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Home > News > Woman forced by abusive husband to exercise so she wouldnt get fat
Woman forced by abusive boyfriend to exercise so she wouldn't get fat
Madeline Cox	| July 12, 2019
"I assumed it was normal. I was sure I could change him."
Smartphone apps helping victims of domestic violence00:17:25
Safety apps have been created for victims of domestic violence in an effort to make them feel safer, easier.
/display/newscorpaustralia.com/Web/NewsNetwork/Technology - syndicated/
Looking back, Gennady has no idea what made her fall head over heels in love with him straight away.
The New Zealand woman just instantly felt a connection to this man – some sense that at 27 she had finally met the man of her dreams.
Within months they had moved in together, which meant that Gennady had nowhere to go when he suddenly transformed into a completely different person.
"He started forcing me to go to the gym because he didn't want me to get fat," she first wrote on Love What Matters.
"When I say force I mean he called me every name under the sun until I was in utter tears and would leave the house.
"This was my first 'adult relationship' so I just assumed it was normal."
Domestic violence affects 1 in 3 Australian women. See the haunting photo taken the night before Judy left her abusive relationship forever or read about the horrific beating that finally gave one woman the courage to escape.
Gennady found herself quickly falling for this older man. Source: Gennady Sharpe
"Why is she not leaving him?"
Soon Gennady began to rapidly lose weight – she was forced to exercise constantly and only eat what he told her to.
He also started choosing her clothes for her as well as styling her hair.
"My friends were shocked at how small I was getting and how quickly my look changed," she said.
"They started to ask questions. 'Does he treat her OK?' 'Is he controlling her?' 'Is she happy?'
"I guess that's the thing about these kinds of relationships. On the outside looking in you're asking yourself, 'Why is she not leaving him?'
"But when you're the person involved, it's a whole new level of control that you really don't know how to escape or understand."
The information that could save your life in an abusive relationship
The domestic violence signs you never knew
What to do if you think you're at risk
He forced her to exercise and also controlled what she ate. Source: Gennady Sharpe
"This was all my fault"
He hit Gennady for the first time a short time later – the terrified woman curling into a ball on the floor in an attempt to shield herself from the violent blows.
"I remember the feeling of utter fear as I looked in his eyes and couldn't see him," she said.
"I remember laying in the foetal position on the ground until it was over and looking at him, telling him I was sorry.
"This was all my fault.
"I sat next to him, holding him, begging him not to be mad at me.
"I know what you're thinking. If that was me, I would leave him straight away … I used to think I would, too."
Soon Gennady began to rapidly lose weight - even her friends noticed something was wrong. Source: Gennady Sharpe
"I was so sure I could help and change him"
But the reality was so much more complicated than that – Gennady desperately believed she could bring back the man she had fallen in love with.
"I pushed everyone close to me away, anyone who might be able to see through my lies and the layers of makeup to cover the bruises," she said.
"If people knew, they would tell me to leave him and I was not ready for that at all.
"I was so sure I could help and change him. I did everything in my power.
"But every step I took to try to help him was a step that meant that I got more and more hurt mentally and physically.
"He put me down so much that I believed these things about myself and thought, 'If I left, who would want someone like me anyway?'"
He hit Gennady for the first time not long after moving in together. Source: Gennady Sharpe
"How had I hidden this from them?"
It wasn't until Gennady's ribs and hand were broken that she finally realised she needed to leave.
She fled, finally confessing to her friends and family the truth she had desperately tried to hide from them.
"I felt utterly sick. How had I hidden this from them?" she said.
"My mum and dad were just completely and utterly hurt and broken for me.
"But none of these people were mad; they were proud of me.
"This was not the emotion I expected. They were proud of me for having the courage to leave and for telling them."
"But none of these people were mad; they were proud of me." Source: Gennady Sharpe
"I felt other people needed me to be strong"
But leaving didn't fix Gennady's shattered self-esteem or the constant anxiety she felt every single day.
It wasn't until she signed up for the reality show Heartbreak Island that everything started to turn around.
"My confidence was already knocked, but I wanted a platform to spread awareness for domestic violence," she said.
"I felt other people needed me to be strong.
"I wanted to make sure this happened less and less.
"I want to be someone young girls and women look up to.
"I want them to know just how beautiful they are, and I wanted to be honest with my life struggles."
"I think when you are truly ready for love, the right kind of love, it finds you". Source: Gennady Sharpe
"He's the best thing that has ever happened to me"
Gennady's story doesn't end there, in fact, she's since finally met the real man of her dreams.
It happened on a night out with friends after she had run into her ex who tried to confront her.
Gennady ran, jumping into the first taxi she saw, as tears streamed down her face, when she discovered a large wallet on the seat.
"I put the name on my Instagram saying, 'Does anyone know this lady. I have found her wallet," she said.
"She called me five minutes later, absolutely overwhelmed that somebody was returning her lost wallet.
"She gave me her son's number to contact him and arrange for him to collect it from me.
"That night, there was a knock on my door and a brief chat. Now, one year later, he is my partner and the best thing that has ever happened to me.
"I think when you are truly ready for love, the right kind of love, it finds you. Just like this modern-day Cinderella wallet."
Follow Gennady's journey on Instagram and her blog
If you or someone you know is experiencing violence at home, help is available. Contact 1800 RESPECT, White Ribbon or alternatively, click here to see a variety of support services available by state. If you believe you are in immediate danger, please contact police on 000.
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QUIZ: What is your family's holiday type


