simulation go above the maximum normalised density value, 1024, (see, Equation (9) in Vázquez-Semadenietal.(2005))) constrained by the numerical Jeans condition for preventing artificial fragmentation (Trueloveetal.1997).
Secondly, Figure 2, for example, shows that density values at a high density tail especially at later stages of our simulation go above the maximum normalised density value, 1024, (see, Equation (9) in \citet{vaz05}) ) constrained by the numerical Jeans condition for preventing artificial fragmentation \citep{tru97}.
. In this Letter we are interested in not the fragmentation of cores but the total amount of mass of cores defined by a critical density.
In this Letter we are interested in not the fragmentation of cores but the total amount of mass of cores defined by a critical density.
So the violation of the condition will not change our main result but add uncertainty in the measured CFRa.
So the violation of the condition will not change our main result but add uncertainty in the measured $_{\rm ff}$.
Thirdly, we didn't include feedback processes from the stars that might form in our simulation.
Thirdly, we didn't include feedback processes from the stars that might form in our simulation.
Without the stellar feedback core formation efficiency eventually approaches one.
Without the stellar feedback core formation efficiency eventually approaches one.
However, at least, before the formation of a first star in the simulation, our measurement of the core formation efficiency is quite right.
However, at least, before the formation of a first star in the simulation, our measurement of the core formation efficiency is quite right.
In order to properly measure the core formation efficiency, especially, at the later evolutionary state of a molecular cloud, one should include the feedback.
In order to properly measure the core formation efficiency, especially, at the later evolutionary state of a molecular cloud, one should include the feedback.
We performed a magnetically supercritical, supersonic turbulence simulation with the isothermal equation of state to study the effects of self-gravity on density PDFs and the core formation rate.
We performed a magnetically supercritical, supersonic turbulence simulation with the isothermal equation of state to study the effects of self-gravity on density PDFs and the core formation rate.
Here are conclusions from the study.
Here are conclusions from the study.
First, self-gravity helps to form the extended tail of a density PDF at high densities, which significantly increases CFRe.
First, self-gravity helps to form the extended tail of a density PDF at high densities, which significantly increases $_{\rm ff}$.
Second, the normalised critical density for core collapse determined by the equal condition between the local Jeans and sonic lengths is 25Spc/po<57, which is smaller than 100, a density jump brought by an isothermal Mach 10 shock in our simulation.
Second, the normalised critical density for core collapse determined by the equal condition between the local Jeans and sonic lengths is $25 \stackrel{<}{_\sim} \rho_c/\rho_0 \stackrel{<}{_\sim} 57$, which is smaller than 100, a density jump brought by an isothermal Mach 10 shock in our simulation.
So the determined critical density may not give a correct condition for the core formation.
So the determined critical density may not give a correct condition for the core formation.
Third, for our fiducial normalised critical density, 100, CFRg= 0.045, measured from our numerical simulation is about 30 times larger than the one, 0.0015, based on the lognormal distribution.
Third, for our fiducial normalised critical density, 100, $_{\rm ff}=0.045$ , measured from our numerical simulation is about 30 times larger than the one, 0.0015, based on the lognormal distribution.
Therefore, self-gravity plays a significant role in enhancing CFRa in aturbulent cloud.
Therefore, self-gravity plays a significant role in enhancing $_{\rm ff}$ in aturbulent cloud.
The authorsthank the referee for constructive comments.
The authorsthank the referee for constructive comments.
The work of J.K. was supported by the Korea Foundation for International Cooperation of Science and Technology
The work of J.K. was supported by the Korea Foundation for International Cooperation of Science and Technology
apocentre occurs at large group (i.e., comparable to the group virial radius).
apocentre occurs at large group (i.e., comparable to the group virial radius).
However, we expect that both of these conditions are rarely fulfilled simultaneously in real systems, as massive satellites tend preferentially to fall into groups and clusters on nearly radial orbits (i.e., along filaments; see, e.g., Benson 2005).
However, we expect that both of these conditions are rarely fulfilled simultaneously in real systems, as massive satellites tend preferentially to fall into groups and clusters on nearly radial orbits (i.e., along filaments; see, e.g., Benson 2005).
Finally, we experiment with varying the internal structure of the galaxy (both its gas and dark matter) by varying its initial concentration parameter, c299 (equivalently, its scale radius, r;).
Finally, we experiment with varying the internal structure of the galaxy (both its gas and dark matter) by varying its initial concentration parameter, $c_{200}$ (equivalently, its scale radius, $r_s$ ).
This will mainly have the effect of changing the shape of the radial profile of the restoring force (per unit area).
This will mainly have the effect of changing the shape of the radial profile of the restoring force (per unit area).
This test is motivated by the fact that in cosmological simulations there is a large degree of intrinsic scatter in the concentration parameter for a system of fixed mass (e.g., Dolag et 22004; Neto et 22007).
This test is motivated by the fact that in cosmological simulations there is a large degree of intrinsic scatter in the concentration parameter for a system of fixed mass (e.g., Dolag et 2004; Neto et 2007).
Note that changing the concentration can also mimic the addition of another mass component to the galaxy, such as a stellar component (which we have neglected to include explicitly).
Note that changing the concentration can also mimic the addition of another mass component to the galaxy, such as a stellar component (which we have neglected to include explicitly).
111 shows that the concentration has a significant effect on the amount of gas that the galaxy is able to retain as it orbits about the group.
11 shows that the concentration has a significant effect on the amount of gas that the galaxy is able to retain as it orbits about the group.
As expected, as the concentration is increased so too is the bound mass of gas.
As expected, as the concentration is increased so too is the bound mass of gas.
As in the previous experiments, the simple analytic model with a= 2,0.5«B<0.7 (shown is 6= 2/3), and tram=Gtsouna matches the mass loss in the simulations very well.
As in the previous experiments, the simple analytic model with $\alpha = 2$ , $0.5 < \beta < 0.7$ (shown is $\beta = 2/3$ ), and $t_{\rm ram} = \beta t_{\rm sound}$ matches the mass loss in the simulations very well.
Using a suite of carefully controlled 3D hydrodynamic simulations, we have investigated the ram pressure stripping of hot gas in the halos of galaxies as they fall into groups and clusters.
Using a suite of carefully controlled 3D hydrodynamic simulations, we have investigated the ram pressure stripping of hot gas in the halos of galaxies as they fall into groups and clusters.
We have proposed a physically simple analytic model that describes the stripping seen in the simulations remarkably well.
We have proposed a physically simple analytic model that describes the stripping seen in the simulations remarkably well.
This model is analogous to the original formulation of Gunn Gott (1972), except that it is appropriate for the case of a spherical gas distribution (as opposed to a face-on disk) and takes into account that stripping is not instantaneous but occurs on approximately a sound crossing time.
This model is analogous to the original formulation of Gunn Gott (1972), except that it is appropriate for the case of a spherical gas distribution (as opposed to a face-on disk) and takes into account that stripping is not instantaneous but occurs on approximately a sound crossing time.