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President's Page
Don Quixote Award
ASHP Lifetime Achievement Award
PRMS Resources
Hispano-American Research and Training Collaborations
Maruicio Tohen ASHP President 2015
16:15-16:35 CSF Biomarkers and Neuroimaging in First Episode Psychosis – Mexico
Juan A. Gallego, Camilo De La Fuente-Sandoval
Patients presenting with first episode psychosis who have not been exposed to antipsychotic medications will undergo a lumbar puncture, brain scan and clinical ratings. The relationship between CSF biomarkers (miRNAs), neuroimaging and clinical variables will be investigated.
16:35-16:55 Training and Research Collaborations at Mount Sinai in NY: Web-based Assessments, Data Sharing and International Genetics Consortia – Spain
M. Mercedes Perez-Rodriguez, Monica Prat Galbany, WORECA (World Research Consortium for Suicide)
Description of the Visiting Researchers Program at the Mood and Personality Disorders Program at Mount Sinai, NY. Establishment of international collaborations with Spanish research programs: 1) Borderline Personality Disorder International Genetics Consortium; 2) Comparing symptom and comorbidity profiles in borderline personality disorder in Spain and the US; Web-based assessments and cross-national data sharing: MeMind Project and WORECA.
16:55-17:15 BMI and Antipsychotic Exposure – Mexico and Colombia
Bernardo Ng, Alvaro Camacho, Mirna Trancoso, Severiano Lozano, Katherine Parra, Perla Orozco, Ricardo de la Espriella, Ignacio Calderon
Clinical and demographic data from psychiatric patients of a total of 4 different clinical sites in Mexico and Colombia exposed to antipsychotics will be presented. Differences in BMI according to age, gender, and geographical location will be discussed.
17:30-17:50 Funding Strategies for Rotations in the US – Mexico
Mauricio Tohen
Rotation of Mexican psychiatry residents at the University of New Mexico will be presented. The mechanism of this successful strategy will be discussed by the chairman of the Department of Psychiatry at this University.
17:50-18:10 US and Latin American Academic Collaborations – Latin America
Manuel Sanchez de Carmona
For 10 years a successful Latin American collaboration has developed as a strategic research, educational and clinical experience network in Bipolar Disorders between Argentina, Brazil, Chile, Colombia, Mexico, Peru, Venezuela and key opinion leaders from Spain and the Hispanic medical community of the USA, under the auspice of the International Society for Bipolar Disorders (ISBD). The result of this interaction has given fruits on many levels and has given strength to the global presence of the Latin mood disorders psychiatric community. This initiative has empowered young investigator's careers, stimulated research collaborations and the synergy to create local and regional academic meetings.
18:10-18:30 The International Field Trial of DSM-5 Cultural Formulation Interview: Key Lessons and Preliminary Findings from the Peru-US Collaboration – Peru
Oscar Jiménez- Solomon, Renato Alarcon, Roberto Lewis-Fernandez
The international field trials of the DSM-5 Cultural Formulation (CFI) included a collaboration between the New York State Psychiatric Institute (United States) and the Universidad Peruana Cayetano Heredia (Peru) to assess the feasibility, acceptability and perceived clinical utility of the CFI in a clinical sample in Lima, Peru. This workshop will describe key lessons from this collaboration and describe preliminary findings.
18:30-18:50 An Overview of Clinical Translational Research and Training at Intramural NIMH – Latin America
Carlos Zarate
This presentation will summarize ongoing research strategies and training opportunities being conducted at the Division of the Intramural Research Program (DIRP) on the Bethesda campus of NIMH. Proof-of-concept studies utilizing novel compounds, biomarkers, and technologies (magnetoencephalography [MEG] and polysomnography [PSG], positron emission tomography, functional MRI, magnetic resonance spectroscopy [MRS]), metabolomics, proteonomics and genetics are being utilized to identify potentially relevant drug targets and biosignatures of pathophysiology and treatment response. A multidisciplinary translational research team conducts such research. In addition, DIRP provides training to develop the next generation of clinical translational researchers.
18:50-19:30 Feedback and Discussion
Also join us for the:
Simon Bolivar Award Lecture
Sunday, May 17th
8am to 9:30 am
Toronto Convention Center, Room 205 A-D, North Level 200
Lecturer: Carlos Zarate, MD
© 2020 American Society of Hispanic Psychiatry

redpajama_set_name: RedPajamaCommonCrawl


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Network Security and Multi-Factor Authentication Solutions from WatchGuard Earn TrustRadius 2020 Top Rated Awards
Users have identified WatchGuard as a leading vendor among a broad field of firewall and MFA providers, while TrustRadius also recognizes the company's COVID-19 response
SEATTLE – September 23, 2020 – WatchGuard® Technologies, a global leader in network security and intelligence, secure Wi-Fi, multi-factor authentication and endpoint security, today announced that TrustRadius has recognized its Network Security solutions with a 2020 Top Rated Award. The TrustRadius community ranked WatchGuard as a valuable player in the Firewall Solutions category with trScore of 9.2 out of 10 from 140 verified reviews. WatchGuard's AuthPoint service was also recognized with a 2020 Top Rated MFA Award in the Authentication Systems category. Additionally, the company has earned a TrustRadius 2020 Tech Cares Award for its ongoing efforts to go above and beyond in supporting customers, partners and employees during the COVID-19 crisis.
"The TrustRadius community's overwhelmingly positive reviews validate the quality, sophistication and ease of use of our network security and multi-factor authentication solutions," said Michelle Welch, SVP of Marketing at WatchGuard. "By consolidating these and other vital security solutions under a single platform, we're continuing to simplify protection for customers and their trusted channel partners."
Receiving these two Top Rated Awards from TrustRadius further emphasizes WatchGuard's leadership in the security space and commitment to making enterprise-grade security accessible to all companies through simplicity.
Since 2016, the TrustRadius Top Rated Awards have become an industry standard for unbiased recognition of B2B technology products. This annual award program is based entirely on customer feedback and strict scoring system based on user reviews and ratings.
Here are several reviews from verified users that contributed to WatchGuard's TrustRadius Top Rated awards:
"WatchGuard is always innovating their security suite and adding and improving features to their perimeter security devices," said a division network support specialist at an international charitable organization. "Each iteration of the firewall appliance is uniquely outfitted with the appropriate tools to both enable data communication and protect the local network. Scalable and reliable, WatchGuard security devices simply work."
"We use WatchGuard Total Security Suite in our office. It provides our network with perimeter security that is always constant and vigilant in keeping intruders out of our network," said one telecommunications executive. "The fact that it is a subscription service and always updated, means that we do not have to worry about threats we don't know about – WatchGuard does it for us and keeps us safe."
"We needed a solution to enable multi-factor authentication within our environment for identity management," said the chief technology officer of a IT services company. "WatchGuard was able to provide the complete solution from RDS servers to endpoints."
"AuthPoint is easy to use, configure, manage, and integrate with any environment," said a cybersecurity engineer at a leading computer security service provider.
Organizations that received TrustRadius 2020 Tech Cares Awards are B2B technology companies that have demonstrated extraordinary caring throughout the COVID-19 pandemic. The TrustRadius research team reviewed nominations from a wide range of parties, including employees and partners, and selected Tech Cares Awards winners based on the merits of the nominations as well as TrustRadius reviewer feedback that clearly illustrated a strong and caring response during the pandemic.
For a detailed criteria breakdown on the methodology and scoring TrustRadius uses to determine Top Rated winners, click here. Those looking to share their own product feedback can leave a review for WatchGuard here.
Learn more about the 2020 Tech Cares Awards here, and to find out how WatchGuard is supporting its employees, partners and customers through the COVID-19 pandemic, click here. For more information on WatchGuard's recent industry awards and recognition, click here.
About WatchGuard Technologies, Inc.
WatchGuard® Technologies, Inc. is a global leader in network security, endpoint security, secure Wi-Fi, multi-factor authentication, and network intelligence. The company's award-winning products and services are trusted around the world by more than18,000 security resellers and service providers to protect more than 250,000 customers. WatchGuard's mission is to make enterprise-grade security accessible to companies of all types and sizes through simplicity, making WatchGuard an ideal solution for midmarket businesses and distributed enterprises. The company is headquartered in Seattle, Washington, with offices throughout North America, Europe, Asia Pacific, and Latin America. To learn more, visit WatchGuard.com.
For additional information, promotions and updates, follow WatchGuard on Twitter (@WatchGuard), on Facebook, or on the LinkedIn Company page. Also, visit our InfoSec blog, Secplicity, for real-time information about the latest threats and how to cope with them at www.secplicity.org. Subscribe to The 443 – Security Simplified podcast at Secplicity.org, or wherever you find your favorite podcasts.
Chris Warfield
Anthony Cogswell
Voxus PR


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A Ride Through Closed And Open Sections Of The SF Bike Trail
by: Dan Santella
Posted: Apr 2, 2019 / 10:13 PM CDT / Updated: Apr 3, 2019 / 12:23 AM CDT
The Sioux Falls bike trail is open from the Sertoma Park area north up and around the airport and then back south again to Falls Park. From there it's closed as it heads south, looping down along the Big Sioux River until it's open again at the Sertoma Park area.
You meet a number of different people enjoying these open trails in different ways.
"It's a little windy, the west wind a little bit, but other than that it's pretty good," Frank Feltis of Sioux Falls said.
"It's a beautiful day out, it's starting to warm up, so it's great to be out," Benson Langat of Sioux Falls said.
We ran into Benson Langat—well, he was the one running—north of 49th Street by Sertoma Park Tuesday afternoon.
"So I ran on this other side, the west side of the bike path from Sertoma all the way to Dunham Park where the bridge collapsed, and I turned around, I cross Marion Road, and went all the way to the country club golf course, and I'm just on my way back," Langat said.
He uses the trails a lot.
"Pretty much every day, yeah," Langat said.
Feltis likes what he sees of the trails.
"I've only been on the side that's open, on the west side, and it's good here," Feltis said.
"Overall it's looking good," Langat said.
North of Falls Park, the bike trail is open. But south of Falls Park, it's closed. At Tomar Park, ice floes cover a section of the closed trail. But when the trail curves back to the Sertoma Park area, it opens up again.
"We're just here to enjoy some of the weather, we saw the sun out, and so we didn't realize how windy it was, but that's not really a deterrent," Christian Headrick of Sioux Falls said.
BISMARCK, N.D. (AP) - Iowa regulators have gone beyond their counterparts in North Dakota by requiring owners of the Dakota Access Pipeline to provide expert analysis to back up the company's claim that doubling the line's capacity does not increase the potential of a spill.
Energy Transfer wants to double the capacity of the pipeline to as much as 1.1 million barrels daily to meet growing demand for oil shipments from North Dakota, and is seeking permission for additional pump stations in the Dakotas, Iowa and Illinois to do it.
Flooding Resources
City of Sioux Falls Flood Resources
Sioux Falls Flood Web Cams
Big Sioux River Flood Information System
SD Department Of Public Safety
SDResponse.gov
FEMA Flood Map


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Q: Feeding example to tf predictor.from_saved_model() for estimator trained with tf hub module I try to export the model for text classification with tf hub modules, and then infer a prediction from it for a single string example using predictor.from_saved_model(). I saw some examples of similar ideas, but still couldn't make it work for the case when using tf hub modules to build features. Here is what I do:
        train_input_fn = tf.estimator.inputs.pandas_input_fn(
        train_df, train_df['label_ids'], num_epochs= None, shuffle=True)

    # Prediction on the whole training set.
    predict_train_input_fn = tf.estimator.inputs.pandas_input_fn(
        train_df, train_df['label_ids'], shuffle=False)

    embedded_text_feature_column = hub.text_embedding_column(
        key='sentence',
        module_spec='https://tfhub.dev/google/nnlm-de-dim128/1')

    #Estimator
    estimator = tf.estimator.DNNClassifier(
        hidden_units=[500, 100],
        feature_columns=[embedded_text_feature_column],
        n_classes=num_of_class,
        optimizer=tf.train.AdagradOptimizer(learning_rate=0.003) )

    # Training
    estimator.train(input_fn=train_input_fn, steps=1000)

    #prediction on training set
    train_eval_result = estimator.evaluate(input_fn=predict_train_input_fn)

    print('Training set accuracy: {accuracy}'.format(**train_eval_result))

    feature_spec = tf.feature_column.make_parse_example_spec([embedded_text_feature_column])
    serving_input_receiver_fn = tf.estimator.export.build_parsing_serving_input_receiver_fn(feature_spec)

    export_dir_base = self.cfg['model_path']
    servable_model_path = estimator.export_savedmodel(export_dir_base, serving_input_receiver_fn)

    # Example message for inference
    message = "Was ist denn los"
    saved_model_predictor = predictor.from_saved_model(export_dir=servable_model_path)
    content_tf_list = tf.train.BytesList(value=[str.encode(message)])
    example = tf.train.Example(
            features=tf.train.Features(
                feature={
                    'sentence': tf.train.Feature(
                        bytes_list=content_tf_list
                    )
                }
            )
        )

    with tf.python_io.TFRecordWriter('the_message.tfrecords') as writer:
        writer.write(example.SerializeToString())

    reader = tf.TFRecordReader()
    data_path = 'the_message.tfrecords'
    filename_queue = tf.train.string_input_producer([data_path], num_epochs=1)
    _, serialized_example = reader.read(filename_queue)
    output_dict = saved_model_predictor({'inputs': [serialized_example]})

And the output:
Traceback (most recent call last):
  File "/Users/dimitrs/component-pythia/src/pythia.py", line 321, in _train
    model = algo.generate_model(samples, generation_id)
  File "/Users/dimitrs/component-pythia/src/algorithm_layer/algorithm.py", line 56, in generate_model
    model = self._process_training(samples, generation)
  File "/Users/dimitrs/component-pythia/src/algorithm_layer/tf_hub_classifier.py", line 91, in _process_training
    output_dict = saved_model_predictor({'inputs': [serialized_example]})
  File "/Users/dimitrs/anaconda3/envs/pythia/lib/python3.6/site-packages/tensorflow/contrib/predictor/predictor.py", line 77, in __call__
    return self._session.run(fetches=self.fetch_tensors, feed_dict=feed_dict)
  File "/Users/dimitrs/anaconda3/envs/pythia/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 900, in run
    run_metadata_ptr)
  File "/Users/dimitrs/anaconda3/envs/pythia/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1135, in _run
    feed_dict_tensor, options, run_metadata)
  File "/Users/dimitrs/anaconda3/envs/pythia/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1316, in _do_run
    run_metadata)
  File "/Users/dimitrs/anaconda3/envs/pythia/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1335, in _do_call
    raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InternalError: Unable to get element as bytes.

Isn't serialized_example the right input that is suggested by serving_input_receiver_fn ?

A: So, all I need was serialized_example = example.SerializeToString()
Writing the example on a file requires to start a session before reading it back. Simply serialising is enough:
    # Example message for inference
    message = "Was ist denn los"
    saved_model_predictor = predictor.from_saved_model(export_dir=servable_model_path)
    content_tf_list = tf.train.BytesList(value=[message.encode('utf-8')])
    sentence = tf.train.Feature(bytes_list=content_tf_list)
    sentence_dict = {'sentence': sentence}
    features = tf.train.Features(feature=sentence_dict)

    example = tf.train.Example(features=features)

    serialized_example = example.SerializeToString()
    output_dict = saved_model_predictor({'inputs': [serialized_example]})

redpajama_set_name: RedPajamaStackExchange


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How I wish I lived in Halifax just to be able to attend such sessions! Thanks for posting on your website!
I've just found your blog and it speaks to me so very much! We eat similarly and use similar health care as I assume you do being chiros. There are some awesome recipes here, I'm making a version of your choc chip coconut muffins right now! Can't wait to read more!
You're welcome, leading healthy lifestyles has become such a fun way of life for us. Although difficult at times it has certainly proven to be beneficial in the long run with more healthy, vibrant kids! Thanks for your comments!


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Domains @aip.de
{}^{6}Li detection in metal-poor stars: can 3D model atmospheres solve the second lithium problem?
6Li detection in metal-poor stars: can 3D model atmospheres solve the second lithium problem?
M. Steffen Leibniz-Institut für Astrophysik Potsdam (AIP), An der Sternwarte 16, 14482 Potsdam, Germany, msteffen@aip.de GEPI Observatoire de Paris, CNRS, Universit´e Paris Diderot, F-92195 Meudon Cedex, France R. Cayrel GEPI Observatoire de Paris, CNRS, Universit´e Paris Diderot, F-92195 Meudon Cedex, France E. Caffau Zentrum für Astronomie der Universität Heidelberg, Landessternwarte, Königstuhl 12, 69117 Heidelberg, Germany GEPI Observatoire de Paris, CNRS, Universit´e Paris Diderot, F-92195 Meudon Cedex, France P. Bonifacio GEPI Observatoire de Paris, CNRS, Universit´e Paris Diderot, F-92195 Meudon Cedex, France H.-G. Ludwig Zentrum für Astronomie der Universität Heidelberg, Landessternwarte, Königstuhl 12, 69117 Heidelberg, Germany GEPI Observatoire de Paris, CNRS, Universit´e Paris Diderot, F-92195 Meudon Cedex, France M. Spite GEPI Observatoire de Paris, CNRS, Universit´e Paris Diderot, F-92195 Meudon Cedex, France
The presence of 6Li in the atmospheres of metal-poor halo stars is usually inferred from the detection of a subtle extra depression in the red wing of the 7Li doublet line at 670.8 nm. However, as pointed out recently by Cayrel et al. (2007) , the intrinsic line asymmetry caused by convective flows in the photospheres of cool stars is almost indistinguishable from the asymmetry produced by a weak 6Li blend on a (presumed) symmetric 7Li profile. Previous determinations of the 6Li/ 7Li isotopic ratio based on 1D model atmospheres, ignoring the convection-induced line asymmetry, must therefore be considered as upper limits. By comparing synthetic 1D LTE and 3D non-LTE line profiles of the \ionLii 670.8 nm feature, we quantify the differential effect of the convective line asymmetry on the derived 6Li abundance as a function of effective temperature, gravity, and metallicity. As expected, we find that the asymmetry effect systematically reduces the resulting 6Li/7Li ratios. Depending on the stellar parameters, the 3D-1D offset in 6Li/7Li ranges between −0.005 and −0.020. When this purely theoretical correction is taken into account for the Asplund et al. (2006) sample of stars, the number of significant 6Li detections decreases from 9 to 5 (2σ criterion), or from 5 to 2 (3σ criterion).
We also present preliminary results of a re-analysis of high-resolution, high S/N spectra of individual metal-poor turn-off stars, to see whether the second Lithium problem actually disappears when accounting properly for convection and non-LTE line formation in 3D stellar atmospheres. Out of 8 stars, HD 84937 seems to be the only significant (2σ) detection of 6Li. In view of our results, the existence of a 6Li plateau appears questionable.
stars: abundances -– stars: atmospheres – convection – line: profiles – stars: population II –- stars: individual: (HD 74000, HD 84937, HD 140283, HD 160617, G271-162, G020-024, G64-12, G275-4)
\idline
A systematic analysis of high-dispersion, high signal-to-noise (S/N) UVES and Keck spectra of about 30 bright metal-poor stars by Asplund et al. (2006) and Asplund & Melendez (2008) (henceforth A06 and A08, respectively) resulted in the detection of 6Li (at the 2σ level) in about one third of these objects. The average 6Li/7Li isotopic ratio in the stars in which 6Li has been detected is about 4% and is very similar in each of these stars, defining a 6Li plateau at approximately logn(6Li)/n(H)+12=0.85. A convincing theoretical explanation of this new 6Li plateau turned out to be problematic: the high abundance of 6Li at the lowest metallicities cannot be explained by current models of galactic cosmic-ray production, even if the depletion of 6Li during the pre-main-sequence phase is ignored (see reviews by e.g. Cayrel et al., 2008; Christlieb, 2008; Prantzos, 2010, 2012, and references therein) .
A possible solution of this so-called second Lithium problem was proposed by Cayrel et al. (2007) : noting that the spectroscopic signature of the presence of 6Li in the atmospheres of metal-poor halo stars is just a subtle extra depression in the red wing of the 7Li doublet, which is difficult to measure even in spectra of the highest quality, they point out that the intrinsic line asymmetry caused by convection in the photospheres of cool stars closely mimics the presence of 6Li at the level of a few percent if interpreted in terms of 1D, intrinsically symmetric blend components. As a consequence, the 6Li abundance derived so far by using 1D model atmospheres, ignoring the convective line asymmetry, are expected to be systematically too high.
We quantify the theoretical effect of the convection-induced line asymmetry on the resulting 6Li abundance by fitting a given synthetic profile both with a grid of 1D LTE and a grid of 3D non-LTE line profiles. The 1D-3D difference of the required 6Li/7Li isotopic ratio measures the expected systematic error in 6Li/7Li inherent to the standard 1D analysis. The synthetic spectra used for this differential comparison are based on a set of 3D hydrodynamical model atmospheres computed with the CO5BOLD code (Freytag et al., 2002, 2012) and a corresponding set of fully compatible 1D mixing-length models, respectively, as outlined in Sect. 2. The results of this investigation may be represented by an analytical approximation, giving the 3D correction of 6Li/7Li as a function of effective temperature, gravity, and metallicity, for a parameter range that covers the stars of the A06 sample (Sect. 3). We also discuss the potential advantages and disadvantages of using so called 'calibration lines' for fixing the residual line broadening, and show that the usage of such 'calibration lines' is potentially dangerous, because the inferred broadening parameter shows considerable line-to-line variations. Depending on the choice of these lines, the resulting 6Li abundance may be systematically biased (Sect. 4).
A careful reanalysis of individual objects is under way to see whether the second Lithium problem can be resolved when accounting properly for convection and non-LTE line formation in 3D stellar atmospheres. In Sect. 5 we present a preliminary analysis of eight individual metal-poor turn-off stars for which sufficiently high-resolution, high S/N spectra are at our disposal, fitting the observed \ionLii 670.8 nm feature with both 1D LTE and 3D non-LTE synthetic line profiles. As expected, the 3D analysis gives a systematically lower 6Li/7Li ratio by roughly 0.01 with respect to the 1D result. In most cases, we find that the detection of 6Li is not significant at the 2σ level.
2 3D model atmospheres and spectrum synthesis
The hydrodynamical atmospheres used in the present study are part of the CIFIST 3D model atmosphere grid (Ludwig et al., 2009) . They have been obtained from realistic numerical simulations with the CO5BOLD code111http://www.astro.uu.se/~bf/co5bold_main.html (Freytag et al., 2002, 2012) which solves the time-dependent equations of compressible hydrodynamics in a constant gravity field together with the equations of non-local, frequency-dependent radiative transfer in a Cartesian box representative of a volume located at the stellar surface. The computational domain is periodic in x and y direction, has open top and bottom boundaries, and is resolved by typically 140×140×150 grid cells. The vertical optical depth of the box varies typically from logτRoss≈−7.0 (top) to logτRoss≈+7.5 (bottom). Further information about the grid of models used in the present study is compiled in Table 1. As indicated in columns (2) – (4), the grid covers the range 5900 K ≤Teff ≤6500 K, 3.5 ≤logg ≤4.5, −3.0 ≤[Fe/H] ≤0.0. Radiative transfer is solved in 5 or 6 opacity bins (col. 5). Each of the models is represented by typically 20 snapshots chosen from the full time sequence of the corresponding simulation (col. 6).
These representative snapshots are processed by the non-LTE code NLTE3D that solves the statistical equilibrium equations for a 17 level lithium atom with 34 line transitions, fully taking into account the 3D thermal structure of the respective model atmosphere (but neglecting the differential Doppler shifts implied by the hydrodynamical velocity field). The photo-ionizing radiation field is computed at 704 frequency points between λ925 and 32 407 Å, using the opacity distribution functions of Castelli & Kurucz (2004) to allow for metallicity-dependent line-blanketing, including the H I–H+ and H I–H I quasi-molecular absorption near λ1400 and 1600 Å, respectively. Collisional ionization by neutral hydrogen via the charge transfer reaction H(1s) + Li(nℓ) ↔ Li+(1s2) + H− is treated according to Barklem, et al. (2003) . More details are given in Sbordone et al. (2010) . Finally, 3D non-LTE synthetic line profiles of the \ionLii λ670.8 nm doublet are computed with the line formation code Linfor3D222http://www.aip.de/~mst/linfor3D_main.html, using the departure coefficients bi = ni(NLTE)/ni(LTE) provided by NLTE3D for each level i of the lithium model atom as a function of geometrical position within the 3D model atmospheres. At this stage, the 3D hydrodynamical velocity field provides the differential line shifts along each line of sight. As demonstrated in Steffen et al. (2010b) , non-LTE effects are very important for the 3D models of the metal-poor dwarfs considered here: they strongly reduce the height range of line formation such that the 3D non-LTE equivalent width is smaller by roughly a factor 2 compared to 3D LTE. Ironically, the line strength predicted by standard 1D mixing-length models in LTE are close to the results obtained from elaborate 3D non-LTE calculations. Nevertheless, when it comes to the determination of the 6Li/7Li isotopic ratio, it is important to account for the convective line asymmetry, which requires the full 3D non-LTE line formation calculations.
Figure 1: Example of a 3D non-LTE synthetic profile of the \ionLii 670.8 nm feature, for stellar parameters Teff=6300 K, logg=4.0, [Fe/H]=-2. Increasing the 6Li content from 0 to 5% leads to an extra depression of the red wing by about 0.5% (indicated by the gray area), leaving the blue wing unaffected. In order to detect such a small effect, observed spectra are required to have an S/N in excess of ≈400.
Model T 1)eff logg [Fe/H] Nbin Nsnap W2) Δq(Li)=q∗(1DLTE)(Li)
[K] [mÅ] [×100]
d3t59g35mm30n01 5873 3.5 -3.0 6 20 41.7 2.13 1.99
d3t59g35mm00n01 5884 3.5 0.0 5 20 33.0 3.37 3.06
Notes: 1) averaged over selected snapshots; 2) averaged over selected 3D non-LTE spectra
Table 1: List of 3D models used in the present study. Columns (2)-(6) give effective temperature, surface gravity, metallicity, number of opacity bins used in the radiation hydrodynamics simulation, and number of snapshots selected for spectrum synthesis. The equivalent width of the synthetic 3D non-LTE 7Li doublet at λ670.8 nm, assuming A(Li)=2.2 and no 6Li, is given in col. (7). Columns (8) and (9) show Δq(A)(Li) and Δq(B)(Li), the 3D non-LTE correction of the 6Li/7Li isotopic ratio inferred from fitting the unprocessed (no rotational and instrumental broadening) and the broadened (vsini=1.5 km/s, FWHM=2.5 km/s) 3D non-LTE line profile, respectively, with a grid of 1D LTE profiles.
3 Theoretical 3D non-LTE correction of 1D LTE 6Li/7Li determinations
As outlined above, the 6Li abundance is systematically overestimated if the intrinsic asymmetry of the 7Li line components is ignored. To quantify this bias theoretically, we rely on synthetic spectra. The idea is as follows: we represent the observation by the synthetic 3D non-LTE line profile of the 7Li line blend, computed with zero 6Li content. The non-thermal line broadening is naturally provided by the 3D hydrodynamical velocity field, which replaces the classical concept of micro- and macroturbulence, and also gives rise to a convective blue-shift and an intrinsic line asymmetry. Optionally, the line blend is subsequently broadened by rotation (vsini) and a Gaussian instrumental profile (FWHM denoting its full width at half maximum). Now this realistic proxy of the 7Li line blend is fitted by a grid of 'classical' 1D LTE synthetic line profiles. The 1D LTE line profiles are computed from so-called LHD models, 1D mixing-length model atmospheres that have the same stellar parameters and employ the same microphysics and radiative transfer scheme as the corresponding 3D model, assuming a mixing-length parameter of αMLT=0.5, and a depth-independent microturbulence of ξmic=1.5 km/s.
Figure 2: Contours of Δq(B)(Li) in the Teff – logg plane at constant metallicity [Fe/H] = −3, −2, −1, and 0, respectively (from top left to bottom right) according to the approximation given by Eq. (1) and Table 2. White symbols mark the positions of the stars from the A06 sample with Fe/H] <−2.5 (top left), −2.5< [Fe/H] <−1.5 (top right), and −1.5< [Fe/H] <−0.5 (bottom left).
Five parameters are varied independently until the best fit is found (χ2 minimization): in addition to the total 6Li+7Li abundance, A(Li), and the isotopic ratio, q(Li)=6Li/7Li, which control line strength and line asymmetry, respectively, we also allow for a residual line broadening described by a Gaussian kernel with half-width VBR, a global line shift, Δv, and a global scaling of the continuum level, Sc. For technical reasons, the rotational line broadening is not treated as a fitting parameter; it is fixed to the value used in the 3D spectrum synthesis.
Note that, although the five fitting parameters are correlated, each one has a distinctly different effect on the line profile. The broadening VBR is fixed by the slope of the blue wing, which is not affected by the 6Li content, (see Fig. 1), while q(Li) is fixed by the shape of the red wing. This allows to obtain unambiguously a unique solution although a given half width of the line blend can be obtained by different combinations of q(Li) and VBR, but with higher χ2 values than the best solution.
Finally, the 3D non-LTE correction of the 6Li/7Li isotopic ratio is defined as the difference between the values of q(Li) that provide the best fit (∗) to the given profile using 1D LTE and 3D non-LTE profiles, respectively: Δq(Li) = q∗(1DLTE)(Li) - q∗(3DNLTE)(Li)333q∗(3DNLTE)(Li) is zero by construction here. This differential correction is meant to be subtracted from the 6Li/7Li isotopic ratio derived from the 1D LTE analysis to correct for the bias introduced by neglecting the intrinsic line asymmetry: q(3DNLTE)(Li) = q(1DLTE)(Li) - Δq(Li). Note that this correction procedure properly accounts for radiative transfer in the lines, including saturation effects.
We have determined Δq(Li) according to the method outlined above for each of the 3D model atmospheres in our grid, considering two cases: Δq(A)(Li) and Δq(B)(Li) are the result of fitting the unprocessed (vsini=0.0 km/s, FWHM=0.0 km/s) and the broadened (vsini=1.5 km/s, FWHM=2.5 km/s) synthetic line profile, respectively. Case (B) is the more realistic one, while case (A) has been included only to show how the amount of instrumental broadening is affecting the amplitude of Δq(Li). Note also that the 3D corrections given in previous studies by (Steffen et al., 2010a, b) correspond to case (A).
The results of this computationally very expensive procedure are given in cols. (8) and (9) of Table 1. They may be aproximated fairly well by the following polyonmial expression:
Δq(B)(Li){Teff,logg,[Fe/H]}=a00+a01Z +
(a10+a11Z)Y+(a20+a21Z)Y2 +
(a30+a31Z)X+(a40+a41Z)XY +
(a50+a51Z+a52Z2)X2 (1)
where X≡(Teff−6300)/1000, Y≡logg−4.0, and Z≡[Fe/H]+2. The coefficients aij are listed in Table 2. Contour plots of this polynomial approximation are shown in Fig. 2.
a0j×100
-2.6658 -0.4547 0.0000
2.3370 -0.3211 0.0000
Table 2: Coefficients aij representing Δq(B)(Li) as a function of Teff, logg, and [Fe/H] according to Eq. (1).
At given metallicity, the corrections are largest for low gravity and high effective temperature. They increase towards higher metallicity. Note that the corrections are strictly valid only for a Li abundance of A(Li)=2.2. The results for [Fe/H]=0.0 are most uncertain, since they are based on fewer data points.
Figure 3: Histogram of the 6Li/7Li determinations by A06 before (top) and after (bottom) application of the 3D correction Δq(B)(Li).
The analysis of A06 utilizes 1D LTE profiles computed from MARCS model atmospheres. Hence, the correction Δq(B)(Li) should be applied to their 6Li/7Li isotopic ratios. The resulting downward corrections are typically in the range 1%<Δq(B)(Li) <2% for the stars of their sample (cf. Fig. 2). After subtracting the individual Δq(B)(Li) for each of these stars, interpolated via Eq. (1) to their Teff, logg, and [Fe/H], the mean 6Li/7Li isotopic ratio of the sample is reduced from 0.0212 to 0.0088. The center of the distribution of 6Li/7Li is shifted essentially to zero (see Fig. 3).
Based on the original 6Li/7Li isotopic ratios and their 1σ error bars as determined by A06, the number of stars with a 6Li detection above the 2σ and 3σ level is 9 and 5, respectively, out of 24. After correction, the number of 2σ and 3σ detections is reduced to 5 and 2, respectively. The 3σ detections after correction are HD 106038 and G020−024, the remaining 2σ detections are HD 102200, HD 160617, and CD−30 18140.
We note that HD 106038 survives as a 3σ detection only because of its amazingly small error bar of ±0.006. Assuming a more typical error of ±0.013, this object would not even qualify as a 2σ detection. HD 102200 seems to be a clear 2σ detection (q(Li) =0.036±0.013), while CD−30 18140 is a marginal case (q(Li) =0.029±0.013). The remaining objects, G020-024 and HD 160617, are discussed in more detail in Sect. 5, where their spectra are reanalyzed with 3D non-LTE line profiles; none of them provides convincing evidence for the presence of 6Li.
4 Fixing the residual line broadening by 'calibration lines'
So far we have derived all information from fitting the Li 670.8 nm feature, for which we have a 3D non-LTE line formation model. In principle, the accuracy of the fitting procedure can be improved by fixing the value of the residual line broadening, VBR, from additional unblended 'calibration lines'. This approach has been adopted by A06 (see also Lind et al., 2012, henceforth L12) . This fact complicates the comparison with our results, and the correction procedure described in Sect. 3 explains only part of the differences. We show in the following that 'calibration lines' may introduce additional uncertainties, unless their 3D non-LTE line formation is fully understood.
In the framework of 1D models, VBR, represents the combined effect of macroturbulence and instrumental broadening (for given microturbulence and rotational velocity) and is expected to be independent of the spectral line. In general, an average value of VBR from several calibration lines is then used for the analysis of the Li line, thus reducing the number of free fitting parameters, and hence the formal errors of the fitting results.
Wλ
δq(1D)
[nm]
[eV]
[pm]
[kms]
[×100]
\ionFei
538.337 4.312 2.74 4.88 0.35
\ionCai
\ionLii
Table 3: List of calibration lines. Columns (2) to (4) show their wavelength, excitation potential, and equivalent width. The residual line broadening derived from 1D LTE fitting and the resulting error of 6Li/7Li is given in cols. (5) and (6). The last row refers to the Li line; cols. (5) and (6) show the results of 3D non-LTE fitting without calibration lines.
Teff [K]
[Fe/H]
R=λ/Δλ
S/N ∗)
HD 74000 6203 4.03 -2.05 120000 600 ESO3.6 / HARPS C07 1)
G271−162 6330 4.00 -2.25 110000 600 VLT / UVES N00 2)
HD 84937 6310 4.10 -2.40 100000 650 CFHT / GECKO C99 3)
G020−024 6247 3.98 -1.89 120000 420 VLT / UVES A06 4)
HD 140283 5753 3.70 -2.40 95000 1000 SUBARU / HDS A04 5)
HD 160617 5990 3.79 -1.76 100000 400 ESO3.6 / HARPS M12 6)
G64−12 6371 4.26 -3.24 95000 620 Keck / HIRES A08 7)
G275−4 6338 4.32 -3.21 93000 700 VLT / UVES N09 8)
Notes: 1) Cayrel et al. (2007) , 2) Nissen et al. (2000) , 3) Cayrel et al. (1999) , 4) Asplund et al. (2006) , 5) Aoki et al. (2004) ,
6) L. Monaco and G. Lo Curto (2012, priv. comm.), 7) Asplund & Melendez (2008) , 8) P.E. Nissen (2009, priv. comm.),
∗) S/N estimated from the (re-reduced) spectra actually used in this work, may differ from literature values.
Table 4: List of observed Li I λ670.8 nm spectra of eight metal-poor turn-off stars, analyzed in this work for the presence of 6Li.
We have simulated this procedure using synthetic lines only. Our selection of 10 calibration lines is compiled in Tab. 3. The lines have been selected to have similar wavelength, equivalent width, and excitation energy (relative to the ionization continuum) as \ionLii 670.8 nm. Synthetic line profiles generated from 3D model d3t63g40mm20n01 (assuming LTE) represent the observed line profiles and are fitted with 1D LTE profiles from the corresponding LHD model to derive VBR. The results are given in col. (5) of Tab. 3. These numbers have to be compared with the value of VBR obtained by fitting the 3D non-LTE Li profile of the same 3D model with 1D LTE profiles from LHD models, VBR=4.97 km/s (last row of Tab. 3). When fixing VBR to the values implied by the individual calibration lines, the resulting 6Li/7Li isotopic ratio changes by δq(1D)(Li) with respect to the value obtained when treating VBR as a free fitting parameter.
We find that, even under these idealized conditions, δq(1D)(Li) is not constant but varies from line to line (see col. (6) of Tab. 3). This is an indication that 1D micro / macro model is not perfectly adequate to describe the 3D hydrodynamical velocity field. Moreover, since δq(1D)(Li) is positive for all of our calibration lines, the use of calibration lines leads to an additional overestimation of the Li isotopic ratio by up to 2%. The total error of the 1D analysis is obtained by adding the corrections from Tables 1 and 3, Δq(Li) = Δq(B)(Li) + δq(1D)(Li).
In a further experiment, we have fitted both the calibration lines and the Li feature with 3D LTE line profiles. By construction, the derived VBR is then equal to FWHM of the assumed instrumental profile, and is identical for all calibration lines. The Li isotopic ratio obtained from fitting the 3D non-LTE Li feature with 3D LTE profiles and fixed VBR=FWHM is δq(3D)(Li)=0.054, compared to 0.003 if VBR is a free fitting parameter. The basic reason for this alarming result is that the half width of the Li line is significantly smaller in LTE than in non-LTE, which must be compensated by a higher 6Li content. This effect may explain why A06 find higher 6Li/7Li isotopic ratios in their 3D LTE analysis (with respect to 1D LTE, their Table 5).
We note that the above results are consistent with previous findings by Steffen et al. (2010b) , who analyzed the spectrum of HD 74000 with a subset of 6 clean \ionFei calibration lines, and obtained significantly higher 6Li/7Li isotopic ratios compared to the analysis without calibration lines. The same behavior is seen by L12, if they fit both the calibration lines and Li with 3D LTE profiles. Using instead non-LTE profiles for fitting both \ionCai and \ionLii lines leads to consistent 6Li/7Li isotopic ratios for HD 84937.
Figure 4: The best fitting 3D non-LTE Li 670.8 nm line profile (green) superimposed on the observed spectrum (solid black) of HD 84937 (top) and G020–024 (bottom). The difference between computed and observed spectrum (×10; dashed line) indicates the quality of the fit, which is much better for HD 84937 than for G020–024, the latter suffering from a 'perturbation' in its lower red wing.
5 Analysis of observed spectra: 3D non-LTE versus 1D LTE line fitting
With the results given in Sect. 3, the recommended method of analysis of the \ionLii 670.8 nm doublet in an observed spectrum is as follows. The first step is to produce a grid of 1D LTE synthetic line profiles as a function of A(Li), q(Li), using any standard 1D mixing-length model with the correct stellar parameters (Teff, logg, [Fe/H]). This grid is then used to find the best fit to the observed line profile, as described above (no calibration lines). Finally, the resulting q(Li) is corrected for 3D effects by subtracting the differential correction Δq(B)(Li) defined in Sect. 3, interpolated to the actual stellar parameters according to Eq. (1) and Table 2.
In the following, however, we disregard this differential approach, and present instead preliminary results of fitting the observed \ionLii λ670.8 nm spectra of eight halo turn-off stars (see Table 4) directly with our grids of 3D non-LTE and 1D LTE synthetic line profiles, computed from 3D CO5BOLD models and 1D LHD models, respectively. As described above, five free fitting parameters (A(Li), q(Li), VBR, Δv, Sc) are varied independently to find the best fit. Formally, we fix the rotational velocity to vsini=1.0 km/s, noting that the derived 6Li/7Li isotopic ratio is insensitive to this assumption (see Steffen et al., 2010b) .
Star Model synthetic A(Li) 1) V 2)BR q(Li) [×100]
name spectrum [km/s] this work A06,A08,L12
HD 74000 d3t63g40mm20n01 3D NLTE 2.25 2.9±0.4 −0.8±1.2 —
t6280g40mm20a05ob6 1D LTE 2.23 5.7±0.2 0.6±1.1 —
G271−162 d3t63g40mm20n01 3D NLTE 2.29 3.5±0.3 0.6±1.1 —
t6280g40mm20a05ob6 1D LTE 2.27 6.1±0.2 2.1±1.2 1.9±1.2 3)
HD 84937 d3t63g40mm20n01 3D NLTE 2.20 3.1±0.7 5.1±2.3 0.7±0.5 5)
t6280g40mm20a05ob6 1D LTE 2.19 5.0±0.4 11.7±2.5 7.0±1.7 3)
HD 140283 d3t57g37mm20n01 3D NLTE 2.20 3.7±0.1 −0.6±0.4 −0.4±0.2 5)
d3t57g37mm30n01 3D NLTE 2.22 3.3±0.1 −1.0±0.4 —
t5775g37mm20ml3a05ob6 1D LTE 2.18 5.5±0.1 0.5±0.4 0.8±0.6 3)
t5789g37mm30ml3a05ob6 1D LTE 2.19 5.5±0.1 0.4±0.4 —
HD 160617 d3t59g40mm20n02 3D NLTE 2.14 4.5±0.2 −0.5±1.2 —
t5860g40mm20ml3a05 1D LTE 2.11 5.8±0.2 0.1±1.2 3.6±1.0 3)
t5860g35mm20a05 1D LTE 2.13 5.8±0.2 0.3±1.2 —
G64−12 d3t63g40mm30n01 3D NLTE 2.19 5.0±0.5 0.8±2.7 2.1±1.2 5)
d3t63g45mm30n01 3D NLTE 2.19 5.5±0.5 1.2±2.8 —
G275−4 d3t63g40mm30n01 3D NLTE 2.11 3.6±0.6 3.5±2.4 —
t6270g40mm30ml3a05 1D LTE 2.08 6.3±0.4 4.7±2.5 —
Notes: 1) log[n(6Li)+n(7Li)]−logn(H)+12; 2) Gaussian kernel, 3) Asplund et al. (2006) , 4) Asplund & Melendez (2008) ,
5) Lind et al. (2012)
Table 5: Fitting the observed spectra of Table 4 with 3D non-LTE and 1D LTE line profiles.
The results are presented in Table 5. The errors quoted for VBR and q(Li) are the formal 1σ confidence intervals due to the finite S/N of the fitted spectra. They are computed in the usual way (see e.g. Nissen et al., 2000; Asplund et al., 2006) by finding the distance Δp of the parameter of interest p from is optimum value p0 such that χ2(p+Δp)−χ2(p0)≡Δχ2=1, fixing p and minimizing Δχ2 over the remaining free fittting parameters. Here χ2 is defined as
χ2=∑i(Oi−Si)2σ2, (2)
where Oi and Si are the observed and synthetic flux at wavelength point i, and σ=(S/N)−1, with S/N taken from Tab. 4. Note that this is a lower limit to the real error, which may have several other significant contributions (see discussion in García Pérez et al., 2009) .
As expected, the 3D analysis yields systematically lower 6Li/7Li isotopic ratios by up to 1.8%. For comparison, we list in the last column the 1D LTE results of A06, A08, and the 3D non-LTE results of L12 from their case (a) for the stars in common with our sample. In some cases, the agreement is very good (G271–162, HD140283), even though the analysis is based on different observational data. In other cases, we obtain significantly lower 6Li/7Li ratios (HD 160617, G64–12). The most striking discrepancy, this time in the opposite direction, is seen for HD 84937.
G020–024 is a special case. We have retrieved the spectra obtained by A06 from the ESO archive and re-reduced them with the current UVES pipeline. We note that all six sub-exposures show an unexplained 'perturbation' in the lower red wing of the Li 670.8 nm line that cannot be fitted properly, neither with 1D LTE nor with 3D non-LTE model spectra. The best 3D non-LTE fit suggests a very high 6Li content near 10% (see Fig. 4), even exceeding the 1D LTE result by A06 of 7%. In view of the poor quality of the fit, we consider this result as doubtful. It is worth mentioning that G020–024 is listed as a suspected binary in Fouts (1987) ; moreover, A06 report that this star shows an unusually large discrepancy between the photometric and the Hα-based Teff. Possibly, G020–024 is a spectroscopic binary whose components are of similar spectral type.
Disregarding G020–024, we are left with one formal 2σ detection (HD 84937), one 1σ detection (G275–4), and five non-detections (HD 74000, G271–162, HD 140283, HD160617, G64–12) out of the eight stars, when considering the 3D non-LTE results only. In 1D LTE, G271–162 would turn into a formal 1σ detection (see Tab. 5).
The 6Li/7Li isotopic ratio derived by fitting the Li I doublet with 3D non-LTE synthetic line profiles is shown to be 1% to 2% lower than what is obtained with 1D LTE profiles. This result implies that only 2 out of the 24 stars of the Asplund et al. (2006) sample would formally remain significant (3σ) 6Li detections when subjected to a 3D non-LTE analysis.
In another theoretical case study we have demonstrated that the difference between 3D non-LTE and 1D LTE results increases even more if we would rely on additional 'calibration lines' to fix the residual line broadening, as advocated e.g. by A06, A08, L12. The number of possible 6Li detections is thus further reduced. We conclude that the usage of additional 'calibration lines', even if carefully selected, introduces additional uncertainties rather than reducing the error of the analysis, unless the 3D non-LTE line formation is fully understood for all involved lines.
Finally, we have analyzed available high quality spectra of eight turn-off halo stars, both with 1D LTE and 3D non-LTE modeling. In most cases, the evidence for the presence of 6Li is not significant. Only in the case of HD 84937 it is difficult to deny the signature of 6Li. Surprisingly, L12 no longer find any significant evidence for the presence of 6Li in this object, which since almost two decades has been considered as an undisputed 6Li detection. From the results reported by L12, it is unclear whether the reason for this disturbing discrepancy is related to the new superior observational data or to the improved method of analysis.
We conclude that 3D model atmospheres can indeed help to solve the second lithium problem. In view of the 3D non-LTE results reported in this work (and by L12), it seems that the presence of 6Li in the atmospheres of galactic halo stars is rather the exception than the rule, and hence does not necessarily constitute a cosmological problem.
We thank L. Monaco and G. Lo Curto for re-reducing the HARPS spectrum of HD 160617, P.E. Nissen for providing us with a carefully reduced UVES spectrum of G275-4, and W. Aoki for allowing us to use his HDS spectrum of HD 140283.
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More From On the Shoulders of Giants
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The haunting things Bob Lang once saw as a firefighter have influenced the advice he now gives to young colleagues in his work as a lawyer.
By Elisha Brown
Andrew Kelly / Reuters
Robert Lang, a 68-year-old lawyer at the American Bar Association, has over the course of his career served clients facing immigration crises and domestic violence, among other issues. It's stressful work, and in order to avoid burnout, Lang has set aside time for what he calls his "adventure" periods: He's worked as a hospitality manager in the Virgin Islands and a teacher in Los Angeles.
But Lang says it was his first job, working as a firefighter throughout college and law school, that taught him the most about compassion, grief, and advocating for others. Now, Lang says that mentoring young lawyers and interns is the highlight of his job, and his firefighting days influence the way he goes about that. I spoke with Lang about his work relationships for The Atlantic's series "On the Shoulders of Giants." The following interview has been edited and condensed.
Elisha Brown: What lessons did you learn from your work as a firefighter, and how do those lessons influence how you work with others?
Robert Lang: I left with post-traumatic stress because of kids I couldn't save. It involved hallucinations, panic attacks, insomnia, and nightmares. I was suicidal. The lessons I learned from the fire department, and the key thing I apply to mentoring, is that we're not guaranteed any time on Earth. When I start working with interns, at the beginning I tell them that we have very little time together, and we have to treat every day as if it was precious.
Brown: When did you realize that you enjoyed mentoring and were good at it?
Robert Lang, a lawyer who says mentorship is the highlight of his job (Courtesy of Jillian Skonieczka)
Lang: After I became a lawyer, I worked for Legal Aid in Iowa. I was good at bringing people together to build a strong team. I recognized right away that I enjoyed mentoring people and helping them become the best that they could be. I always consider mentorship a sacred trust. People are trusting you with some of their life in hopes that you can help them develop what they want to do and be.
Brown: And how do you help in that development?
Lang: I try to make sure their mentorship or internship isn't transactional, which is what most internships are. What I want to do is help people to see that they're not just novices with nothing to offer. They've got a unique life experience that they're bringing. They need to capitalize on that and bring to bear the skills they've already developed.
Brown: Do you think your mentorship skills get recognized in your line of work?
Lang: Roger Forman, a former boss of mine, once wrote me a letter of recommendation. The first line said, "No matter what office Bob Lang works in, he will never be the best lawyer in that office, but he will leave the biggest hole when he moves on." That taught me that people in offices have different roles. When I'm working with my interns and law students, I can pick out what role they'll play in the office after I get to know them a little bit. There's the academician, the person who loves to do research, the advocate, the one who's really good at going to court. Another one is the role I was in—which I didn't know until Roger told me—which was the person who's the unifier of the team.
Brown: What specific qualities do you look for in young law students and interns?
Lang: I want them to have a good heart. They demonstrate a good heart to me if they have volunteered to help other people on their own time. When we're dealing with social justice, the clients have had the world dropped on them. I can't teach anybody how to care about those people—I can teach them the law, but I can't teach them compassion.